From f7289bfb8b3cd0e08b6e515051c0b4f62abd210d Mon Sep 17 00:00:00 2001 From: SamueleSoraggi Date: Tue, 27 Jun 2023 09:45:16 +0200 Subject: [PATCH] Deployed 5122c46 with MkDocs version: 1.3.0 --- galaxy.html | 1 + search/search_index.json | 2 +- sitemap.xml | 18 +++++++++--------- sitemap.xml.gz | Bin 203 -> 203 bytes 4 files changed, 11 insertions(+), 10 deletions(-) diff --git a/galaxy.html b/galaxy.html index de250c1..13faf0d 100644 --- a/galaxy.html +++ b/galaxy.html @@ -752,6 +752,7 @@

Prepare the tools and data and log into galaxy.
  • Find the course data by going to this web address and by clicking on Import this history (top left corner of the page).
    • +

    Note: If usegalaxy.org has availability problems, you can use the other server https://usegalaxy.eu/ and get the data at this link. You might need to create an account there as well.

    Data

    You will be working with two types of sequencing data. The first is PacBio Hifi reads, which are long and accurate. You can find them under diff --git a/search/search_index.json b/search/search_index.json index a26822d..3dbcfeb 100644 --- a/search/search_index.json +++ b/search/search_index.json @@ -1 +1 @@ -{"config":{"indexing":"full","lang":["en"],"min_search_length":3,"prebuild_index":false,"separator":"[\\s\\-]+"},"docs":[{"location":"index.html","text":"Introduction to Next Generation Sequencing data \u00b6 A course of the Summer School of Aarhus University \u00b6 Computing and didactical support from the Danish Health Data Science Sandbox The material for this course is organized in four separated jupyter notebooks in both bash , python and R where you will benefit of an interactive coding setup on jupyterlab . Overview After the course, you will have knowledge of bioinformatics methods for analyzing genomes using NGS data, including knowledge of the existing types of genome data, how the different types of data can be displayed and analyzed, the current methods for genome assembly and analysis, their accuracy and how they can be used. The course will enable you to devise and run a project that makes use of NGS data. \ud83d\udcda Prerequisites: This is an introductory course that needs a basic understanding of the biology behind sequencing, and not necessarily programming experience (though this would help!). \ud83d\udcac Syllabus: 1. Describe key challenges in the analysis of NGS data 2. Explain the theoretical foundation for methods that use NGS for assembly and analysis of genomes 3. Discuss the bioinformatic methods for genome analysis and hypothesize what drives the outcome of the methods 4. Review original literature within the subjects and relate the discussed topics to analysis scenarios 5. Apply bioinformatics tools within the selected application areas and reflect on the results, formulating your own conclusion in the proposed tasks \ud83d\udd70 Total Time Estimation: 20 hours \ud83d\udcc1 Supporting Materials: jupyter notebooks for interactive coding lecture slides from the instructor You can find the links to the material in the table at the bottom of this page. \ud83d\udd8d Course authors and instructors: Mikkel H Schierup Stig U Andersen Samuele Soraggi Peter S Porsborg Adri\u00e1n G Repoll\u00e9s \ud83d\udccb License: Tutorial Content is licensed under Creative Commons Attribution 4.0 International License \ud83d\udcdd Citation: If you use any of this material for your research, please cite this course with the DOI below, and acknowledge the Health Data Science Sandbox project of the Novo Nordisk Foundation (grant number NNF20OC0063268). It is of great help to support the project. \ud83d\udce7 Contact: Samuele Soraggi (samuele at birc.au.dk). Course structure and Instructions The course exercises are organized in four exercise modules. The first one is executed with the web interface usegalaxy.org . Click on 1.Galaxy Exercise in the menu for instructions) Afterwards, we will work on a computing environment to use jupyterlab . Use 2.Instructions for instructions. The menu 3.Course exercises contains all the compiled exercises as a reference. Course material 2022 \u00b6 Here you find a table with the instructor's slides from 2022. Topic Slide Notebook Sequencing technologies link -- Mapping to reference link Notebook Data visualization link -- SNPs and structural variants link Notebook RNA sequencing link Notebook De-novo assembly link -- Microbiomes and metagenomics link -- Single cell RNA sequencing link Notebook Course material 2023 (on its way after the course's end) \u00b6 Here you find a table with the instructor's slides and a link to the compiled notebooks, that you can also run on your own following the instructions in this webpage. Data alignment can also be performed on the Galaxy interactive webpage (see the galaxy exercise in this webpage).","title":"Course Introduction"},{"location":"index.html#introduction-to-next-generation-sequencing-data","text":"","title":"Introduction to Next Generation Sequencing data"},{"location":"index.html#a-course-of-the-summer-school-of-aarhus-university","text":"Computing and didactical support from the Danish Health Data Science Sandbox The material for this course is organized in four separated jupyter notebooks in both bash , python and R where you will benefit of an interactive coding setup on jupyterlab . Overview After the course, you will have knowledge of bioinformatics methods for analyzing genomes using NGS data, including knowledge of the existing types of genome data, how the different types of data can be displayed and analyzed, the current methods for genome assembly and analysis, their accuracy and how they can be used. The course will enable you to devise and run a project that makes use of NGS data. \ud83d\udcda Prerequisites: This is an introductory course that needs a basic understanding of the biology behind sequencing, and not necessarily programming experience (though this would help!). \ud83d\udcac Syllabus: 1. Describe key challenges in the analysis of NGS data 2. Explain the theoretical foundation for methods that use NGS for assembly and analysis of genomes 3. Discuss the bioinformatic methods for genome analysis and hypothesize what drives the outcome of the methods 4. Review original literature within the subjects and relate the discussed topics to analysis scenarios 5. Apply bioinformatics tools within the selected application areas and reflect on the results, formulating your own conclusion in the proposed tasks \ud83d\udd70 Total Time Estimation: 20 hours \ud83d\udcc1 Supporting Materials: jupyter notebooks for interactive coding lecture slides from the instructor You can find the links to the material in the table at the bottom of this page. \ud83d\udd8d Course authors and instructors: Mikkel H Schierup Stig U Andersen Samuele Soraggi Peter S Porsborg Adri\u00e1n G Repoll\u00e9s \ud83d\udccb License: Tutorial Content is licensed under Creative Commons Attribution 4.0 International License \ud83d\udcdd Citation: If you use any of this material for your research, please cite this course with the DOI below, and acknowledge the Health Data Science Sandbox project of the Novo Nordisk Foundation (grant number NNF20OC0063268). It is of great help to support the project. \ud83d\udce7 Contact: Samuele Soraggi (samuele at birc.au.dk). Course structure and Instructions The course exercises are organized in four exercise modules. The first one is executed with the web interface usegalaxy.org . Click on 1.Galaxy Exercise in the menu for instructions) Afterwards, we will work on a computing environment to use jupyterlab . Use 2.Instructions for instructions. The menu 3.Course exercises contains all the compiled exercises as a reference.","title":"A course of the Summer School of Aarhus University"},{"location":"index.html#course-material-2022","text":"Here you find a table with the instructor's slides from 2022. Topic Slide Notebook Sequencing technologies link -- Mapping to reference link Notebook Data visualization link -- SNPs and structural variants link Notebook RNA sequencing link Notebook De-novo assembly link -- Microbiomes and metagenomics link -- Single cell RNA sequencing link Notebook","title":"Course material 2022"},{"location":"index.html#course-material-2023-on-its-way-after-the-courses-end","text":"Here you find a table with the instructor's slides and a link to the compiled notebooks, that you can also run on your own following the instructions in this webpage. Data alignment can also be performed on the Galaxy interactive webpage (see the galaxy exercise in this webpage).","title":"Course material 2023 (on its way after the course's end)"},{"location":"empty.html","text":"General Page \u00b6 Section Overview \ud83d\udd70 Time Estimation: X minutes \ud83d\udcac Learning Objectives: 1. First item 2. Second item Write your introduction to the page here. Useful functions \u00b6 You should continue to write your markdown document as normal. But here are some useful functions. You can find more in the Reference guide listed on the tab above. Be sure to delete the guide and the tips below when finished with them. Code \u00b6 Text will be highlighted appropriately when you include language abbreviation: import tensorflow as tf Admonitions \u00b6 The admonitions used for course/section overview and requirements should be consistent, though you can use any other admonitions freely. Examples (both drop-down and not): Quote Here is a quote What is the smallest country in the world? A: Vatican City Footnotes \u00b6 We can include footnotes like this one 1 . LaTeX \u00b6 You write an equation as normal: \\[ \\operatorname{ker} f=\\{g\\in G:f(g)=e_{H}\\}{\\mbox{.}} \\] Images \u00b6 Format is similar to links, but include an exclamation mark before: You can link to a URL or to somewhere locally. Remember to eat your vegetables. \u21a9","title":"General page"},{"location":"empty.html#general-page","text":"Section Overview \ud83d\udd70 Time Estimation: X minutes \ud83d\udcac Learning Objectives: 1. First item 2. Second item Write your introduction to the page here.","title":"General Page"},{"location":"empty.html#useful-functions","text":"You should continue to write your markdown document as normal. But here are some useful functions. You can find more in the Reference guide listed on the tab above. Be sure to delete the guide and the tips below when finished with them.","title":"Useful functions"},{"location":"empty.html#code","text":"Text will be highlighted appropriately when you include language abbreviation: import tensorflow as tf","title":"Code"},{"location":"empty.html#admonitions","text":"The admonitions used for course/section overview and requirements should be consistent, though you can use any other admonitions freely. Examples (both drop-down and not): Quote Here is a quote What is the smallest country in the world? A: Vatican City","title":"Admonitions"},{"location":"empty.html#footnotes","text":"We can include footnotes like this one 1 .","title":"Footnotes"},{"location":"empty.html#latex","text":"You write an equation as normal: \\[ \\operatorname{ker} f=\\{g\\in G:f(g)=e_{H}\\}{\\mbox{.}} \\]","title":"LaTeX"},{"location":"empty.html#images","text":"Format is similar to links, but include an exclamation mark before: You can link to a URL or to somewhere locally. Remember to eat your vegetables. \u21a9","title":"Images"},{"location":"filtering.html","text":"Data filtering \u00b6 Each person works on its own personal folder, which contains a dataset. As a reminder, these are the personal folders Name Sample folder Andersen, Albert Lund Gifu_ctr_1 Bagger, Andreas Gifu_ctr_2 Hansen, Mads W\u00fcrgler Gifu_ctr_ 3 Milo, Lasse Gifu_ctr_4 Reimick, Sebastian Haunstrup Gifu_R7A_1 Hemmingsen, Jonas Klejs Gifu_R7A_2 Skovm\u00f8ller, Emma Hvitfeldt Gifu_R7A_3 S\u00f8rensen, Emma Frasez Gifu_R7A_4 Agersnap, Simon N\u00f8rregaard HAR1_ctr_1 Schmidt, Alina HAR1_ctr_2 Henriksen, Frederik Oskar HAR1_ctr_3 Lundby, Josephine Marie HAR1_ctr_4 N\u00f8rholm, Anne HAR1_R7A_1 Odgaard, Louise Nyrup HAR1_R7A_2 S\u00f8rensen, Sara Sejer HAR1_R7A_3 L\u00f8nskov, Jonas HAR1_R7A_4 Niklassen, Jacob Hansen Gifu_ctr_1_bis \u00d8llgaard, Ann Mai Br\u00f8ndum Holm Gifu_ctr_2_bis Overgaard, Morten \u00d8gelund Gifu_R7A_1_bis S\u00f8rensen, Elisabeth Asta Gifu_R7A_2_bis Rey, Isabel HAR1_ctr_1_bis Instructions \u00b6 Open a new session of jupyterlab in the same way as in the tutorial session Find your way to your personal folder, which is inside 426401/Students_analysis Start filtering your data using the notebook filtering.ipynb . This is a copy of the first part of the tutorial, so be careful about small changes you have to made (e.g. condition name, thresholds). For the condition name created in the metadata at the beginning of the tutorial, here we use one of Gifu_Control , Gifu_Infected , HAR1_Control , HAR1_Infected (depending on the dataset you have assigned), so that it is easy to integrated all datasets at the end. When you are done and you saved your data at the end (keep the filename filtered.h5Seurat ), write when you are done at this google docs under the sheet Filtering , so we all know when we are ready to proceed to data integration.","title":"Data filtering"},{"location":"filtering.html#data-filtering","text":"Each person works on its own personal folder, which contains a dataset. As a reminder, these are the personal folders Name Sample folder Andersen, Albert Lund Gifu_ctr_1 Bagger, Andreas Gifu_ctr_2 Hansen, Mads W\u00fcrgler Gifu_ctr_ 3 Milo, Lasse Gifu_ctr_4 Reimick, Sebastian Haunstrup Gifu_R7A_1 Hemmingsen, Jonas Klejs Gifu_R7A_2 Skovm\u00f8ller, Emma Hvitfeldt Gifu_R7A_3 S\u00f8rensen, Emma Frasez Gifu_R7A_4 Agersnap, Simon N\u00f8rregaard HAR1_ctr_1 Schmidt, Alina HAR1_ctr_2 Henriksen, Frederik Oskar HAR1_ctr_3 Lundby, Josephine Marie HAR1_ctr_4 N\u00f8rholm, Anne HAR1_R7A_1 Odgaard, Louise Nyrup HAR1_R7A_2 S\u00f8rensen, Sara Sejer HAR1_R7A_3 L\u00f8nskov, Jonas HAR1_R7A_4 Niklassen, Jacob Hansen Gifu_ctr_1_bis \u00d8llgaard, Ann Mai Br\u00f8ndum Holm Gifu_ctr_2_bis Overgaard, Morten \u00d8gelund Gifu_R7A_1_bis S\u00f8rensen, Elisabeth Asta Gifu_R7A_2_bis Rey, Isabel HAR1_ctr_1_bis","title":"Data filtering"},{"location":"filtering.html#instructions","text":"Open a new session of jupyterlab in the same way as in the tutorial session Find your way to your personal folder, which is inside 426401/Students_analysis Start filtering your data using the notebook filtering.ipynb . This is a copy of the first part of the tutorial, so be careful about small changes you have to made (e.g. condition name, thresholds). For the condition name created in the metadata at the beginning of the tutorial, here we use one of Gifu_Control , Gifu_Infected , HAR1_Control , HAR1_Infected (depending on the dataset you have assigned), so that it is easy to integrated all datasets at the end. When you are done and you saved your data at the end (keep the filename filtered.h5Seurat ), write when you are done at this google docs under the sheet Filtering , so we all know when we are ready to proceed to data integration.","title":"Instructions"},{"location":"galaxy.html","text":"Quality Control and Alignment of raw reads \u00b6 This first exercise will be executed on Galaxy , an interactive platform to run bioinformatics workflows. We will replicate this lesson with computer code later in this course. Galaxy has the possibility of working with a free account. Biological introduction \u00b6 White clover ( Trifolium repens ) is an allotetraploid . This means that it contains genomes originating from two different species within the same nucleus. Normally, white clover is an outbreeding species, but a self-compatible line was used for sequencing the white clover genome . This line is designated S10 in your data, indicating that this is the 10 th self-fertilized generation. In addition, you have data from a wild clover accession (ecotype) called Tienshan ( Ti ), which is collected from Chinese mountains and is adapted to alpine conditions. Figure: Characterisation of the white clover population. T.Repens is a hybrid of T.Occidentale and T.Pallescens Exercise guide \u00b6 Prepare the tools and data \u00b6 Install IGV on your computer from here . This is a genome browser you will use to look at some files. Create an account at usegalaxy.org and log into galaxy. Find the course data by going to this web address and by clicking on Import this history (top left corner of the page). Data \u00b6 You will be working with two types of sequencing data. The first is PacBio Hifi reads , which are long and accurate. You can find them under Hifi_reads_white_clover.fastq. The second type is Illumina RNA-seq reads , which are short and accurate and should be aligned using a spliced aligner, such as STAR . There are 24 of these files, 12 for each of the two genotypes mentioned before. The files are named [genotype]_[treatment]_[replicate].fastq . Treatment 1 is before and treatment 2 is after exposure to frost, respectively. In addition to the sequencing data, there are also three reference files: one for homologous contig 1 (referencing T. occidentale-derived subgenome), one for contig 2 (T. pallescens-derived subgenome) and one for both Contigs 1 and 2. The reference files are in fasta format. The file white_clover_genes.gtf contains the gene annotations for the two contigs. Workflow illustration \u00b6 Through Galaxy , we build a workflow applying tools to the data. We will look at the quality of the raw reads for both PacBio HiFi and Illumina RNA-seq reads. Afterwards, we align to references, using two different tools for the two types of data. Finally, we will look at the alignments on a genome browser. We will work then on a computing cluster through uCloud to analyze the aligned data in some of the upcoming lessons of the course. Galaxy Workflow \u00b6 When you import the files, what you actually import is a History - a sequence of files and softwares applied on the data. You can see the history on the right side of your usegalaxy.org webpage with green panels. Here, we only have the starting data, and you will build the rest of your history through various tools. On the left side of the screen, you have a menu with various available tools organized by category. All those softwares are also available on a classical computing command line (we will try those as well). Quality control \u00b6 1 Run FastQC on the PacBio Hifi reads and on two of the Illumina RNA-seq libraries. FastQC does quality control of the raw sequence data, providing an overview of the data which can help identify if there are any problems that should be addressed before further analysis. In the tool menu, click on FASTQ quality control --> FASTQC read quality reports . You will see a window with tool parameters : for the first option (raw read data from history), choose multiple files and select Hifi_reads_white_clover.fastq plus other fastq files you want to see the quality of (example in figure below). Then click on the button Run Tool . You will notice that some new elements are added to your history. Part of them are FastQC producing a text file, while others are FastQC producing a webpage report. The reports are ready when coloured in green: click on the eye symbol of a history item to read a report. 2 FastQC provides a report for each sample. To have a better comparison between the Hifi and Illumina data, we would combine the three FastQC reports into one using MultiQC . Choose the MultiQC tool from FASTQ quality control --> MultiQC aggregate results from ... . In the options, select FastQC as the used tool for the logsselect FastQC as the tool used to generate the output, and then select the items of FastQC of your history producing RawData (Figure below). In this way, you build a pipeline from the previous reports to the new tool you are using. Now click on Run Tool . The tool will be now running in your history. When it is done, click on the eye symbol to see the report. select the three \u201cRawData\u201d outputs generated by FastQC. Visualize the Webpage generated by MultiQC. Hint: You can find a \u201cHelp\u201d button that offers additional information about the plots for each panel. Questions: Focus on the following panels: \u201cPer base sequence quality\u201d, \u201cPer sequence quality scores\u201d\u2026. (\u201cPer base sequence content\u201d always gives a FAIL for RNA-seq data). What do you notice with respect to the sequence quality scores? And are there any other quality issues worth noting? Hifi Data Alignment \u00b6 3 Map the PacBio Hifi reads ( Hifi_reads_white_clover.fastq ) to the white clover reference sequence (Contigs 1 and 2) using minimap2 (Map with minimap2). Find Genomics Analysis --> Mapping --> Map with minimap2 . In the options, do not leave Use a built-in genome index , but select the option for having a genome from history. Choose then DNA_Contig1_2.fasta as the reference sequence. Under the profile with preset options, choose PacBio/Oxford Nanopore read to reference mapping (map-pb) . Then click on Run Tool . 4 Run the same alignment, but choose as preset options Long assembly to reference mapping. Divergence is far below 20% (asm20) . Rename then the two alignments using the edit function ( pen symbol in the history). Use for example names Contig1_2_mappb and Contig1_2_asm20 , to distinguish alignment options and reference genome. 5 The aligned genomes are not sorted by coordinates. Sort the alignments using Samtools sort (Find the tool under Genomic file manipulation --> SAM/BAM --> Samtools sort ... ). In the options, choose the two aligned files with multiple selection. Then click on Run Tool . 6 Download the two alignments to your computer. To do so, click on the disk symbol of each file in your history, and for each download both the Dataset (alignments in bam format) and their index files (in bai format). Download as well the reference genome in fasta format ( DNA_Contig1_2.fasta from the history). 7 Open IGV on your computer. Load the reference first: go on Genome --> Load genome from file and select the fasta file you downloaded. Then load the two alignments: go on File --> Load from file and select the bam and bai files you downloaded, together. You can now visualize the alignments. Questions: Look at the alignments in IGV. What do you notice about the alignments? What is the difference between the two alignments? Do you think one of them is better than the other? Choose on of the two alignments for the next steps. 8 Repeat the alignment with Minimap2 (using the chosen alignment option from the question above) and the sorting, but using the reference genomes for Contig 1 and for Contig 2 searaately. Note: you can run all at once by choosing multiple reference genomes in the options! Questions: Download the two references for Contig 1 and 2, and the two sorted alignments. Load the references from the menu `Genomes` in IGV, and then open the two alignments using the menu `File` in IGV. - Why do you see fluctuations in coverage and large regions without any apparent subgenome SNPs? - What are the major differences between the stats for the reads mapped to Contigs1&2 versus contig1 and contig2? What is your interpretation of the differences? RNA Data Alignment \u00b6 9 First, group the 24 RNA-seq libraries into two dataset lists, one list of pairs for S10 libraries and another for Tienshan libraries. so we can work with multiple samples simultaneously. You can do this by selecting the libraries for each genotype and choosing Build Lists of Dataset Pairs . The grouping suggested by Galaxy is wrong, because the paired reads are paired according to _1 and _2 in their names. Change those two with R1 and R2 , click on Unpair all , and then click on the suggested corrected pairs with the buttons Pair these datasets . Your sequences will be substituted by two elements in your history. Here we chose for example to name them S10 and TI . 10 Do alignment of the RNA-seq lists of raw files to the reference DNA_Contig1_2.fasta using STAR . Go to Genomics analysis --> RNA-seq --> RNA STAR Gapped-read mapper for RNA-seq data . In the options use: as data, the parameter Paired-end (as collection) , and then choose one of the two collections (you cannot run them all at once) as reference, DNA_Contig1_2.fasta , with Length of SA pre-indexing string equal to 9 as index with gene-model, use white_clover_annotations.gtf as output, Per gene read counts (GeneCounts) . 11 Use MultiQC to see the quality of the output. The alignment of STAR produces log files which can be used for quality reports. Go on Genomic File Manipulation --> MultiQC . In the options select the tool STAR . Then Insert STAR output , as type of output the Log , and choose the two logs listing collections of STAR alignments. Then click on Run Tool . View the report to see the alignment statistics. Final note: `Galaxy can also be used to create an automatic workflow that will map the data. This workflow can be useful when running multiple samples. You can generate a workflow from the analysis already completed in a history, by going to Settings \u2192 Extract workflow. You can also create a workflow from scratch using the Workflow editor.","title":"1.Galaxy Exercise"},{"location":"galaxy.html#quality-control-and-alignment-of-raw-reads","text":"This first exercise will be executed on Galaxy , an interactive platform to run bioinformatics workflows. We will replicate this lesson with computer code later in this course. Galaxy has the possibility of working with a free account.","title":"Quality Control and Alignment of raw reads"},{"location":"galaxy.html#biological-introduction","text":"White clover ( Trifolium repens ) is an allotetraploid . This means that it contains genomes originating from two different species within the same nucleus. Normally, white clover is an outbreeding species, but a self-compatible line was used for sequencing the white clover genome . This line is designated S10 in your data, indicating that this is the 10 th self-fertilized generation. In addition, you have data from a wild clover accession (ecotype) called Tienshan ( Ti ), which is collected from Chinese mountains and is adapted to alpine conditions. Figure: Characterisation of the white clover population. T.Repens is a hybrid of T.Occidentale and T.Pallescens","title":"Biological introduction"},{"location":"galaxy.html#exercise-guide","text":"","title":"Exercise guide"},{"location":"galaxy.html#prepare-the-tools-and-data","text":"Install IGV on your computer from here . This is a genome browser you will use to look at some files. Create an account at usegalaxy.org and log into galaxy. Find the course data by going to this web address and by clicking on Import this history (top left corner of the page).","title":"Prepare the tools and data"},{"location":"galaxy.html#data","text":"You will be working with two types of sequencing data. The first is PacBio Hifi reads , which are long and accurate. You can find them under Hifi_reads_white_clover.fastq. The second type is Illumina RNA-seq reads , which are short and accurate and should be aligned using a spliced aligner, such as STAR . There are 24 of these files, 12 for each of the two genotypes mentioned before. The files are named [genotype]_[treatment]_[replicate].fastq . Treatment 1 is before and treatment 2 is after exposure to frost, respectively. In addition to the sequencing data, there are also three reference files: one for homologous contig 1 (referencing T. occidentale-derived subgenome), one for contig 2 (T. pallescens-derived subgenome) and one for both Contigs 1 and 2. The reference files are in fasta format. The file white_clover_genes.gtf contains the gene annotations for the two contigs.","title":"Data"},{"location":"galaxy.html#workflow-illustration","text":"Through Galaxy , we build a workflow applying tools to the data. We will look at the quality of the raw reads for both PacBio HiFi and Illumina RNA-seq reads. Afterwards, we align to references, using two different tools for the two types of data. Finally, we will look at the alignments on a genome browser. We will work then on a computing cluster through uCloud to analyze the aligned data in some of the upcoming lessons of the course.","title":"Workflow illustration"},{"location":"galaxy.html#galaxy-workflow","text":"When you import the files, what you actually import is a History - a sequence of files and softwares applied on the data. You can see the history on the right side of your usegalaxy.org webpage with green panels. Here, we only have the starting data, and you will build the rest of your history through various tools. On the left side of the screen, you have a menu with various available tools organized by category. All those softwares are also available on a classical computing command line (we will try those as well).","title":"Galaxy Workflow"},{"location":"galaxy.html#quality-control","text":"1 Run FastQC on the PacBio Hifi reads and on two of the Illumina RNA-seq libraries. FastQC does quality control of the raw sequence data, providing an overview of the data which can help identify if there are any problems that should be addressed before further analysis. In the tool menu, click on FASTQ quality control --> FASTQC read quality reports . You will see a window with tool parameters : for the first option (raw read data from history), choose multiple files and select Hifi_reads_white_clover.fastq plus other fastq files you want to see the quality of (example in figure below). Then click on the button Run Tool . You will notice that some new elements are added to your history. Part of them are FastQC producing a text file, while others are FastQC producing a webpage report. The reports are ready when coloured in green: click on the eye symbol of a history item to read a report. 2 FastQC provides a report for each sample. To have a better comparison between the Hifi and Illumina data, we would combine the three FastQC reports into one using MultiQC . Choose the MultiQC tool from FASTQ quality control --> MultiQC aggregate results from ... . In the options, select FastQC as the used tool for the logsselect FastQC as the tool used to generate the output, and then select the items of FastQC of your history producing RawData (Figure below). In this way, you build a pipeline from the previous reports to the new tool you are using. Now click on Run Tool . The tool will be now running in your history. When it is done, click on the eye symbol to see the report. select the three \u201cRawData\u201d outputs generated by FastQC. Visualize the Webpage generated by MultiQC. Hint: You can find a \u201cHelp\u201d button that offers additional information about the plots for each panel. Questions: Focus on the following panels: \u201cPer base sequence quality\u201d, \u201cPer sequence quality scores\u201d\u2026. (\u201cPer base sequence content\u201d always gives a FAIL for RNA-seq data). What do you notice with respect to the sequence quality scores? And are there any other quality issues worth noting?","title":"Quality control"},{"location":"galaxy.html#hifi-data-alignment","text":"3 Map the PacBio Hifi reads ( Hifi_reads_white_clover.fastq ) to the white clover reference sequence (Contigs 1 and 2) using minimap2 (Map with minimap2). Find Genomics Analysis --> Mapping --> Map with minimap2 . In the options, do not leave Use a built-in genome index , but select the option for having a genome from history. Choose then DNA_Contig1_2.fasta as the reference sequence. Under the profile with preset options, choose PacBio/Oxford Nanopore read to reference mapping (map-pb) . Then click on Run Tool . 4 Run the same alignment, but choose as preset options Long assembly to reference mapping. Divergence is far below 20% (asm20) . Rename then the two alignments using the edit function ( pen symbol in the history). Use for example names Contig1_2_mappb and Contig1_2_asm20 , to distinguish alignment options and reference genome. 5 The aligned genomes are not sorted by coordinates. Sort the alignments using Samtools sort (Find the tool under Genomic file manipulation --> SAM/BAM --> Samtools sort ... ). In the options, choose the two aligned files with multiple selection. Then click on Run Tool . 6 Download the two alignments to your computer. To do so, click on the disk symbol of each file in your history, and for each download both the Dataset (alignments in bam format) and their index files (in bai format). Download as well the reference genome in fasta format ( DNA_Contig1_2.fasta from the history). 7 Open IGV on your computer. Load the reference first: go on Genome --> Load genome from file and select the fasta file you downloaded. Then load the two alignments: go on File --> Load from file and select the bam and bai files you downloaded, together. You can now visualize the alignments. Questions: Look at the alignments in IGV. What do you notice about the alignments? What is the difference between the two alignments? Do you think one of them is better than the other? Choose on of the two alignments for the next steps. 8 Repeat the alignment with Minimap2 (using the chosen alignment option from the question above) and the sorting, but using the reference genomes for Contig 1 and for Contig 2 searaately. Note: you can run all at once by choosing multiple reference genomes in the options! Questions: Download the two references for Contig 1 and 2, and the two sorted alignments. Load the references from the menu `Genomes` in IGV, and then open the two alignments using the menu `File` in IGV. - Why do you see fluctuations in coverage and large regions without any apparent subgenome SNPs? - What are the major differences between the stats for the reads mapped to Contigs1&2 versus contig1 and contig2? What is your interpretation of the differences?","title":"Hifi Data Alignment"},{"location":"galaxy.html#rna-data-alignment","text":"9 First, group the 24 RNA-seq libraries into two dataset lists, one list of pairs for S10 libraries and another for Tienshan libraries. so we can work with multiple samples simultaneously. You can do this by selecting the libraries for each genotype and choosing Build Lists of Dataset Pairs . The grouping suggested by Galaxy is wrong, because the paired reads are paired according to _1 and _2 in their names. Change those two with R1 and R2 , click on Unpair all , and then click on the suggested corrected pairs with the buttons Pair these datasets . Your sequences will be substituted by two elements in your history. Here we chose for example to name them S10 and TI . 10 Do alignment of the RNA-seq lists of raw files to the reference DNA_Contig1_2.fasta using STAR . Go to Genomics analysis --> RNA-seq --> RNA STAR Gapped-read mapper for RNA-seq data . In the options use: as data, the parameter Paired-end (as collection) , and then choose one of the two collections (you cannot run them all at once) as reference, DNA_Contig1_2.fasta , with Length of SA pre-indexing string equal to 9 as index with gene-model, use white_clover_annotations.gtf as output, Per gene read counts (GeneCounts) . 11 Use MultiQC to see the quality of the output. The alignment of STAR produces log files which can be used for quality reports. Go on Genomic File Manipulation --> MultiQC . In the options select the tool STAR . Then Insert STAR output , as type of output the Log , and choose the two logs listing collections of STAR alignments. Then click on Run Tool . View the report to see the alignment statistics. Final note: `Galaxy can also be used to create an automatic workflow that will map the data. This workflow can be useful when running multiple samples. You can generate a workflow from the analysis already completed in a history, by going to Settings \u2192 Extract workflow. You can also create a workflow from scratch using the Workflow editor.","title":"RNA Data Alignment"},{"location":"instructions.html","text":"Instructions \u00b6 You have various options to run the course material. You can execute it on a supercomputing system or on your own computer. Access the material on uCloud \u00b6 Ucloud is an interactive online platform from the University of Southern Danmark eScience center that allows users to execute softwares and computer code directly from their browser. If you are enrolled in a danish university or hospital, you can access the platform for free and try the course. Please follow these instructions to access uCloud and get access to the course: To access uCloud for the first time, go to the ucloud webpage and log in by pressing on the green button, choosing your institution, and writing your institutional credentials. If you have a mail address from a danish institution or university, you should be able to login with your institutional credentials. If you are participating in the NGS Summer Course 2023 in Aarhus, click AFTER logging in on this link . This will add you to a project on uCloud, where we have data and extra computing credit for the course. You should see a message on your browser where you have to accept the invitation to the project. Click on the uCloud symbol on the top-left corner of the uCloud webpage. You should see a dashboard window similar to the one below. The dashboard is a home page you will always see when opening uCloud. You can use the menu on the left to navigate to different sections of uCloud (File explorer, applications, etc.) On top of the uCloud interface you have My Workspace . That is your private space where you have some free hundreds of CPU hours and GBs of memory to run different applications (that you can see by using the menu Apps on the left). Click on Apps and find the application called Genomics Courses . Here choose the course Intro to NGS data , and use the other options to choose, for example, how many resources you want to use (we suggest at least 8 vCPUs) Run the docker container \u00b6 With Docker , you can create containers that will work in the same way on all the machines. The results of the course are exactly reproducible if you use such a Docker container. We created a container for the course that already contains packages and course material installed. You need to download Docker Desktop at this link . We suggest you have at least 8GB of RAM and 20GB of hard disk space. When Docker Desktop is installed, you can use the command line to retrieve the container from the web: docker pull samuelesoraggi/ngs-summer-aarhus:2022.08.01 You can now execute the container. You will see a web address in the output of the command line: you need to copy that into your web browser to see jupyterlab . docker run --rm -it -p 8888:8888 samuelesoraggi/ngs-summer-aarhus:2022.08.01","title":"Instructions"},{"location":"instructions.html#instructions","text":"You have various options to run the course material. You can execute it on a supercomputing system or on your own computer.","title":"Instructions"},{"location":"instructions.html#access-the-material-on-ucloud","text":"Ucloud is an interactive online platform from the University of Southern Danmark eScience center that allows users to execute softwares and computer code directly from their browser. If you are enrolled in a danish university or hospital, you can access the platform for free and try the course. Please follow these instructions to access uCloud and get access to the course: To access uCloud for the first time, go to the ucloud webpage and log in by pressing on the green button, choosing your institution, and writing your institutional credentials. If you have a mail address from a danish institution or university, you should be able to login with your institutional credentials. If you are participating in the NGS Summer Course 2023 in Aarhus, click AFTER logging in on this link . This will add you to a project on uCloud, where we have data and extra computing credit for the course. You should see a message on your browser where you have to accept the invitation to the project. Click on the uCloud symbol on the top-left corner of the uCloud webpage. You should see a dashboard window similar to the one below. The dashboard is a home page you will always see when opening uCloud. You can use the menu on the left to navigate to different sections of uCloud (File explorer, applications, etc.) On top of the uCloud interface you have My Workspace . That is your private space where you have some free hundreds of CPU hours and GBs of memory to run different applications (that you can see by using the menu Apps on the left). Click on Apps and find the application called Genomics Courses . Here choose the course Intro to NGS data , and use the other options to choose, for example, how many resources you want to use (we suggest at least 8 vCPUs)","title":"Access the material on uCloud"},{"location":"instructions.html#run-the-docker-container","text":"With Docker , you can create containers that will work in the same way on all the machines. The results of the course are exactly reproducible if you use such a Docker container. We created a container for the course that already contains packages and course material installed. You need to download Docker Desktop at this link . We suggest you have at least 8GB of RAM and 20GB of hard disk space. When Docker Desktop is installed, you can use the command line to retrieve the container from the web: docker pull samuelesoraggi/ngs-summer-aarhus:2022.08.01 You can now execute the container. You will see a web address in the output of the command line: you need to copy that into your web browser to see jupyterlab . docker run --rm -it -p 8888:8888 samuelesoraggi/ngs-summer-aarhus:2022.08.01","title":"Run the docker container"},{"location":"ucloud.html","text":"Access through uCloud \u00b6 Ucloud is an interactive online platform from the University of Southern Danmark eScience center that allows users to execute softwares and computer code directly from their browser. If you have an account from a danish university, hospital, and other institutions, you can access the platform for free and try the course. Starting a session of the Introduction to NGS data analysis on uCloud \u00b6 Log onto ucloud at the address http://cloud.sdu.dk using the university/institution credentials you possess. If you are participating in the NGS Summer Course 2023 in Aarhus, click AFTER logging in on this link . You should see a message on your browser where you have to accept the invitation to the project. This will add you to a project on uCloud, where we have data and extra computing credit for the course. Be sure to be in the right project. If you are participating in the NGS Summer Course 2023 in Aarhus, choose NGS Summer Course from the project menu (red circle). Afterwards, click on the App menu (green circle) Note: The project My Workspace is instead your private space where you have some free hundreds of CPU hours and GBs of memory. Find the app Genomics Sandbox (red circle), which is under the title Featured . Click on it. You will get into the settings window. Choose any Job Name (Nr 1 in the figure below), how many hours you want to use for the job (Nr 2, choose at least 2 hours, you can increase this later), and how many CPUs (Nr 3, choose at least 4 CPUs for the first three exercises, but use at least 8 CPUs to run the single cell analysis). Select the Introduction to NGS Data Analysis as course (Nr 4). Then click on Submit (Nr 5). You will now enter a waiting queue. When the session starts, the timer begins to count down (red circle). In a couple of minutes you should be able to open the interface through the button (green circle). Note the buttons to add time to your session (blue circle) and the button to stop the session when you are done (pink circle) You will be able to see jupyterlab in your browser. On the left side of the screen you can see various folders. You can find the exercises inside the folder Notebooks . You can download any file by right-clicking on it and choosing Download . You can also upload files by clicking on the Upload button on the top left corner of the screen, if you want to experiment with your own data! Reuse your notebooks and data \u00b6 The first time you run the Genomics Sandbox, you download data and notebooks. You can reuse them! Open the Genomics Sandbox App and use the settings to run it. You can click on your previous job name to recycle the settings (red circle). Now click on Add Folder (red circle) and on the appearing browsing bar (green circle). You need to find a drive with your user name (red circle), written as Member Files: NameSurname#Number . Then, go into Jobs/Genomics Sandbox/JobID/Notebooks and choose the folder (green circle). JobID corresponds to the Job Name you used in the previous session, followed by some random number in parenthesis. Do the same procedure adding a folder again and finding Jobs/Genomics Sandbox/JobID/Data . At the end you should have two added folders like this: Click on Submit. Now you will be able to reuse data, results and exercises on which you worked before. Note: remember to recycle the settings of this session the next time, so you can actually reuse the data and notebooks without having to add folders.","title":"2.1.Ucloud Access"},{"location":"ucloud.html#access-through-ucloud","text":"Ucloud is an interactive online platform from the University of Southern Danmark eScience center that allows users to execute softwares and computer code directly from their browser. If you have an account from a danish university, hospital, and other institutions, you can access the platform for free and try the course.","title":"Access through uCloud"},{"location":"ucloud.html#starting-a-session-of-the-introduction-to-ngs-data-analysis-on-ucloud","text":"Log onto ucloud at the address http://cloud.sdu.dk using the university/institution credentials you possess. If you are participating in the NGS Summer Course 2023 in Aarhus, click AFTER logging in on this link . You should see a message on your browser where you have to accept the invitation to the project. This will add you to a project on uCloud, where we have data and extra computing credit for the course. Be sure to be in the right project. If you are participating in the NGS Summer Course 2023 in Aarhus, choose NGS Summer Course from the project menu (red circle). Afterwards, click on the App menu (green circle) Note: The project My Workspace is instead your private space where you have some free hundreds of CPU hours and GBs of memory. Find the app Genomics Sandbox (red circle), which is under the title Featured . Click on it. You will get into the settings window. Choose any Job Name (Nr 1 in the figure below), how many hours you want to use for the job (Nr 2, choose at least 2 hours, you can increase this later), and how many CPUs (Nr 3, choose at least 4 CPUs for the first three exercises, but use at least 8 CPUs to run the single cell analysis). Select the Introduction to NGS Data Analysis as course (Nr 4). Then click on Submit (Nr 5). You will now enter a waiting queue. When the session starts, the timer begins to count down (red circle). In a couple of minutes you should be able to open the interface through the button (green circle). Note the buttons to add time to your session (blue circle) and the button to stop the session when you are done (pink circle) You will be able to see jupyterlab in your browser. On the left side of the screen you can see various folders. You can find the exercises inside the folder Notebooks . You can download any file by right-clicking on it and choosing Download . You can also upload files by clicking on the Upload button on the top left corner of the screen, if you want to experiment with your own data!","title":"Starting a session of the Introduction to NGS data analysis on uCloud"},{"location":"ucloud.html#reuse-your-notebooks-and-data","text":"The first time you run the Genomics Sandbox, you download data and notebooks. You can reuse them! Open the Genomics Sandbox App and use the settings to run it. You can click on your previous job name to recycle the settings (red circle). Now click on Add Folder (red circle) and on the appearing browsing bar (green circle). You need to find a drive with your user name (red circle), written as Member Files: NameSurname#Number . Then, go into Jobs/Genomics Sandbox/JobID/Notebooks and choose the folder (green circle). JobID corresponds to the Job Name you used in the previous session, followed by some random number in parenthesis. Do the same procedure adding a folder again and finding Jobs/Genomics Sandbox/JobID/Data . At the end you should have two added folders like this: Click on Submit. Now you will be able to reuse data, results and exercises on which you worked before. Note: remember to recycle the settings of this session the next time, so you can actually reuse the data and notebooks without having to add folders.","title":"Reuse your notebooks and data"},{"location":"nb/align.html","text":"(function (global, factory) { typeof exports === 'object' && typeof module !== 'undefined' ? module.exports = factory() : typeof define === 'function' && define.amd ? define(factory) : (global = global || self, global.ClipboardCopyElement = factory()); }(this, function () { 'use strict'; function createNode(text) { const node = document.createElement('pre'); node.style.width = '1px'; node.style.height = '1px'; node.style.position = 'fixed'; node.style.top = '5px'; node.textContent = text; return node; } function copyNode(node) { if ('clipboard' in navigator) { // eslint-disable-next-line flowtype/no-flow-fix-me-comments // $FlowFixMe Clipboard is not defined in Flow yet. return navigator.clipboard.writeText(node.textContent); } const selection = getSelection(); 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When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-ui-font-scale-factor: 1.2; --jp-ui-font-size0: 0.83333em; --jp-ui-font-size1: 13px; /* Base font size */ --jp-ui-font-size2: 1.2em; --jp-ui-font-size3: 1.44em; --jp-ui-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Use these font colors against the corresponding main layout colors. * In a light theme, these go from dark to light. */ /* Defaults use Material Design specification */ --jp-ui-font-color0: rgba(0, 0, 0, 1); --jp-ui-font-color1: rgba(0, 0, 0, 0.87); --jp-ui-font-color2: rgba(0, 0, 0, 0.54); --jp-ui-font-color3: rgba(0, 0, 0, 0.38); /* * Use these against the brand/accent/warn/error colors. * These will typically go from light to darker, in both a dark and light theme. */ --jp-ui-inverse-font-color0: rgba(255, 255, 255, 1); --jp-ui-inverse-font-color1: rgba(255, 255, 255, 1); --jp-ui-inverse-font-color2: rgba(255, 255, 255, 0.7); --jp-ui-inverse-font-color3: rgba(255, 255, 255, 0.5); /* Content Fonts * * Content font variables are used for typography of user generated content. * * The font sizing here is done assuming that the body font size of --jp-content-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-content-line-height: 1.6; --jp-content-font-scale-factor: 1.2; --jp-content-font-size0: 0.83333em; --jp-content-font-size1: 14px; /* Base font size */ --jp-content-font-size2: 1.2em; --jp-content-font-size3: 1.44em; --jp-content-font-size4: 1.728em; --jp-content-font-size5: 2.0736em; /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-content-presentation-font-size1: 17px; --jp-content-heading-line-height: 1; --jp-content-heading-margin-top: 1.2em; --jp-content-heading-margin-bottom: 0.8em; --jp-content-heading-font-weight: 500; /* Defaults use Material Design specification */ --jp-content-font-color0: rgba(0, 0, 0, 1); --jp-content-font-color1: rgba(0, 0, 0, 0.87); --jp-content-font-color2: rgba(0, 0, 0, 0.54); --jp-content-font-color3: rgba(0, 0, 0, 0.38); --jp-content-link-color: var(--md-blue-700); --jp-content-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Code Fonts * * Code font variables are used for typography of code and other monospaces content. */ --jp-code-font-size: 13px; --jp-code-line-height: 1.3077; /* 17px for 13px base */ --jp-code-padding: 5px; /* 5px for 13px base, codemirror highlighting needs integer px value */ --jp-code-font-family-default: Menlo, Consolas, \"DejaVu Sans Mono\", monospace; --jp-code-font-family: var(--jp-code-font-family-default); /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-code-presentation-font-size: 16px; /* may need to tweak cursor width if you change font size */ --jp-code-cursor-width0: 1.4px; --jp-code-cursor-width1: 2px; --jp-code-cursor-width2: 4px; /* Layout * * The following are the main layout colors use in JupyterLab. In a light * theme these would go from light to dark. */ --jp-layout-color0: white; --jp-layout-color1: white; --jp-layout-color2: var(--md-grey-200); --jp-layout-color3: var(--md-grey-400); --jp-layout-color4: var(--md-grey-600); /* Inverse Layout * * The following are the inverse layout colors use in JupyterLab. In a light * theme these would go from dark to light. */ --jp-inverse-layout-color0: #111111; --jp-inverse-layout-color1: var(--md-grey-900); --jp-inverse-layout-color2: var(--md-grey-800); --jp-inverse-layout-color3: var(--md-grey-700); --jp-inverse-layout-color4: var(--md-grey-600); /* Brand/accent */ --jp-brand-color0: var(--md-blue-900); --jp-brand-color1: var(--md-blue-700); --jp-brand-color2: var(--md-blue-300); --jp-brand-color3: var(--md-blue-100); --jp-brand-color4: var(--md-blue-50); --jp-accent-color0: var(--md-green-900); --jp-accent-color1: var(--md-green-700); --jp-accent-color2: var(--md-green-300); --jp-accent-color3: var(--md-green-100); /* State colors (warn, error, success, info) */ --jp-warn-color0: var(--md-orange-900); --jp-warn-color1: var(--md-orange-700); --jp-warn-color2: var(--md-orange-300); --jp-warn-color3: var(--md-orange-100); --jp-error-color0: var(--md-red-900); --jp-error-color1: var(--md-red-700); --jp-error-color2: var(--md-red-300); --jp-error-color3: var(--md-red-100); --jp-success-color0: var(--md-green-900); --jp-success-color1: var(--md-green-700); --jp-success-color2: var(--md-green-300); --jp-success-color3: var(--md-green-100); --jp-info-color0: var(--md-cyan-900); --jp-info-color1: var(--md-cyan-700); --jp-info-color2: var(--md-cyan-300); --jp-info-color3: var(--md-cyan-100); /* Cell specific styles */ --jp-cell-padding: 5px; --jp-cell-collapser-width: 8px; --jp-cell-collapser-min-height: 20px; --jp-cell-collapser-not-active-hover-opacity: 0.6; --jp-cell-editor-background: var(--md-grey-100); --jp-cell-editor-border-color: var(--md-grey-300); --jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-cell-editor-active-background: var(--jp-layout-color0); --jp-cell-editor-active-border-color: var(--jp-brand-color1); --jp-cell-prompt-width: 64px; --jp-cell-prompt-font-family: var(--jp-code-font-family-default); --jp-cell-prompt-letter-spacing: 0px; --jp-cell-prompt-opacity: 1; --jp-cell-prompt-not-active-opacity: 0.5; --jp-cell-prompt-not-active-font-color: var(--md-grey-700); /* A custom blend of MD grey and blue 600 * See https://meyerweb.com/eric/tools/color-blend/#546E7A:1E88E5:5:hex */ --jp-cell-inprompt-font-color: #307fc1; /* A custom blend of MD grey and orange 600 * https://meyerweb.com/eric/tools/color-blend/#546E7A:F4511E:5:hex */ --jp-cell-outprompt-font-color: #bf5b3d; /* Notebook specific styles */ --jp-notebook-padding: 10px; --jp-notebook-select-background: var(--jp-layout-color1); --jp-notebook-multiselected-color: var(--md-blue-50); /* The scroll padding is calculated to fill enough space at the bottom of the notebook to show one single-line cell (with appropriate padding) at the top when the notebook is scrolled all the way to the bottom. We also subtract one pixel so that no scrollbar appears if we have just one single-line cell in the notebook. This padding is to enable a 'scroll past end' feature in a notebook. */ --jp-notebook-scroll-padding: calc( 100% - var(--jp-code-font-size) * var(--jp-code-line-height) - var(--jp-code-padding) - var(--jp-cell-padding) - 1px ); /* Rendermime styles */ --jp-rendermime-error-background: #fdd; --jp-rendermime-table-row-background: var(--md-grey-100); --jp-rendermime-table-row-hover-background: var(--md-light-blue-50); /* Dialog specific styles */ --jp-dialog-background: rgba(0, 0, 0, 0.25); /* Console specific styles */ --jp-console-padding: 10px; /* Toolbar specific styles */ --jp-toolbar-border-color: var(--jp-border-color1); --jp-toolbar-micro-height: 8px; --jp-toolbar-background: var(--jp-layout-color1); --jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, 0.24); --jp-toolbar-header-margin: 4px 4px 0px 4px; --jp-toolbar-active-background: var(--md-grey-300); /* Statusbar specific styles */ --jp-statusbar-height: 24px; /* Input field styles */ --jp-input-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-input-active-background: var(--jp-layout-color1); --jp-input-hover-background: var(--jp-layout-color1); --jp-input-background: var(--md-grey-100); --jp-input-border-color: var(--jp-border-color1); --jp-input-active-border-color: var(--jp-brand-color1); --jp-input-active-box-shadow-color: rgba(19, 124, 189, 0.3); /* General editor styles */ --jp-editor-selected-background: #d9d9d9; --jp-editor-selected-focused-background: #d7d4f0; --jp-editor-cursor-color: var(--jp-ui-font-color0); /* Code mirror specific styles */ --jp-mirror-editor-keyword-color: #008000; --jp-mirror-editor-atom-color: #88f; --jp-mirror-editor-number-color: #080; --jp-mirror-editor-def-color: #00f; --jp-mirror-editor-variable-color: var(--md-grey-900); --jp-mirror-editor-variable-2-color: #05a; --jp-mirror-editor-variable-3-color: #085; --jp-mirror-editor-punctuation-color: #05a; --jp-mirror-editor-property-color: #05a; --jp-mirror-editor-operator-color: #aa22ff; --jp-mirror-editor-comment-color: #408080; --jp-mirror-editor-string-color: #ba2121; --jp-mirror-editor-string-2-color: #708; --jp-mirror-editor-meta-color: #aa22ff; --jp-mirror-editor-qualifier-color: #555; --jp-mirror-editor-builtin-color: #008000; --jp-mirror-editor-bracket-color: #997; --jp-mirror-editor-tag-color: #170; --jp-mirror-editor-attribute-color: #00c; --jp-mirror-editor-header-color: blue; --jp-mirror-editor-quote-color: #090; --jp-mirror-editor-link-color: #00c; --jp-mirror-editor-error-color: #f00; --jp-mirror-editor-hr-color: #999; /* Vega extension styles */ --jp-vega-background: white; /* Sidebar-related styles */ --jp-sidebar-min-width: 250px; /* Search-related styles */ --jp-search-toggle-off-opacity: 0.5; --jp-search-toggle-hover-opacity: 0.8; --jp-search-toggle-on-opacity: 1; --jp-search-selected-match-background-color: rgb(245, 200, 0); --jp-search-selected-match-color: black; --jp-search-unselected-match-background-color: var( --jp-inverse-layout-color0 ); --jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0); /* Icon colors that work well with light or dark backgrounds */ --jp-icon-contrast-color0: var(--md-purple-600); --jp-icon-contrast-color1: var(--md-green-600); --jp-icon-contrast-color2: var(--md-pink-600); --jp-icon-contrast-color3: var(--md-blue-600); } [data-md-color-scheme=\"slate\"] .jupyter-wrapper { /* Elevation * * We style box-shadows using Material Design's idea of elevation. These particular numbers are taken from here: * * https://github.com/material-components/material-components-web * https://material-components-web.appspot.com/elevation.html */ /* The dark theme shadows need a bit of work, but this will probably also require work on the core layout * colors used in the theme as well. */ --jp-shadow-base-lightness: 32; --jp-shadow-umbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.2 ); --jp-shadow-penumbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.14 ); --jp-shadow-ambient-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.12 ); --jp-elevation-z0: none; --jp-elevation-z1: 0px 2px 1px -1px var(--jp-shadow-umbra-color), 0px 1px 1px 0px var(--jp-shadow-penumbra-color), 0px 1px 3px 0px var(--jp-shadow-ambient-color); --jp-elevation-z2: 0px 3px 1px -2px var(--jp-shadow-umbra-color), 0px 2px 2px 0px var(--jp-shadow-penumbra-color), 0px 1px 5px 0px var(--jp-shadow-ambient-color); --jp-elevation-z4: 0px 2px 4px -1px var(--jp-shadow-umbra-color), 0px 4px 5px 0px var(--jp-shadow-penumbra-color), 0px 1px 10px 0px var(--jp-shadow-ambient-color); --jp-elevation-z6: 0px 3px 5px -1px var(--jp-shadow-umbra-color), 0px 6px 10px 0px var(--jp-shadow-penumbra-color), 0px 1px 18px 0px var(--jp-shadow-ambient-color); --jp-elevation-z8: 0px 5px 5px -3px var(--jp-shadow-umbra-color), 0px 8px 10px 1px var(--jp-shadow-penumbra-color), 0px 3px 14px 2px var(--jp-shadow-ambient-color); --jp-elevation-z12: 0px 7px 8px -4px var(--jp-shadow-umbra-color), 0px 12px 17px 2px var(--jp-shadow-penumbra-color), 0px 5px 22px 4px var(--jp-shadow-ambient-color); --jp-elevation-z16: 0px 8px 10px -5px var(--jp-shadow-umbra-color), 0px 16px 24px 2px var(--jp-shadow-penumbra-color), 0px 6px 30px 5px var(--jp-shadow-ambient-color); --jp-elevation-z20: 0px 10px 13px -6px var(--jp-shadow-umbra-color), 0px 20px 31px 3px var(--jp-shadow-penumbra-color), 0px 8px 38px 7px var(--jp-shadow-ambient-color); --jp-elevation-z24: 0px 11px 15px -7px var(--jp-shadow-umbra-color), 0px 24px 38px 3px var(--jp-shadow-penumbra-color), 0px 9px 46px 8px var(--jp-shadow-ambient-color); /* Borders * * The following variables, specify the visual styling of borders in JupyterLab. */ --jp-border-width: 1px; --jp-border-color0: var(--md-grey-700); --jp-border-color1: var(--md-grey-700); --jp-border-color2: var(--md-grey-800); --jp-border-color3: var(--md-grey-900); --jp-border-radius: 2px; /* UI Fonts * * The UI font CSS variables are used for the typography all of the JupyterLab * user interface elements that are not directly user generated content. * * The font sizing here is done assuming that the body font size of --jp-ui-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-ui-font-scale-factor: 1.2; --jp-ui-font-size0: 0.83333em; --jp-ui-font-size1: 13px; /* Base font size */ --jp-ui-font-size2: 1.2em; --jp-ui-font-size3: 1.44em; --jp-ui-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Use these font colors against the corresponding main layout colors. * In a light theme, these go from dark to light. */ /* Defaults use Material Design specification */ --jp-ui-font-color0: rgba(255, 255, 255, 1); --jp-ui-font-color1: rgba(255, 255, 255, 0.87); --jp-ui-font-color2: rgba(255, 255, 255, 0.54); --jp-ui-font-color3: rgba(255, 255, 255, 0.38); /* * Use these against the brand/accent/warn/error colors. * These will typically go from light to darker, in both a dark and light theme. */ --jp-ui-inverse-font-color0: rgba(0, 0, 0, 1); --jp-ui-inverse-font-color1: rgba(0, 0, 0, 0.8); --jp-ui-inverse-font-color2: rgba(0, 0, 0, 0.5); --jp-ui-inverse-font-color3: rgba(0, 0, 0, 0.3); /* Content Fonts * * Content font variables are used for typography of user generated content. * * The font sizing here is done assuming that the body font size of --jp-content-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-content-line-height: 1.6; --jp-content-font-scale-factor: 1.2; --jp-content-font-size0: 0.83333em; --jp-content-font-size1: 14px; /* Base font size */ --jp-content-font-size2: 1.2em; --jp-content-font-size3: 1.44em; --jp-content-font-size4: 1.728em; --jp-content-font-size5: 2.0736em; /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-content-presentation-font-size1: 17px; --jp-content-heading-line-height: 1; --jp-content-heading-margin-top: 1.2em; --jp-content-heading-margin-bottom: 0.8em; --jp-content-heading-font-weight: 500; /* Defaults use Material Design specification */ --jp-content-font-color0: rgba(255, 255, 255, 1); --jp-content-font-color1: rgba(255, 255, 255, 1); --jp-content-font-color2: rgba(255, 255, 255, 0.7); --jp-content-font-color3: rgba(255, 255, 255, 0.5); --jp-content-link-color: var(--md-blue-300); --jp-content-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Code Fonts * * Code font variables are used for typography of code and other monospaces content. */ --jp-code-font-size: 13px; --jp-code-line-height: 1.3077; /* 17px for 13px base */ --jp-code-padding: 5px; /* 5px for 13px base, codemirror highlighting needs integer px value */ --jp-code-font-family-default: Menlo, Consolas, \"DejaVu Sans Mono\", monospace; --jp-code-font-family: var(--jp-code-font-family-default); /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-code-presentation-font-size: 16px; /* may need to tweak cursor width if you change font size */ --jp-code-cursor-width0: 1.4px; --jp-code-cursor-width1: 2px; --jp-code-cursor-width2: 4px; /* Layout * * The following are the main layout colors use in JupyterLab. In a light * theme these would go from light to dark. */ --jp-layout-color0: #111111; --jp-layout-color1: var(--md-grey-900); --jp-layout-color2: var(--md-grey-800); --jp-layout-color3: var(--md-grey-700); --jp-layout-color4: var(--md-grey-600); /* Inverse Layout * * The following are the inverse layout colors use in JupyterLab. In a light * theme these would go from dark to light. */ --jp-inverse-layout-color0: white; --jp-inverse-layout-color1: white; --jp-inverse-layout-color2: var(--md-grey-200); --jp-inverse-layout-color3: var(--md-grey-400); --jp-inverse-layout-color4: var(--md-grey-600); /* Brand/accent */ --jp-brand-color0: var(--md-blue-700); --jp-brand-color1: var(--md-blue-500); --jp-brand-color2: var(--md-blue-300); --jp-brand-color3: var(--md-blue-100); --jp-brand-color4: var(--md-blue-50); --jp-accent-color0: var(--md-green-700); --jp-accent-color1: var(--md-green-500); --jp-accent-color2: var(--md-green-300); --jp-accent-color3: var(--md-green-100); /* State colors (warn, error, success, info) */ --jp-warn-color0: var(--md-orange-700); --jp-warn-color1: var(--md-orange-500); --jp-warn-color2: var(--md-orange-300); --jp-warn-color3: var(--md-orange-100); --jp-error-color0: var(--md-red-700); --jp-error-color1: var(--md-red-500); --jp-error-color2: var(--md-red-300); --jp-error-color3: var(--md-red-100); --jp-success-color0: var(--md-green-700); --jp-success-color1: var(--md-green-500); --jp-success-color2: var(--md-green-300); --jp-success-color3: var(--md-green-100); --jp-info-color0: var(--md-cyan-700); --jp-info-color1: var(--md-cyan-500); --jp-info-color2: var(--md-cyan-300); --jp-info-color3: var(--md-cyan-100); /* Cell specific styles */ --jp-cell-padding: 5px; --jp-cell-collapser-width: 8px; --jp-cell-collapser-min-height: 20px; --jp-cell-collapser-not-active-hover-opacity: 0.6; --jp-cell-editor-background: var(--jp-layout-color1); --jp-cell-editor-border-color: var(--md-grey-700); --jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-cell-editor-active-background: var(--jp-layout-color0); --jp-cell-editor-active-border-color: var(--jp-brand-color1); --jp-cell-prompt-width: 64px; --jp-cell-prompt-font-family: var(--jp-code-font-family-default); --jp-cell-prompt-letter-spacing: 0px; --jp-cell-prompt-opacity: 1; --jp-cell-prompt-not-active-opacity: 1; --jp-cell-prompt-not-active-font-color: var(--md-grey-300); /* A custom blend of MD grey and blue 600 * See https://meyerweb.com/eric/tools/color-blend/#546E7A:1E88E5:5:hex */ --jp-cell-inprompt-font-color: #307fc1; /* A custom blend of MD grey and orange 600 * https://meyerweb.com/eric/tools/color-blend/#546E7A:F4511E:5:hex */ --jp-cell-outprompt-font-color: #bf5b3d; /* Notebook specific styles */ --jp-notebook-padding: 10px; --jp-notebook-select-background: var(--jp-layout-color1); --jp-notebook-multiselected-color: rgba(33, 150, 243, 0.24); /* The scroll padding is calculated to fill enough space at the bottom of the notebook to show one single-line cell (with appropriate padding) at the top when the notebook is scrolled all the way to the bottom. We also subtract one pixel so that no scrollbar appears if we have just one single-line cell in the notebook. This padding is to enable a 'scroll past end' feature in a notebook. */ --jp-notebook-scroll-padding: calc( 100% - var(--jp-code-font-size) * var(--jp-code-line-height) - var(--jp-code-padding) - var(--jp-cell-padding) - 1px ); /* Rendermime styles */ --jp-rendermime-error-background: rgba(244, 67, 54, 0.28); --jp-rendermime-table-row-background: var(--md-grey-900); --jp-rendermime-table-row-hover-background: rgba(3, 169, 244, 0.2); /* Dialog specific styles */ --jp-dialog-background: rgba(0, 0, 0, 0.6); /* Console specific styles */ --jp-console-padding: 10px; /* Toolbar specific styles */ --jp-toolbar-border-color: var(--jp-border-color2); --jp-toolbar-micro-height: 8px; --jp-toolbar-background: var(--jp-layout-color1); --jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, 0.8); --jp-toolbar-header-margin: 4px 4px 0px 4px; --jp-toolbar-active-background: var(--jp-layout-color0); /* Statusbar specific styles */ --jp-statusbar-height: 24px; /* Input field styles */ --jp-input-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-input-active-background: var(--jp-layout-color0); --jp-input-hover-background: var(--jp-layout-color2); --jp-input-background: var(--md-grey-800); --jp-input-border-color: var(--jp-border-color1); --jp-input-active-border-color: var(--jp-brand-color1); --jp-input-active-box-shadow-color: rgba(19, 124, 189, 0.3); /* General editor styles */ --jp-editor-selected-background: var(--jp-layout-color2); --jp-editor-selected-focused-background: rgba(33, 150, 243, 0.24); --jp-editor-cursor-color: var(--jp-ui-font-color0); /* Code mirror specific styles */ --jp-mirror-editor-keyword-color: var(--md-green-500); --jp-mirror-editor-atom-color: var(--md-blue-300); --jp-mirror-editor-number-color: var(--md-green-400); --jp-mirror-editor-def-color: var(--md-blue-600); --jp-mirror-editor-variable-color: var(--md-grey-300); --jp-mirror-editor-variable-2-color: var(--md-blue-400); --jp-mirror-editor-variable-3-color: var(--md-green-600); --jp-mirror-editor-punctuation-color: var(--md-blue-400); --jp-mirror-editor-property-color: var(--md-blue-400); --jp-mirror-editor-operator-color: #aa22ff; --jp-mirror-editor-comment-color: #408080; --jp-mirror-editor-string-color: #ff7070; --jp-mirror-editor-string-2-color: var(--md-purple-300); --jp-mirror-editor-meta-color: #aa22ff; --jp-mirror-editor-qualifier-color: #555; --jp-mirror-editor-builtin-color: var(--md-green-600); --jp-mirror-editor-bracket-color: #997; --jp-mirror-editor-tag-color: var(--md-green-700); --jp-mirror-editor-attribute-color: var(--md-blue-700); --jp-mirror-editor-header-color: var(--md-blue-500); --jp-mirror-editor-quote-color: var(--md-green-300); --jp-mirror-editor-link-color: var(--md-blue-700); --jp-mirror-editor-error-color: #f00; --jp-mirror-editor-hr-color: #999; /* Vega extension styles */ --jp-vega-background: var(--md-grey-400); /* Sidebar-related styles */ --jp-sidebar-min-width: 250px; /* Search-related styles */ --jp-search-toggle-off-opacity: 0.6; --jp-search-toggle-hover-opacity: 0.8; --jp-search-toggle-on-opacity: 1; --jp-search-selected-match-background-color: rgb(255, 225, 0); --jp-search-selected-match-color: black; --jp-search-unselected-match-background-color: var( --jp-inverse-layout-color0 ); --jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0); /* scrollbar related styles. Supports every browser except Edge. */ /* colors based on JetBrain's Darcula theme */ --jp-scrollbar-background-color: #3f4244; --jp-scrollbar-thumb-color: 88, 96, 97; /* need to specify thumb color as an RGB triplet */ --jp-scrollbar-endpad: 3px; /* the minimum gap between the thumb and the ends of a scrollbar */ /* hacks for setting the thumb shape. These do nothing in Firefox */ --jp-scrollbar-thumb-margin: 3.5px; /* the space in between the sides of the thumb and the track */ --jp-scrollbar-thumb-radius: 9px; /* set to a large-ish value for rounded endcaps on the thumb */ /* Icon colors that work well with light or dark backgrounds */ --jp-icon-contrast-color0: var(--md-purple-600); --jp-icon-contrast-color1: var(--md-green-600); --jp-icon-contrast-color2: var(--md-pink-600); --jp-icon-contrast-color3: var(--md-blue-600); } :root{--md-red-50: #ffebee;--md-red-100: #ffcdd2;--md-red-200: #ef9a9a;--md-red-300: #e57373;--md-red-400: #ef5350;--md-red-500: #f44336;--md-red-600: #e53935;--md-red-700: #d32f2f;--md-red-800: #c62828;--md-red-900: #b71c1c;--md-red-A100: #ff8a80;--md-red-A200: #ff5252;--md-red-A400: #ff1744;--md-red-A700: #d50000;--md-pink-50: #fce4ec;--md-pink-100: #f8bbd0;--md-pink-200: #f48fb1;--md-pink-300: #f06292;--md-pink-400: #ec407a;--md-pink-500: #e91e63;--md-pink-600: #d81b60;--md-pink-700: #c2185b;--md-pink-800: 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#7986cb;--md-indigo-400: #5c6bc0;--md-indigo-500: #3f51b5;--md-indigo-600: #3949ab;--md-indigo-700: #303f9f;--md-indigo-800: #283593;--md-indigo-900: #1a237e;--md-indigo-A100: #8c9eff;--md-indigo-A200: #536dfe;--md-indigo-A400: #3d5afe;--md-indigo-A700: #304ffe;--md-blue-50: #e3f2fd;--md-blue-100: #bbdefb;--md-blue-200: #90caf9;--md-blue-300: #64b5f6;--md-blue-400: #42a5f5;--md-blue-500: #2196f3;--md-blue-600: #1e88e5;--md-blue-700: #1976d2;--md-blue-800: #1565c0;--md-blue-900: #0d47a1;--md-blue-A100: #82b1ff;--md-blue-A200: #448aff;--md-blue-A400: #2979ff;--md-blue-A700: #2962ff;--md-light-blue-50: #e1f5fe;--md-light-blue-100: #b3e5fc;--md-light-blue-200: #81d4fa;--md-light-blue-300: #4fc3f7;--md-light-blue-400: #29b6f6;--md-light-blue-500: #03a9f4;--md-light-blue-600: #039be5;--md-light-blue-700: #0288d1;--md-light-blue-800: #0277bd;--md-light-blue-900: #01579b;--md-light-blue-A100: #80d8ff;--md-light-blue-A200: #40c4ff;--md-light-blue-A400: #00b0ff;--md-light-blue-A700: #0091ea;--md-cyan-50: #e0f7fa;--md-cyan-100: #b2ebf2;--md-cyan-200: #80deea;--md-cyan-300: #4dd0e1;--md-cyan-400: #26c6da;--md-cyan-500: #00bcd4;--md-cyan-600: #00acc1;--md-cyan-700: #0097a7;--md-cyan-800: #00838f;--md-cyan-900: #006064;--md-cyan-A100: #84ffff;--md-cyan-A200: #18ffff;--md-cyan-A400: #00e5ff;--md-cyan-A700: #00b8d4;--md-teal-50: #e0f2f1;--md-teal-100: #b2dfdb;--md-teal-200: #80cbc4;--md-teal-300: #4db6ac;--md-teal-400: #26a69a;--md-teal-500: #009688;--md-teal-600: #00897b;--md-teal-700: #00796b;--md-teal-800: #00695c;--md-teal-900: #004d40;--md-teal-A100: #a7ffeb;--md-teal-A200: #64ffda;--md-teal-A400: #1de9b6;--md-teal-A700: #00bfa5;--md-green-50: #e8f5e9;--md-green-100: #c8e6c9;--md-green-200: #a5d6a7;--md-green-300: #81c784;--md-green-400: #66bb6a;--md-green-500: #4caf50;--md-green-600: #43a047;--md-green-700: #388e3c;--md-green-800: #2e7d32;--md-green-900: #1b5e20;--md-green-A100: #b9f6ca;--md-green-A200: #69f0ae;--md-green-A400: #00e676;--md-green-A700: #00c853;--md-light-green-50: #f1f8e9;--md-light-green-100: #dcedc8;--md-light-green-200: #c5e1a5;--md-light-green-300: #aed581;--md-light-green-400: #9ccc65;--md-light-green-500: #8bc34a;--md-light-green-600: #7cb342;--md-light-green-700: #689f38;--md-light-green-800: #558b2f;--md-light-green-900: #33691e;--md-light-green-A100: #ccff90;--md-light-green-A200: #b2ff59;--md-light-green-A400: #76ff03;--md-light-green-A700: #64dd17;--md-lime-50: #f9fbe7;--md-lime-100: #f0f4c3;--md-lime-200: #e6ee9c;--md-lime-300: #dce775;--md-lime-400: #d4e157;--md-lime-500: #cddc39;--md-lime-600: #c0ca33;--md-lime-700: #afb42b;--md-lime-800: #9e9d24;--md-lime-900: #827717;--md-lime-A100: #f4ff81;--md-lime-A200: #eeff41;--md-lime-A400: #c6ff00;--md-lime-A700: #aeea00;--md-yellow-50: #fffde7;--md-yellow-100: #fff9c4;--md-yellow-200: #fff59d;--md-yellow-300: #fff176;--md-yellow-400: #ffee58;--md-yellow-500: #ffeb3b;--md-yellow-600: #fdd835;--md-yellow-700: #fbc02d;--md-yellow-800: #f9a825;--md-yellow-900: #f57f17;--md-yellow-A100: #ffff8d;--md-yellow-A200: #ffff00;--md-yellow-A400: #ffea00;--md-yellow-A700: #ffd600;--md-amber-50: #fff8e1;--md-amber-100: #ffecb3;--md-amber-200: #ffe082;--md-amber-300: #ffd54f;--md-amber-400: #ffca28;--md-amber-500: #ffc107;--md-amber-600: #ffb300;--md-amber-700: #ffa000;--md-amber-800: #ff8f00;--md-amber-900: #ff6f00;--md-amber-A100: #ffe57f;--md-amber-A200: #ffd740;--md-amber-A400: #ffc400;--md-amber-A700: #ffab00;--md-orange-50: #fff3e0;--md-orange-100: #ffe0b2;--md-orange-200: #ffcc80;--md-orange-300: #ffb74d;--md-orange-400: #ffa726;--md-orange-500: #ff9800;--md-orange-600: #fb8c00;--md-orange-700: #f57c00;--md-orange-800: #ef6c00;--md-orange-900: #e65100;--md-orange-A100: #ffd180;--md-orange-A200: #ffab40;--md-orange-A400: #ff9100;--md-orange-A700: #ff6d00;--md-deep-orange-50: #fbe9e7;--md-deep-orange-100: #ffccbc;--md-deep-orange-200: #ffab91;--md-deep-orange-300: #ff8a65;--md-deep-orange-400: #ff7043;--md-deep-orange-500: 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sourceMappingURL=mkdocs-jupyter.css.map*/ init_mathjax = function() { if (window.MathJax) { // MathJax loaded MathJax.Hub.Config({ TeX: { equationNumbers: { autoNumber: \"AMS\", useLabelIds: true } }, tex2jax: { inlineMath: [ ['$','$'], [\"\\\\(\",\"\\\\)\"] ], displayMath: [ ['$$','$$'], [\"\\\\[\",\"\\\\]\"] ], processEscapes: true, processEnvironments: true }, displayAlign: 'center', CommonHTML: { linebreaks: { automatic: true } } }); MathJax.Hub.Queue([\"Typeset\", MathJax.Hub]); } } init_mathjax(); Raw data alignment This tutorial will cover the steps for performing the alignment of raw RNA- and HiFi-sequencing data. You will need to use the software IGV on your computer to visualize some of the output files, which can be easily downloaded once they are produced. At the end of this tutorial you will be able to: perform and discuss quality control on raw data in fastq format using FastQC and MultiQC align HiFi and RNA sequencing data with dedicated tools such as MiniMap2 and STAR analyze the quality the alignment with qualimap The output of this notebook will be used for the Variant calling analysis and the bulk RNA-sequencing analysis. If you do not want to run this notebook, you can alternatively use the free interactive tool Galaxy to perform the alignment steps. We have uploaded the data on Galaxy, and the manual to perform the exercise is found at the course webpage . The present tutorial, like the rest of the course material, is available at our open-source github repository . A few introductory points to run this notebook (click to show): To use this notebook, use the NGS (python) kernel that contains the packages. Choose it by selecting Kernel -> Change Kernel in the menu on top of the window. In this notebook you will use only bash commands as you would do in the command line (this is why you read %%bash at the beginning of each piece of code). Those commands can be replicated in the command line, but we thought of integrating them in a notebook to make the tutorial understandable. The bash commands can also be marked with an ! sign at the beginning of the line On some computers, you might see the result of the commands once they are done running. This means you will wait some time while the computer is crunching, and only afterwards you will see the result of the command you have executed You can run the code in each cell by clicking on the run cell button, or by pressing Shift + Enter . When the code is done running, a small green check sign will appear on the left side You need to run the cells in sequential order, please do not run a cell until the one above finished running and do not skip any cells Each cell contains a short description of the code and the output you should get. Please try not to focus on understanding the code for each command in too much detail, but rather try to focus on the output You can create new code cells by pressing + in the Menu bar above. Biological background \u00b6 White clover (Trifolium repens) is an allotetraploid. It is a relatively young, outcrossing species, which originated during the most recent glaciation around 20,000 years ago by hybridisation of two diploid species, T. occidentale and T. pallescens (see figure below). This means that it contains genomes originating from two different species within the same nucleus. Normally, white clover is an outbreeding species, but a self-compatible line was used for sequencing the white clover genome (Griffiths et al, 2019) . This line will be designated as S10 in the data, indicating that this is the 10th self-fertilized generation. In addition, we have data from a wild clover accession (ecotype) called Tienshan ( Ti ), which is collected from the Chinese mountains and is adapted to alpine conditions. We will perform alignment of the data to the white clover's reference genome containing both T. occidentale and T. pallescens (called contig 1 and contig 2 in the data). We will also perform alignment to each subgenome, and see which are the differences with the quality control tools. Quality control and mapping \u00b6 Quality Control \u00b6 We run FastQC on the PacBio Hifi reads and on two of the Illumina RNA-seq libraries. FastQC does quality control of the raw sequence data, providing an overview of the data which can help identify if there are any problems that should be addressed before further analysis. You can find the report for each file into the folder results/fastqc_output/ . The output is in HTML format and can be opened in any browser or in jupyterlab . It is however not easy to compare the various libraries by opening separate reports. To aggregate all the results, we apply the MultiQC software to the reports' folder. The output of MultiQC is in the directory results/multiqc_output/fastqc_data . In [1]: Copied! %% bash #run fastqc mkdir - p results / fastqc_output fastqc - q - o results / fastqc_output ../ Data / Clover_Data /*. fastq > / dev / null 2 >& 1 %%bash #run fastqc mkdir -p results/fastqc_output fastqc -q -o results/fastqc_output ../Data/Clover_Data/*.fastq > /dev/null 2>&1 Note: fastqc prints a lot of output conisting of a simple confirmation of execution without error, even when using the option -q , which means quiet . Therefore we added > /dev/null 2>&1 to the command to mute the printing of that output. In [2]: Copied! %% bash #run multiqc multiqc -- outdir results / multiqc_output / fastqc_data results / fastqc_output %%bash #run multiqc multiqc --outdir results/multiqc_output/fastqc_data results/fastqc_output / / / \u001b]8;id=903972;https://multiqc.info\u001b\\ MultiQC \u001b]8;;\u001b\\ \ud83d\udd0d | v1.14 | multiqc | Search path : /work/SamueleSoraggi/Notebooks/results/fastqc_output | searching | \u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501 100% 50/50 .html | fastqc | Found 25 reports | multiqc | Compressing plot data | multiqc | Report : results/multiqc_output/fastqc_data/multiqc_report.html | multiqc | Data : results/multiqc_output/fastqc_data/multiqc_data | multiqc | MultiQC complete Questions Visualize the Webpage generated by MultiQC. Hint: You can find a Help button that offers additional information about the plots for each panel. Focus on the following panels: \u201cPer base sequence quality\u201d, \u201cPer sequence quality scores\u201d.... (\u201cPer base sequence content\u201d always gives a FAIL for RNA-seq data). What do you notice with respect to the sequence quality scores? Are there any other quality issues worth noting? Hifi data mapping \u00b6 We map the PacBio Hifi reads ( Hifi_reads_white_clover.fastq ) to the white clover reference sequence (Contig1&2) using minimap2 . We run two mapping rounds, using two different preset options ( -x in the command) for the technology: PacBio/Oxford Nanopore read to reference mapping: map-pb Long assembly to reference mapping. Divergence is below 20%\u201d settings asm20 . Next, we create reports of the mapping results by running QualiMap on the two obtained SAM files. We first need to index the reference fasta files using samtools faidx . This produces files in .fai format containing informations about length of the reference sequence, offset for the quality scores, name of the reference sequence. Click here for a detailed overview. In [3]: Copied! %% bash #copy the reference data in the folder reference_data, so that you can write the indexing files mkdir - p reference_data cp ../ Data / Clover_Data / DNA_Contig1_2 . fasta ../ Data / Clover_Data / DNA_Contig1 . fasta ../ Data / Clover_Data / DNA_Contig2 . fasta reference_data %%bash #copy the reference data in the folder reference_data, so that you can write the indexing files mkdir -p reference_data cp ../Data/Clover_Data/DNA_Contig1_2.fasta ../Data/Clover_Data/DNA_Contig1.fasta ../Data/Clover_Data/DNA_Contig2.fasta reference_data In [4]: Copied! %% bash samtools faidx reference_data / DNA_Contig1_2 . fasta samtools faidx reference_data / DNA_Contig1 . fasta samtools faidx reference_data / DNA_Contig2 . fasta %%bash samtools faidx reference_data/DNA_Contig1_2.fasta samtools faidx reference_data/DNA_Contig1.fasta samtools faidx reference_data/DNA_Contig2.fasta we create an output folder for the HIFI alignment, and run minimap2 with the settings explained before. In [5]: Copied! %% bash mkdir - p results / HIFI_alignment / minimap2 - a - x map - pb - o results / HIFI_alignment / PacBio_clover_alignment_1_2_mappb . sam \\ reference_data / DNA_Contig1_2 . fasta \\ ../ Data / Clover_Data / Hifi_reads_white_clover . fastq minimap2 - a - x asm20 - o results / HIFI_alignment / PacBio_clover_alignment_1_2_asm20 . sam \\ reference_data / DNA_Contig1_2 . fasta \\ ../ Data / Clover_Data / Hifi_reads_white_clover . fastq %%bash mkdir -p results/HIFI_alignment/ minimap2 -a -x map-pb -o results/HIFI_alignment/PacBio_clover_alignment_1_2_mappb.sam \\ reference_data/DNA_Contig1_2.fasta \\ ../Data/Clover_Data/Hifi_reads_white_clover.fastq minimap2 -a -x asm20 -o results/HIFI_alignment/PacBio_clover_alignment_1_2_asm20.sam \\ reference_data/DNA_Contig1_2.fasta \\ ../Data/Clover_Data/Hifi_reads_white_clover.fastq [M::mm_idx_gen::0.075*1.04] collected minimizers [M::mm_idx_gen::0.099*1.52] sorted minimizers [M::main::0.099*1.52] loaded/built the index for 2 target sequence(s) [M::mm_mapopt_update::0.114*1.45] mid_occ = 11 [M::mm_idx_stat] kmer size: 19; skip: 10; is_hpc: 1; #seq: 2 [M::mm_idx_stat::0.118*1.43] distinct minimizers: 203943 (79.05% are singletons); average occurrences: 1.273; average spacing: 8.047; total length: 2089554 [M::worker_pipeline::11.859*2.89] mapped 4395 sequences [M::main] Version: 2.24-r1122 [M::main] CMD: minimap2 -a -x map-pb -o results/HIFI_alignment/PacBio_clover_alignment_1_2_mappb.sam reference_data/DNA_Contig1_2.fasta ../Data/Clover_Data/Hifi_reads_white_clover.fastq [M::main] Real time: 11.873 sec; CPU: 34.246 sec; Peak RSS: 1.186 GB [M::mm_idx_gen::0.084*1.04] collected minimizers [M::mm_idx_gen::0.121*1.62] sorted minimizers [M::main::0.121*1.62] loaded/built the index for 2 target sequence(s) [M::mm_mapopt_update::0.138*1.54] mid_occ = 50 [M::mm_idx_stat] kmer size: 19; skip: 10; is_hpc: 0; #seq: 2 [M::mm_idx_stat::0.143*1.52] distinct minimizers: 298340 (78.21% are singletons); average occurrences: 1.277; average spacing: 5.484; total length: 2089554 [M::worker_pipeline::18.366*2.88] mapped 4395 sequences [M::main] Version: 2.24-r1122 [M::main] CMD: minimap2 -a -x asm20 -o results/HIFI_alignment/PacBio_clover_alignment_1_2_asm20.sam reference_data/DNA_Contig1_2.fasta ../Data/Clover_Data/Hifi_reads_white_clover.fastq [M::main] Real time: 18.394 sec; CPU: 52.932 sec; Peak RSS: 1.692 GB samtools sort is used to sort the alignment with left-to-right coordinates. The output is in .bam format, with .sam files in input (Note that you could have gotten .bam files from minimap2 with a specific option). In [6]: Copied! %% bash samtools sort results / HIFI_alignment / PacBio_clover_alignment_1_2_mappb . sam \\ - o results / HIFI_alignment / PacBio_clover_alignment_1_2_mappb . sort . bam samtools sort results / HIFI_alignment / PacBio_clover_alignment_1_2_asm20 . sam \\ - o results / HIFI_alignment / PacBio_clover_alignment_1_2_asm20 . sort . bam %%bash samtools sort results/HIFI_alignment/PacBio_clover_alignment_1_2_mappb.sam \\ -o results/HIFI_alignment/PacBio_clover_alignment_1_2_mappb.sort.bam samtools sort results/HIFI_alignment/PacBio_clover_alignment_1_2_asm20.sam \\ -o results/HIFI_alignment/PacBio_clover_alignment_1_2_asm20.sort.bam samtools index creates the index for the bam file, stored in .bai format. The index file lets programs access any position into the aligned data without reading the whole file, which would take too much time. In [7]: Copied! %% bash samtools index results / HIFI_alignment / PacBio_clover_alignment_1_2_mappb . sort . bam samtools index results / HIFI_alignment / PacBio_clover_alignment_1_2_asm20 . sort . bam %%bash samtools index results/HIFI_alignment/PacBio_clover_alignment_1_2_mappb.sort.bam samtools index results/HIFI_alignment/PacBio_clover_alignment_1_2_asm20.sort.bam Run quality control on both files In [8]: Copied! %% bash qualimap bamqc - bam results / HIFI_alignment / PacBio_clover_alignment_1_2_mappb . sort . bam \\ - outdir results / qualimap_output / PacBio_clover_alignment_1_2_mappb qualimap bamqc - bam results / HIFI_alignment / PacBio_clover_alignment_1_2_asm20 . sort . bam \\ - outdir results / qualimap_output / PacBio_clover_alignment_1_2_asm20 %%bash qualimap bamqc -bam results/HIFI_alignment/PacBio_clover_alignment_1_2_mappb.sort.bam \\ -outdir results/qualimap_output/PacBio_clover_alignment_1_2_mappb qualimap bamqc -bam results/HIFI_alignment/PacBio_clover_alignment_1_2_asm20.sort.bam \\ -outdir results/qualimap_output/PacBio_clover_alignment_1_2_asm20 Java memory size is set to 1200M Launching application... QualiMap v.2.2.2-dev Built on 2019-11-11 14:05 Selected tool: bamqc Available memory (Mb): 33 Max memory (Mb): 1258 Starting bam qc.... Loading sam header... Loading locator... Loading reference... Number of windows: 400, effective number of windows: 401 Chunk of reads size: 1000 Number of threads: 8 Processed 50 out of 401 windows... Processed 100 out of 401 windows... Processed 150 out of 401 windows... Processed 200 out of 401 windows... Processed 250 out of 401 windows... Processed 300 out of 401 windows... Processed 350 out of 401 windows... Processed 400 out of 401 windows... Total processed windows:401 Number of reads: 4395 Number of valid reads: 4696 Number of correct strand reads:0 Inside of regions... Num mapped reads: 4395 Num mapped first of pair: 0 Num mapped second of pair: 0 Num singletons: 0 Time taken to analyze reads: 12 Computing descriptors... numberOfMappedBases: 70274383 referenceSize: 2089554 numberOfSequencedBases: 70039397 numberOfAs: 23621784 Computing per chromosome statistics... Computing histograms... Overall analysis time: 12 end of bam qc Computing report... Writing HTML report... HTML report created successfully Finished Java memory size is set to 1200M Launching application... QualiMap v.2.2.2-dev Built on 2019-11-11 14:05 Selected tool: bamqc Available memory (Mb): 33 Max memory (Mb): 1258 Starting bam qc.... Loading sam header... Loading locator... Loading reference... Number of windows: 400, effective number of windows: 401 Chunk of reads size: 1000 Number of threads: 8 Processed 50 out of 401 windows... Processed 100 out of 401 windows... Processed 150 out of 401 windows... Processed 200 out of 401 windows... Processed 250 out of 401 windows... Processed 300 out of 401 windows... Processed 350 out of 401 windows... Processed 400 out of 401 windows... Total processed windows:401 Number of reads: 4395 Number of valid reads: 4747 Number of correct strand reads:0 Inside of regions... Num mapped reads: 4395 Num mapped first of pair: 0 Num mapped second of pair: 0 Num singletons: 0 Time taken to analyze reads: 12 Computing descriptors... numberOfMappedBases: 69947563 referenceSize: 2089554 numberOfSequencedBases: 69812688 numberOfAs: 23541478 Computing per chromosome statistics... Computing histograms... Overall analysis time: 12 end of bam qc Computing report... Writing HTML report... HTML report created successfully Finished For easier comparison, we can again collapse the two reports into a single one using MultiQC , in the same way we did for putting together the other reports from fastQC . In [9]: Copied! %% bash #run multiqc multiqc -- outdir results / qualimap_output results / qualimap_output %%bash #run multiqc multiqc --outdir results/qualimap_output results/qualimap_output / / / \u001b]8;id=941002;https://multiqc.info\u001b\\ MultiQC \u001b]8;;\u001b\\ \ud83d\udd0d | v1.14 | multiqc | Search path : /work/SamueleSoraggi/Notebooks/results/qualimap_output | searching | \u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501 100% 93/93 nt.t\u2026 | qualimap | Found 2 BamQC reports | multiqc | Compressing plot data | multiqc | Report : results/qualimap_output/multiqc_report.html | multiqc | Data : results/qualimap_output/multiqc_data | multiqc | MultiQC complete Now you can visualize the report generated, which is in results/qualimap_output/multiqc_report.html . Next, we map the white clover PacBio Hifi reads to contig1 and contig2 separately, using the setting you selected at the previous step (let's say map-pb was chosen, but you are free to change this setting in the commands). As the two contigs represent the two white clover subgenomes, this mapping will allow you to see the two subgenome haplotypes and call subgenome SNPs. In [10]: Copied! %% bash minimap2 - a - x map - pb - o results / HIFI_alignment / PacBio_clover_alignment_1 . sam \\ reference_data / DNA_Contig1 . fasta \\ ../ Data / Clover_Data / Hifi_reads_white_clover . fastq %%bash minimap2 -a -x map-pb -o results/HIFI_alignment/PacBio_clover_alignment_1.sam \\ reference_data/DNA_Contig1.fasta \\ ../Data/Clover_Data/Hifi_reads_white_clover.fastq [M::mm_idx_gen::0.050*0.96] collected minimizers [M::mm_idx_gen::0.071*1.55] sorted minimizers [M::main::0.071*1.55] loaded/built the index for 1 target sequence(s) [M::mm_mapopt_update::0.079*1.50] mid_occ = 10 [M::mm_idx_stat] kmer size: 19; skip: 10; is_hpc: 1; #seq: 1 [M::mm_idx_stat::0.082*1.48] distinct minimizers: 112547 (91.45% are singletons); average occurrences: 1.141; average spacing: 8.031; total length: 1031631 [M::worker_pipeline::27.024*2.90] mapped 4395 sequences [M::main] Version: 2.24-r1122 [M::main] CMD: minimap2 -a -x map-pb -o results/HIFI_alignment/PacBio_clover_alignment_1.sam reference_data/DNA_Contig1.fasta ../Data/Clover_Data/Hifi_reads_white_clover.fastq [M::main] Real time: 27.035 sec; CPU: 78.431 sec; Peak RSS: 2.258 GB In [11]: Copied! %% bash minimap2 - a - x map - pb - o results / HIFI_alignment / PacBio_clover_alignment_2 . sam \\ reference_data / DNA_Contig2 . fasta \\ ../ Data / Clover_Data / Hifi_reads_white_clover . fastq %%bash minimap2 -a -x map-pb -o results/HIFI_alignment/PacBio_clover_alignment_2.sam \\ reference_data/DNA_Contig2.fasta \\ ../Data/Clover_Data/Hifi_reads_white_clover.fastq [M::mm_idx_gen::0.048*1.04] collected minimizers [M::mm_idx_gen::0.069*1.61] sorted minimizers [M::main::0.069*1.61] loaded/built the index for 1 target sequence(s) [M::mm_mapopt_update::0.076*1.55] mid_occ = 10 [M::mm_idx_stat] kmer size: 19; skip: 10; is_hpc: 1; #seq: 1 [M::mm_idx_stat::0.080*1.53] distinct minimizers: 120722 (93.18% are singletons); average occurrences: 1.087; average spacing: 8.062; total length: 1057923 [M::worker_pipeline::27.150*2.94] mapped 4395 sequences [M::main] Version: 2.24-r1122 [M::main] CMD: minimap2 -a -x map-pb -o results/HIFI_alignment/PacBio_clover_alignment_2.sam reference_data/DNA_Contig2.fasta ../Data/Clover_Data/Hifi_reads_white_clover.fastq [M::main] Real time: 27.162 sec; CPU: 79.819 sec; Peak RSS: 2.058 GB Sort the bam files and create their index using samtools In [12]: Copied! %% bash samtools sort results / HIFI_alignment / PacBio_clover_alignment_1 . sam - o results / HIFI_alignment / PacBio_clover_alignment_1 . sort . bam samtools sort results / HIFI_alignment / PacBio_clover_alignment_2 . sam - o results / HIFI_alignment / PacBio_clover_alignment_2 . sort . bam %%bash samtools sort results/HIFI_alignment/PacBio_clover_alignment_1.sam -o results/HIFI_alignment/PacBio_clover_alignment_1.sort.bam samtools sort results/HIFI_alignment/PacBio_clover_alignment_2.sam -o results/HIFI_alignment/PacBio_clover_alignment_2.sort.bam In [13]: Copied! %% bash samtools index results / HIFI_alignment / PacBio_clover_alignment_1 . sort . bam samtools index results / HIFI_alignment / PacBio_clover_alignment_2 . sort . bam %%bash samtools index results/HIFI_alignment/PacBio_clover_alignment_1.sort.bam samtools index results/HIFI_alignment/PacBio_clover_alignment_2.sort.bam Perform quality control In [14]: Copied! %% bash mkdir - p results / qualimap_output qualimap bamqc - bam results / HIFI_alignment / PacBio_clover_alignment_1 . sort . bam - outdir results / qualimap_output / PacBio_clover_alignment_1 %%bash mkdir -p results/qualimap_output qualimap bamqc -bam results/HIFI_alignment/PacBio_clover_alignment_1.sort.bam -outdir results/qualimap_output/PacBio_clover_alignment_1 Java memory size is set to 1200M Launching application... QualiMap v.2.2.2-dev Built on 2019-11-11 14:05 Selected tool: bamqc Available memory (Mb): 33 Max memory (Mb): 1258 Starting bam qc.... Loading sam header... Loading locator... Loading reference... Number of windows: 400, effective number of windows: 400 Chunk of reads size: 1000 Number of threads: 8 Processed 50 out of 400 windows... Processed 100 out of 400 windows... Processed 150 out of 400 windows... Processed 200 out of 400 windows... Processed 250 out of 400 windows... Processed 300 out of 400 windows... Processed 350 out of 400 windows... Processed 400 out of 400 windows... Total processed windows:400 Number of reads: 4395 Number of valid reads: 9070 Number of correct strand reads:0 Inside of regions... Num mapped reads: 4356 Num mapped first of pair: 0 Num mapped second of pair: 0 Num singletons: 0 Time taken to analyze reads: 10 Computing descriptors... numberOfMappedBases: 59058058 referenceSize: 1031631 numberOfSequencedBases: 54917933 numberOfAs: 18508479 Computing per chromosome statistics... Computing histograms... Overall analysis time: 11 end of bam qc Computing report... Writing HTML report... HTML report created successfully Finished In [15]: Copied! %% bash qualimap bamqc - bam results / HIFI_alignment / PacBio_clover_alignment_2 . sort . bam - outdir results / qualimap_output / PacBio_clover_alignment_2 %%bash qualimap bamqc -bam results/HIFI_alignment/PacBio_clover_alignment_2.sort.bam -outdir results/qualimap_output/PacBio_clover_alignment_2 Java memory size is set to 1200M Launching application... QualiMap v.2.2.2-dev Built on 2019-11-11 14:05 Selected tool: bamqc Available memory (Mb): 33 Max memory (Mb): 1258 Starting bam qc.... Loading sam header... Loading locator... Loading reference... Number of windows: 400, effective number of windows: 400 Chunk of reads size: 1000 Number of threads: 8 Processed 50 out of 400 windows... Processed 100 out of 400 windows... Processed 150 out of 400 windows... Processed 200 out of 400 windows... Processed 250 out of 400 windows... Processed 300 out of 400 windows... Processed 350 out of 400 windows... Processed 400 out of 400 windows... Total processed windows:400 Number of reads: 4395 Number of valid reads: 9462 Number of correct strand reads:0 Inside of regions... Num mapped reads: 4394 Num mapped first of pair: 0 Num mapped second of pair: 0 Num singletons: 0 Time taken to analyze reads: 13 Computing descriptors... numberOfMappedBases: 59388453 referenceSize: 1057923 numberOfSequencedBases: 55867300 numberOfAs: 18822184 Computing per chromosome statistics... Computing histograms... Overall analysis time: 13 end of bam qc Computing report... Writing HTML report... HTML report created successfully Finished In [16]: Copied! %% bash #run multiqc multiqc -- outdir results / qualimap_output results / qualimap_output %%bash #run multiqc multiqc --outdir results/qualimap_output results/qualimap_output / / / \u001b]8;id=999624;https://multiqc.info\u001b\\ MultiQC \u001b]8;;\u001b\\ \ud83d\udd0d | v1.14 | multiqc | Search path : /work/SamueleSoraggi/Notebooks/results/qualimap_output | searching | \u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501 100% 187/187 .t\u2026 | qualimap | Found 4 BamQC reports | multiqc | Compressing plot data | multiqc | Previous MultiQC output found! Adjusting filenames.. | multiqc | Use -f or --force to overwrite existing reports instead | multiqc | Report : results/qualimap_output/multiqc_report_1.html | multiqc | Data : results/qualimap_output/multiqc_data_1 | multiqc | MultiQC complete Task: IGV visualization and Questions Now you can inspect the alignment files in IGV . First, you will need to download the reference fasta sequence in ../Data/Clover_Data/DNA_Contig1_2.fasta and import it into IGV. You can do the same for the files DNA_Contig1.fasta and DNA_Contig2.fasta that you might need later. In IGV, this is done with the menu Genomes --> Load Genome from file menu and by selecting the relevant fasta file. Then, choose the reference you need from the drop-down menu (see figure below). You will not yet see much, but you can choose one of the two subgenomes (contig 1 or 2) and double click on a chromosome position to inspect the reference sequence. The next step will visualize the mapped files on IGV. Each mapped genome can be seen in IGV against the reference file of choice. To load an aligned file, first download it together with the index file in .bai format. For example, you need to download both results/HIFI_alignment/PacBio_clover_alignment_1.sort.bam and results/HIFI_alignment/PacBio_clover_alignment_1.sort.bam.bai to see this alignment (you need to open only the .bam file with IGV). If you open more files, their alignments will be distributed in the IGV interface, and you can change the size of each visualization yourself (below shown with only one opened alignment). Now compare in IGV the two bam files PacBio_clover_alignment_1.sort.bam and PacBio_clover_alignment_2.sort.bam . What do you observe when comparing the two BAM files? Have a look at the polymorphic regions in IGV. Are they true polymorphisms? Add to the visualization the third alignment PacBio_clover_alignment_1_2_mappb.sort.bam in IGV. Why do you see fluctuations in coverage and large regions without any apparent subgenome SNPs? What are the major differences between the stats for the reads mapped to Contigs1&2 versus contig1 and contig2? What is your interpretation of the differences? RNA-seq mapping \u00b6 In the ../Data folder you will find 24 RNA-seq libraries, 12 S10 libraries and 12 Tienshan libraries. Each library is paired-end, which is denoted by R1 and R2 at the end of two files having the same name, such as S10_1_1.R1.fastq and S10_1_1.R2.fastq . We will align each library separately and then merge the alignments to create two final samples for S10 and Tienshan . First, we need to create a genome file for the reference fasta file of contigs 1 and 2 . This is done with STAR , using the option --runMode genomeGenerate . We also need to convert the gene annotation from gff to gtf format with gffread to allow counting gene transcripts. STAR is a very complex tool with many options, so it is always useful to have a reference manual . In [17]: Copied! %% bash gffread - T - o reference_data / white_clover_genes . gtf ../ Data / Clover_Data / white_clover_genes . gff %%bash gffread -T -o reference_data/white_clover_genes.gtf ../Data/Clover_Data/white_clover_genes.gff In [18]: Copied! %% bash STAR -- runThreadN 8 \\ -- runMode genomeGenerate \\ -- genomeDir results / STAR_output / indexing_contigs_1_2 \\ -- genomeFastaFiles reference_data / DNA_Contig1_2 . fasta \\ -- sjdbGTFfile reference_data / white_clover_genes . gtf %%bash STAR --runThreadN 8 \\ --runMode genomeGenerate \\ --genomeDir results/STAR_output/indexing_contigs_1_2 \\ --genomeFastaFiles reference_data/DNA_Contig1_2.fasta \\ --sjdbGTFfile reference_data/white_clover_genes.gtf STAR --runThreadN 8 --runMode genomeGenerate --genomeDir results/STAR_output/indexing_contigs_1_2 --genomeFastaFiles reference_data/DNA_Contig1_2.fasta --sjdbGTFfile reference_data/white_clover_genes.gtf STAR version: 2.7.10a compiled: 2022-01-14T18:50:00-05:00 :/home/dobin/data/STAR/STARcode/STAR.master/source Jun 19 10:23:03 ..... started STAR run Jun 19 10:23:03 ... starting to generate Genome files Jun 19 10:23:03 ..... processing annotations GTF !!!!! WARNING: --genomeSAindexNbases 14 is too large for the genome size=2089554, which may cause seg-fault at the mapping step. Re-run genome generation with recommended --genomeSAindexNbases 9 Jun 19 10:23:03 ... starting to sort Suffix Array. This may take a long time... Jun 19 10:23:03 ... sorting Suffix Array chunks and saving them to disk... Jun 19 10:23:04 ... loading chunks from disk, packing SA... Jun 19 10:23:04 ... finished generating suffix array Jun 19 10:23:04 ... generating Suffix Array index Jun 19 10:23:08 ... completed Suffix Array index Jun 19 10:23:08 ..... inserting junctions into the genome indices Jun 19 10:23:16 ... writing Genome to disk ... Jun 19 10:23:16 ... writing Suffix Array to disk ... Jun 19 10:23:17 ... writing SAindex to disk Jun 19 10:23:18 ..... finished successfully We got a warning saying !!!!! WARNING: --genomeSAindexNbases 14 is too large for the genome size=2089554, which may cause seg-fault at the mapping step. Re-run genome generation with recommended --genomeSAindexNbases 9 meaning we need shorter strings of bases (9 bases instead of 14) to be indexed, as our reference genome is very short, and too long strings would cause many alignment errors. So we rerun the command with the suggested option. In [19]: Copied! %% bash STAR -- runThreadN 8 \\ -- runMode genomeGenerate \\ -- genomeDir results / STAR_output / indexing_contigs_1_2 \\ -- genomeFastaFiles reference_data / DNA_Contig1_2 . fasta \\ -- sjdbGTFfile reference_data / white_clover_genes . gtf \\ -- genomeSAindexNbases 9 %%bash STAR --runThreadN 8 \\ --runMode genomeGenerate \\ --genomeDir results/STAR_output/indexing_contigs_1_2 \\ --genomeFastaFiles reference_data/DNA_Contig1_2.fasta \\ --sjdbGTFfile reference_data/white_clover_genes.gtf \\ --genomeSAindexNbases 9 STAR --runThreadN 8 --runMode genomeGenerate --genomeDir results/STAR_output/indexing_contigs_1_2 --genomeFastaFiles reference_data/DNA_Contig1_2.fasta --sjdbGTFfile reference_data/white_clover_genes.gtf --genomeSAindexNbases 9 STAR version: 2.7.10a compiled: 2022-01-14T18:50:00-05:00 :/home/dobin/data/STAR/STARcode/STAR.master/source Jun 19 10:23:18 ..... started STAR run Jun 19 10:23:18 ... starting to generate Genome files Jun 19 10:23:18 ..... processing annotations GTF Jun 19 10:23:20 ... starting to sort Suffix Array. This may take a long time... Jun 19 10:23:20 ... sorting Suffix Array chunks and saving them to disk... Jun 19 10:23:21 ... loading chunks from disk, packing SA... Jun 19 10:23:21 ... finished generating suffix array Jun 19 10:23:21 ... generating Suffix Array index Jun 19 10:23:21 ... completed Suffix Array index Jun 19 10:23:22 ..... inserting junctions into the genome indices Jun 19 10:23:23 ... writing Genome to disk ... Jun 19 10:23:23 ... writing Suffix Array to disk ... Jun 19 10:23:23 ... writing SAindex to disk Jun 19 10:23:24 ..... finished successfully We use again STAR to align every single library for S10 . We extract the library name of each file and run STAR through each pair of files. Note that plant introns are very rarely more than 5000 bp and that you are mapping to two homoeologous contigs that show high similarity, especially in genic regions. We set the maximum size to 5000 using --alignIntronMax 5000 . In [20]: Copied! %% bash for i in ` ls ../ Data / Clover_Data / S10 *. R1 . fastq ` do PREFIXNAME = ` basename $ i . R1 . fastq ` echo \"###############################################\" echo \"##### ALIGNING PAIRED-END READS \" $ PREFIXNAME echo \"###############################################\" STAR -- genomeDir results / STAR_output / indexing_contigs_1_2 / \\ -- runThreadN 8 \\ -- runMode alignReads \\ -- readFilesIn ../ Data / Clover_Data / $ PREFIXNAME . R1 . fastq ../ Data / Clover_Data / $ PREFIXNAME . R2 . fastq \\ -- outFileNamePrefix results / STAR_output / S10_align_contigs_1_2 / $ PREFIXNAME \\ -- outSAMtype BAM SortedByCoordinate \\ -- outSAMattributes Standard \\ -- quantMode GeneCounts \\ -- alignIntronMax 5000 done %%bash for i in `ls ../Data/Clover_Data/S10*.R1.fastq` do PREFIXNAME=`basename $i .R1.fastq` echo \"###############################################\" echo \"##### ALIGNING PAIRED-END READS \"$PREFIXNAME echo \"###############################################\" STAR --genomeDir results/STAR_output/indexing_contigs_1_2/ \\ --runThreadN 8 \\ --runMode alignReads \\ --readFilesIn ../Data/Clover_Data/$PREFIXNAME.R1.fastq ../Data/Clover_Data/$PREFIXNAME.R2.fastq \\ --outFileNamePrefix results/STAR_output/S10_align_contigs_1_2/$PREFIXNAME \\ --outSAMtype BAM SortedByCoordinate \\ --outSAMattributes Standard \\ --quantMode GeneCounts \\ --alignIntronMax 5000 done ############################################### ##### ALIGNING PAIRED-END READS S10_1_1 ############################################### STAR --genomeDir results/STAR_output/indexing_contigs_1_2/ --runThreadN 8 --runMode alignReads --readFilesIn ../Data/Clover_Data/S10_1_1.R1.fastq ../Data/Clover_Data/S10_1_1.R2.fastq --outFileNamePrefix results/STAR_output/S10_align_contigs_1_2/S10_1_1 --outSAMtype BAM SortedByCoordinate --outSAMattributes Standard --quantMode GeneCounts --alignIntronMax 5000 STAR version: 2.7.10a compiled: 2022-01-14T18:50:00-05:00 :/home/dobin/data/STAR/STARcode/STAR.master/source Jun 19 10:23:24 ..... started STAR run Jun 19 10:23:24 ..... loading genome Jun 19 10:23:24 ..... started mapping Jun 19 10:23:35 ..... finished mapping Jun 19 10:23:35 ..... started sorting BAM Jun 19 10:23:37 ..... finished successfully ############################################### ##### ALIGNING PAIRED-END READS S10_1_2 ############################################### STAR --genomeDir results/STAR_output/indexing_contigs_1_2/ --runThreadN 8 --runMode alignReads --readFilesIn ../Data/Clover_Data/S10_1_2.R1.fastq ../Data/Clover_Data/S10_1_2.R2.fastq --outFileNamePrefix results/STAR_output/S10_align_contigs_1_2/S10_1_2 --outSAMtype BAM SortedByCoordinate --outSAMattributes Standard --quantMode GeneCounts --alignIntronMax 5000 STAR version: 2.7.10a compiled: 2022-01-14T18:50:00-05:00 :/home/dobin/data/STAR/STARcode/STAR.master/source Jun 19 10:23:38 ..... started STAR run Jun 19 10:23:38 ..... loading genome Jun 19 10:23:38 ..... started mapping Jun 19 10:23:48 ..... finished mapping Jun 19 10:23:48 ..... started sorting BAM Jun 19 10:23:51 ..... finished successfully ############################################### ##### ALIGNING PAIRED-END READS S10_1_3 ############################################### STAR --genomeDir results/STAR_output/indexing_contigs_1_2/ --runThreadN 8 --runMode alignReads --readFilesIn ../Data/Clover_Data/S10_1_3.R1.fastq ../Data/Clover_Data/S10_1_3.R2.fastq --outFileNamePrefix results/STAR_output/S10_align_contigs_1_2/S10_1_3 --outSAMtype BAM SortedByCoordinate --outSAMattributes Standard --quantMode GeneCounts --alignIntronMax 5000 STAR version: 2.7.10a compiled: 2022-01-14T18:50:00-05:00 :/home/dobin/data/STAR/STARcode/STAR.master/source Jun 19 10:23:52 ..... started STAR run Jun 19 10:23:52 ..... loading genome Jun 19 10:23:52 ..... started mapping Jun 19 10:24:03 ..... finished mapping Jun 19 10:24:03 ..... started sorting BAM Jun 19 10:24:06 ..... finished successfully ############################################### ##### ALIGNING PAIRED-END READS S10_2_1 ############################################### STAR --genomeDir results/STAR_output/indexing_contigs_1_2/ --runThreadN 8 --runMode alignReads --readFilesIn ../Data/Clover_Data/S10_2_1.R1.fastq ../Data/Clover_Data/S10_2_1.R2.fastq --outFileNamePrefix results/STAR_output/S10_align_contigs_1_2/S10_2_1 --outSAMtype BAM SortedByCoordinate --outSAMattributes Standard --quantMode GeneCounts --alignIntronMax 5000 STAR version: 2.7.10a compiled: 2022-01-14T18:50:00-05:00 :/home/dobin/data/STAR/STARcode/STAR.master/source Jun 19 10:24:06 ..... started STAR run Jun 19 10:24:06 ..... loading genome Jun 19 10:24:06 ..... started mapping Jun 19 10:24:16 ..... finished mapping Jun 19 10:24:16 ..... started sorting BAM Jun 19 10:24:18 ..... finished successfully ############################################### ##### ALIGNING PAIRED-END READS S10_2_2 ############################################### STAR --genomeDir results/STAR_output/indexing_contigs_1_2/ --runThreadN 8 --runMode alignReads --readFilesIn ../Data/Clover_Data/S10_2_2.R1.fastq ../Data/Clover_Data/S10_2_2.R2.fastq --outFileNamePrefix results/STAR_output/S10_align_contigs_1_2/S10_2_2 --outSAMtype BAM SortedByCoordinate --outSAMattributes Standard --quantMode GeneCounts --alignIntronMax 5000 STAR version: 2.7.10a compiled: 2022-01-14T18:50:00-05:00 :/home/dobin/data/STAR/STARcode/STAR.master/source Jun 19 10:24:20 ..... started STAR run Jun 19 10:24:20 ..... loading genome Jun 19 10:24:20 ..... started mapping Jun 19 10:24:31 ..... finished mapping Jun 19 10:24:31 ..... started sorting BAM Jun 19 10:24:33 ..... finished successfully ############################################### ##### ALIGNING PAIRED-END READS S10_2_3 ############################################### STAR --genomeDir results/STAR_output/indexing_contigs_1_2/ --runThreadN 8 --runMode alignReads --readFilesIn ../Data/Clover_Data/S10_2_3.R1.fastq ../Data/Clover_Data/S10_2_3.R2.fastq --outFileNamePrefix results/STAR_output/S10_align_contigs_1_2/S10_2_3 --outSAMtype BAM SortedByCoordinate --outSAMattributes Standard --quantMode GeneCounts --alignIntronMax 5000 STAR version: 2.7.10a compiled: 2022-01-14T18:50:00-05:00 :/home/dobin/data/STAR/STARcode/STAR.master/source Jun 19 10:24:33 ..... started STAR run Jun 19 10:24:33 ..... loading genome Jun 19 10:24:33 ..... started mapping Jun 19 10:24:45 ..... finished mapping Jun 19 10:24:45 ..... started sorting BAM Jun 19 10:24:48 ..... finished successfully Do the same alignment for Tienshan libraries In [21]: Copied! %% bash for i in ` ls ../ Data / Clover_Data / TI *. R1 . fastq ` do PREFIXNAME = ` basename $ i . R1 . fastq ` echo \"###############################################\" echo \"##### ALIGNING PAIRED-END READS \" $ PREFIXNAME echo \"###############################################\" STAR -- genomeDir results / STAR_output / indexing_contigs_1_2 / \\ -- runThreadN 8 \\ -- readFilesIn ../ Data / Clover_Data / $ PREFIXNAME . R1 . fastq ../ Data / Clover_Data / $ PREFIXNAME . R2 . fastq \\ -- outFileNamePrefix results / STAR_output / TI_align_contigs_1_2 / $ PREFIXNAME \\ -- outSAMtype BAM SortedByCoordinate \\ -- outSAMattributes Standard \\ -- quantMode GeneCounts \\ -- alignIntronMax 5000 done %%bash for i in `ls ../Data/Clover_Data/TI*.R1.fastq` do PREFIXNAME=`basename $i .R1.fastq` echo \"###############################################\" echo \"##### ALIGNING PAIRED-END READS \"$PREFIXNAME echo \"###############################################\" STAR --genomeDir results/STAR_output/indexing_contigs_1_2/ \\ --runThreadN 8 \\ --readFilesIn ../Data/Clover_Data/$PREFIXNAME.R1.fastq ../Data/Clover_Data/$PREFIXNAME.R2.fastq \\ --outFileNamePrefix results/STAR_output/TI_align_contigs_1_2/$PREFIXNAME \\ --outSAMtype BAM SortedByCoordinate \\ --outSAMattributes Standard \\ --quantMode GeneCounts \\ --alignIntronMax 5000 done ############################################### ##### ALIGNING PAIRED-END READS TI_1_1 ############################################### STAR --genomeDir results/STAR_output/indexing_contigs_1_2/ --runThreadN 8 --readFilesIn ../Data/Clover_Data/TI_1_1.R1.fastq ../Data/Clover_Data/TI_1_1.R2.fastq --outFileNamePrefix results/STAR_output/TI_align_contigs_1_2/TI_1_1 --outSAMtype BAM SortedByCoordinate --outSAMattributes Standard --quantMode GeneCounts --alignIntronMax 5000 STAR version: 2.7.10a compiled: 2022-01-14T18:50:00-05:00 :/home/dobin/data/STAR/STARcode/STAR.master/source Jun 19 10:24:50 ..... started STAR run Jun 19 10:24:50 ..... loading genome Jun 19 10:24:50 ..... started mapping Jun 19 10:25:03 ..... finished mapping Jun 19 10:25:04 ..... started sorting BAM Jun 19 10:25:18 ..... finished successfully ############################################### ##### ALIGNING PAIRED-END READS TI_1_2 ############################################### STAR --genomeDir results/STAR_output/indexing_contigs_1_2/ --runThreadN 8 --readFilesIn ../Data/Clover_Data/TI_1_2.R1.fastq ../Data/Clover_Data/TI_1_2.R2.fastq --outFileNamePrefix results/STAR_output/TI_align_contigs_1_2/TI_1_2 --outSAMtype BAM SortedByCoordinate --outSAMattributes Standard --quantMode GeneCounts --alignIntronMax 5000 STAR version: 2.7.10a compiled: 2022-01-14T18:50:00-05:00 :/home/dobin/data/STAR/STARcode/STAR.master/source Jun 19 10:25:18 ..... started STAR run Jun 19 10:25:18 ..... loading genome Jun 19 10:25:18 ..... started mapping Jun 19 10:25:30 ..... finished mapping Jun 19 10:25:30 ..... started sorting BAM Jun 19 10:25:33 ..... finished successfully ############################################### ##### ALIGNING PAIRED-END READS TI_1_3 ############################################### STAR --genomeDir results/STAR_output/indexing_contigs_1_2/ --runThreadN 8 --readFilesIn ../Data/Clover_Data/TI_1_3.R1.fastq ../Data/Clover_Data/TI_1_3.R2.fastq --outFileNamePrefix results/STAR_output/TI_align_contigs_1_2/TI_1_3 --outSAMtype BAM SortedByCoordinate --outSAMattributes Standard --quantMode GeneCounts --alignIntronMax 5000 STAR version: 2.7.10a compiled: 2022-01-14T18:50:00-05:00 :/home/dobin/data/STAR/STARcode/STAR.master/source Jun 19 10:25:36 ..... started STAR run Jun 19 10:25:36 ..... loading genome Jun 19 10:25:36 ..... started mapping Jun 19 10:25:50 ..... finished mapping Jun 19 10:25:50 ..... started sorting BAM Jun 19 10:25:51 ..... finished successfully ############################################### ##### ALIGNING PAIRED-END READS TI_2_1 ############################################### STAR --genomeDir results/STAR_output/indexing_contigs_1_2/ --runThreadN 8 --readFilesIn ../Data/Clover_Data/TI_2_1.R1.fastq ../Data/Clover_Data/TI_2_1.R2.fastq --outFileNamePrefix results/STAR_output/TI_align_contigs_1_2/TI_2_1 --outSAMtype BAM SortedByCoordinate --outSAMattributes Standard --quantMode GeneCounts --alignIntronMax 5000 STAR version: 2.7.10a compiled: 2022-01-14T18:50:00-05:00 :/home/dobin/data/STAR/STARcode/STAR.master/source Jun 19 10:25:51 ..... started STAR run Jun 19 10:25:51 ..... loading genome Jun 19 10:25:52 ..... started mapping Jun 19 10:26:06 ..... finished mapping Jun 19 10:26:06 ..... started sorting BAM Jun 19 10:26:09 ..... finished successfully ############################################### ##### ALIGNING PAIRED-END READS TI_2_2 ############################################### STAR --genomeDir results/STAR_output/indexing_contigs_1_2/ --runThreadN 8 --readFilesIn ../Data/Clover_Data/TI_2_2.R1.fastq ../Data/Clover_Data/TI_2_2.R2.fastq --outFileNamePrefix results/STAR_output/TI_align_contigs_1_2/TI_2_2 --outSAMtype BAM SortedByCoordinate --outSAMattributes Standard --quantMode GeneCounts --alignIntronMax 5000 STAR version: 2.7.10a compiled: 2022-01-14T18:50:00-05:00 :/home/dobin/data/STAR/STARcode/STAR.master/source Jun 19 10:26:09 ..... started STAR run Jun 19 10:26:09 ..... loading genome Jun 19 10:26:09 ..... started mapping Jun 19 10:26:23 ..... finished mapping Jun 19 10:26:23 ..... started sorting BAM Jun 19 10:26:31 ..... finished successfully ############################################### ##### ALIGNING PAIRED-END READS TI_2_3 ############################################### STAR --genomeDir results/STAR_output/indexing_contigs_1_2/ --runThreadN 8 --readFilesIn ../Data/Clover_Data/TI_2_3.R1.fastq ../Data/Clover_Data/TI_2_3.R2.fastq --outFileNamePrefix results/STAR_output/TI_align_contigs_1_2/TI_2_3 --outSAMtype BAM SortedByCoordinate --outSAMattributes Standard --quantMode GeneCounts --alignIntronMax 5000 STAR version: 2.7.10a compiled: 2022-01-14T18:50:00-05:00 :/home/dobin/data/STAR/STARcode/STAR.master/source Jun 19 10:26:31 ..... started STAR run Jun 19 10:26:31 ..... loading genome Jun 19 10:26:31 ..... started mapping Jun 19 10:26:46 ..... finished mapping Jun 19 10:26:46 ..... started sorting BAM Jun 19 10:26:49 ..... finished successfully Run quality control on each aligned library with MultiQC . In this way there will be a whole report to compare S10 files and Tienshan files. In [22]: Copied! %% bash multiqc -- outdir results / multiqc_output / TI_STAR_align_1_2 \\ results / STAR_output / TI_align_contigs_1_2 / %%bash multiqc --outdir results/multiqc_output/TI_STAR_align_1_2 \\ results/STAR_output/TI_align_contigs_1_2/ / / / \u001b]8;id=121575;https://multiqc.info\u001b\\ MultiQC \u001b]8;;\u001b\\ \ud83d\udd0d | v1.14 | multiqc | Search path : /work/SamueleSoraggi/Notebooks/results/STAR_output/TI_align_contigs_1_2 | searching | \u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501 100% 36/36 og.o\u2026 | star | Found 6 reports and 6 gene count files | multiqc | Compressing plot data | multiqc | Report : results/multiqc_output/TI_STAR_align_1_2/multiqc_report.html | multiqc | Data : results/multiqc_output/TI_STAR_align_1_2/multiqc_data | multiqc | MultiQC complete In [23]: Copied! %% bash multiqc -- outdir results / multiqc_output / S10_STAR_align_1_2 \\ results / STAR_output / S10_align_contigs_1_2 / %%bash multiqc --outdir results/multiqc_output/S10_STAR_align_1_2 \\ results/STAR_output/S10_align_contigs_1_2/ / / / \u001b]8;id=596946;https://multiqc.info\u001b\\ MultiQC \u001b]8;;\u001b\\ \ud83d\udd0d | v1.14 | multiqc | Search path : /work/SamueleSoraggi/Notebooks/results/STAR_output/S10_align_contigs_1_2 | searching | \u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501 100% 36/36 ut.t\u2026 | star | Found 6 reports and 6 gene count files | multiqc | Compressing plot data | multiqc | Report : results/multiqc_output/S10_STAR_align_1_2/multiqc_report.html | multiqc | Data : results/multiqc_output/S10_STAR_align_1_2/multiqc_data | multiqc | MultiQC complete We merge the outputs of each group of aligned libraries. Here is how the files look like for the Tienshan . In [24]: Copied! ! ls - lh results / STAR_output / TI_align_contigs_1_2 / TI_ *. sortedByCoord . out . bam !ls -lh results/STAR_output/TI_align_contigs_1_2/TI_*.sortedByCoord.out.bam -rw-r--r--. 1 ucloud users 6.6M Jun 19 10:25 results/STAR_output/TI_align_contigs_1_2/TI_1_1Aligned.sortedByCoord.out.bam -rw-r--r--. 1 ucloud users 6.0M Jun 19 10:25 results/STAR_output/TI_align_contigs_1_2/TI_1_2Aligned.sortedByCoord.out.bam -rw-r--r--. 1 ucloud users 7.1M Jun 19 10:25 results/STAR_output/TI_align_contigs_1_2/TI_1_3Aligned.sortedByCoord.out.bam -rw-r--r--. 1 ucloud users 9.6M Jun 19 10:26 results/STAR_output/TI_align_contigs_1_2/TI_2_1Aligned.sortedByCoord.out.bam -rw-r--r--. 1 ucloud users 7.2M Jun 19 10:26 results/STAR_output/TI_align_contigs_1_2/TI_2_2Aligned.sortedByCoord.out.bam -rw-r--r--. 1 ucloud users 8.2M Jun 19 10:26 results/STAR_output/TI_align_contigs_1_2/TI_2_3Aligned.sortedByCoord.out.bam Apply samtools merge In [25]: Copied! %% bash mkdir - p results / STAR_output / TI_align_contigs_1_2_merge / samtools merge - f results / STAR_output / TI_align_contigs_1_2_merge / TI . sorted . bam results / STAR_output / TI_align_contigs_1_2 / TI_ *. sortedByCoord . out . bam %%bash mkdir -p results/STAR_output/TI_align_contigs_1_2_merge/ samtools merge -f results/STAR_output/TI_align_contigs_1_2_merge/TI.sorted.bam results/STAR_output/TI_align_contigs_1_2/TI_*.sortedByCoord.out.bam In [26]: Copied! %% bash mkdir - p results / STAR_output / S10_align_contigs_1_2_merge / samtools merge - f results / STAR_output / S10_align_contigs_1_2_merge / S10 . sorted . bam results / STAR_output / S10_align_contigs_1_2 / S10_ *. sortedByCoord . out . bam %%bash mkdir -p results/STAR_output/S10_align_contigs_1_2_merge/ samtools merge -f results/STAR_output/S10_align_contigs_1_2_merge/S10.sorted.bam results/STAR_output/S10_align_contigs_1_2/S10_*.sortedByCoord.out.bam Index both merging outputs. A file in format bam.bai will appear in their respective folders. In [27]: Copied! %% bash samtools index results / STAR_output / S10_align_contigs_1_2_merge / S10 . sorted . bam %%bash samtools index results/STAR_output/S10_align_contigs_1_2_merge/S10.sorted.bam In [28]: Copied! %% bash samtools index results / STAR_output / TI_align_contigs_1_2_merge / TI . sorted . bam %%bash samtools index results/STAR_output/TI_align_contigs_1_2_merge/TI.sorted.bam Wrapping up \ud83c\udf89 \ud83c\udf89 \ud83c\udf89 \u00b6 In this exercise, you learnt to align various types of data after performing quality control for raw data. We looked at some of the options for the aligners and at how to use some of the basic samtools manipulation programs. The outputs from the RNA alignments will be used for the VCF file analysis in the next notebook, and the RNA alignments will be use for the bulk RNA data analysis.","title":"3.1.Alignment of raw data"},{"location":"nb/align.html#biological-background","text":"White clover (Trifolium repens) is an allotetraploid. It is a relatively young, outcrossing species, which originated during the most recent glaciation around 20,000 years ago by hybridisation of two diploid species, T. occidentale and T. pallescens (see figure below). This means that it contains genomes originating from two different species within the same nucleus. Normally, white clover is an outbreeding species, but a self-compatible line was used for sequencing the white clover genome (Griffiths et al, 2019) . This line will be designated as S10 in the data, indicating that this is the 10th self-fertilized generation. In addition, we have data from a wild clover accession (ecotype) called Tienshan ( Ti ), which is collected from the Chinese mountains and is adapted to alpine conditions. We will perform alignment of the data to the white clover's reference genome containing both T. occidentale and T. pallescens (called contig 1 and contig 2 in the data). We will also perform alignment to each subgenome, and see which are the differences with the quality control tools.","title":"Biological background"},{"location":"nb/align.html#quality-control-and-mapping","text":"","title":"Quality control and mapping"},{"location":"nb/align.html#quality-control","text":"We run FastQC on the PacBio Hifi reads and on two of the Illumina RNA-seq libraries. FastQC does quality control of the raw sequence data, providing an overview of the data which can help identify if there are any problems that should be addressed before further analysis. You can find the report for each file into the folder results/fastqc_output/ . The output is in HTML format and can be opened in any browser or in jupyterlab . It is however not easy to compare the various libraries by opening separate reports. To aggregate all the results, we apply the MultiQC software to the reports' folder. The output of MultiQC is in the directory results/multiqc_output/fastqc_data . In [1]: Copied! %% bash #run fastqc mkdir - p results / fastqc_output fastqc - q - o results / fastqc_output ../ Data / Clover_Data /*. fastq > / dev / null 2 >& 1 %%bash #run fastqc mkdir -p results/fastqc_output fastqc -q -o results/fastqc_output ../Data/Clover_Data/*.fastq > /dev/null 2>&1 Note: fastqc prints a lot of output conisting of a simple confirmation of execution without error, even when using the option -q , which means quiet . Therefore we added > /dev/null 2>&1 to the command to mute the printing of that output. In [2]: Copied! %% bash #run multiqc multiqc -- outdir results / multiqc_output / fastqc_data results / fastqc_output %%bash #run multiqc multiqc --outdir results/multiqc_output/fastqc_data results/fastqc_output / / / \u001b]8;id=903972;https://multiqc.info\u001b\\ MultiQC \u001b]8;;\u001b\\ \ud83d\udd0d | v1.14 | multiqc | Search path : /work/SamueleSoraggi/Notebooks/results/fastqc_output | searching | \u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501 100% 50/50 .html | fastqc | Found 25 reports | multiqc | Compressing plot data | multiqc | Report : results/multiqc_output/fastqc_data/multiqc_report.html | multiqc | Data : results/multiqc_output/fastqc_data/multiqc_data | multiqc | MultiQC complete Questions Visualize the Webpage generated by MultiQC. Hint: You can find a Help button that offers additional information about the plots for each panel. Focus on the following panels: \u201cPer base sequence quality\u201d, \u201cPer sequence quality scores\u201d.... (\u201cPer base sequence content\u201d always gives a FAIL for RNA-seq data). What do you notice with respect to the sequence quality scores? Are there any other quality issues worth noting?","title":"Quality Control"},{"location":"nb/align.html#hifi-data-mapping","text":"We map the PacBio Hifi reads ( Hifi_reads_white_clover.fastq ) to the white clover reference sequence (Contig1&2) using minimap2 . We run two mapping rounds, using two different preset options ( -x in the command) for the technology: PacBio/Oxford Nanopore read to reference mapping: map-pb Long assembly to reference mapping. Divergence is below 20%\u201d settings asm20 . Next, we create reports of the mapping results by running QualiMap on the two obtained SAM files. We first need to index the reference fasta files using samtools faidx . This produces files in .fai format containing informations about length of the reference sequence, offset for the quality scores, name of the reference sequence. Click here for a detailed overview. In [3]: Copied! %% bash #copy the reference data in the folder reference_data, so that you can write the indexing files mkdir - p reference_data cp ../ Data / Clover_Data / DNA_Contig1_2 . fasta ../ Data / Clover_Data / DNA_Contig1 . fasta ../ Data / Clover_Data / DNA_Contig2 . fasta reference_data %%bash #copy the reference data in the folder reference_data, so that you can write the indexing files mkdir -p reference_data cp ../Data/Clover_Data/DNA_Contig1_2.fasta ../Data/Clover_Data/DNA_Contig1.fasta ../Data/Clover_Data/DNA_Contig2.fasta reference_data In [4]: Copied! %% bash samtools faidx reference_data / DNA_Contig1_2 . fasta samtools faidx reference_data / DNA_Contig1 . fasta samtools faidx reference_data / DNA_Contig2 . fasta %%bash samtools faidx reference_data/DNA_Contig1_2.fasta samtools faidx reference_data/DNA_Contig1.fasta samtools faidx reference_data/DNA_Contig2.fasta we create an output folder for the HIFI alignment, and run minimap2 with the settings explained before. In [5]: Copied! %% bash mkdir - p results / HIFI_alignment / minimap2 - a - x map - pb - o results / HIFI_alignment / PacBio_clover_alignment_1_2_mappb . sam \\ reference_data / DNA_Contig1_2 . fasta \\ ../ Data / Clover_Data / Hifi_reads_white_clover . fastq minimap2 - a - x asm20 - o results / HIFI_alignment / PacBio_clover_alignment_1_2_asm20 . sam \\ reference_data / DNA_Contig1_2 . fasta \\ ../ Data / Clover_Data / Hifi_reads_white_clover . fastq %%bash mkdir -p results/HIFI_alignment/ minimap2 -a -x map-pb -o results/HIFI_alignment/PacBio_clover_alignment_1_2_mappb.sam \\ reference_data/DNA_Contig1_2.fasta \\ ../Data/Clover_Data/Hifi_reads_white_clover.fastq minimap2 -a -x asm20 -o results/HIFI_alignment/PacBio_clover_alignment_1_2_asm20.sam \\ reference_data/DNA_Contig1_2.fasta \\ ../Data/Clover_Data/Hifi_reads_white_clover.fastq [M::mm_idx_gen::0.075*1.04] collected minimizers [M::mm_idx_gen::0.099*1.52] sorted minimizers [M::main::0.099*1.52] loaded/built the index for 2 target sequence(s) [M::mm_mapopt_update::0.114*1.45] mid_occ = 11 [M::mm_idx_stat] kmer size: 19; skip: 10; is_hpc: 1; #seq: 2 [M::mm_idx_stat::0.118*1.43] distinct minimizers: 203943 (79.05% are singletons); average occurrences: 1.273; average spacing: 8.047; total length: 2089554 [M::worker_pipeline::11.859*2.89] mapped 4395 sequences [M::main] Version: 2.24-r1122 [M::main] CMD: minimap2 -a -x map-pb -o results/HIFI_alignment/PacBio_clover_alignment_1_2_mappb.sam reference_data/DNA_Contig1_2.fasta ../Data/Clover_Data/Hifi_reads_white_clover.fastq [M::main] Real time: 11.873 sec; CPU: 34.246 sec; Peak RSS: 1.186 GB [M::mm_idx_gen::0.084*1.04] collected minimizers [M::mm_idx_gen::0.121*1.62] sorted minimizers [M::main::0.121*1.62] loaded/built the index for 2 target sequence(s) [M::mm_mapopt_update::0.138*1.54] mid_occ = 50 [M::mm_idx_stat] kmer size: 19; skip: 10; is_hpc: 0; #seq: 2 [M::mm_idx_stat::0.143*1.52] distinct minimizers: 298340 (78.21% are singletons); average occurrences: 1.277; average spacing: 5.484; total length: 2089554 [M::worker_pipeline::18.366*2.88] mapped 4395 sequences [M::main] Version: 2.24-r1122 [M::main] CMD: minimap2 -a -x asm20 -o results/HIFI_alignment/PacBio_clover_alignment_1_2_asm20.sam reference_data/DNA_Contig1_2.fasta ../Data/Clover_Data/Hifi_reads_white_clover.fastq [M::main] Real time: 18.394 sec; CPU: 52.932 sec; Peak RSS: 1.692 GB samtools sort is used to sort the alignment with left-to-right coordinates. The output is in .bam format, with .sam files in input (Note that you could have gotten .bam files from minimap2 with a specific option). In [6]: Copied! %% bash samtools sort results / HIFI_alignment / PacBio_clover_alignment_1_2_mappb . sam \\ - o results / HIFI_alignment / PacBio_clover_alignment_1_2_mappb . sort . bam samtools sort results / HIFI_alignment / PacBio_clover_alignment_1_2_asm20 . sam \\ - o results / HIFI_alignment / PacBio_clover_alignment_1_2_asm20 . sort . bam %%bash samtools sort results/HIFI_alignment/PacBio_clover_alignment_1_2_mappb.sam \\ -o results/HIFI_alignment/PacBio_clover_alignment_1_2_mappb.sort.bam samtools sort results/HIFI_alignment/PacBio_clover_alignment_1_2_asm20.sam \\ -o results/HIFI_alignment/PacBio_clover_alignment_1_2_asm20.sort.bam samtools index creates the index for the bam file, stored in .bai format. The index file lets programs access any position into the aligned data without reading the whole file, which would take too much time. In [7]: Copied! %% bash samtools index results / HIFI_alignment / PacBio_clover_alignment_1_2_mappb . sort . bam samtools index results / HIFI_alignment / PacBio_clover_alignment_1_2_asm20 . sort . bam %%bash samtools index results/HIFI_alignment/PacBio_clover_alignment_1_2_mappb.sort.bam samtools index results/HIFI_alignment/PacBio_clover_alignment_1_2_asm20.sort.bam Run quality control on both files In [8]: Copied! %% bash qualimap bamqc - bam results / HIFI_alignment / PacBio_clover_alignment_1_2_mappb . sort . bam \\ - outdir results / qualimap_output / PacBio_clover_alignment_1_2_mappb qualimap bamqc - bam results / HIFI_alignment / PacBio_clover_alignment_1_2_asm20 . sort . bam \\ - outdir results / qualimap_output / PacBio_clover_alignment_1_2_asm20 %%bash qualimap bamqc -bam results/HIFI_alignment/PacBio_clover_alignment_1_2_mappb.sort.bam \\ -outdir results/qualimap_output/PacBio_clover_alignment_1_2_mappb qualimap bamqc -bam results/HIFI_alignment/PacBio_clover_alignment_1_2_asm20.sort.bam \\ -outdir results/qualimap_output/PacBio_clover_alignment_1_2_asm20 Java memory size is set to 1200M Launching application... QualiMap v.2.2.2-dev Built on 2019-11-11 14:05 Selected tool: bamqc Available memory (Mb): 33 Max memory (Mb): 1258 Starting bam qc.... Loading sam header... Loading locator... Loading reference... Number of windows: 400, effective number of windows: 401 Chunk of reads size: 1000 Number of threads: 8 Processed 50 out of 401 windows... Processed 100 out of 401 windows... Processed 150 out of 401 windows... Processed 200 out of 401 windows... Processed 250 out of 401 windows... Processed 300 out of 401 windows... Processed 350 out of 401 windows... Processed 400 out of 401 windows... Total processed windows:401 Number of reads: 4395 Number of valid reads: 4696 Number of correct strand reads:0 Inside of regions... Num mapped reads: 4395 Num mapped first of pair: 0 Num mapped second of pair: 0 Num singletons: 0 Time taken to analyze reads: 12 Computing descriptors... numberOfMappedBases: 70274383 referenceSize: 2089554 numberOfSequencedBases: 70039397 numberOfAs: 23621784 Computing per chromosome statistics... Computing histograms... Overall analysis time: 12 end of bam qc Computing report... Writing HTML report... HTML report created successfully Finished Java memory size is set to 1200M Launching application... QualiMap v.2.2.2-dev Built on 2019-11-11 14:05 Selected tool: bamqc Available memory (Mb): 33 Max memory (Mb): 1258 Starting bam qc.... Loading sam header... Loading locator... Loading reference... Number of windows: 400, effective number of windows: 401 Chunk of reads size: 1000 Number of threads: 8 Processed 50 out of 401 windows... Processed 100 out of 401 windows... Processed 150 out of 401 windows... Processed 200 out of 401 windows... Processed 250 out of 401 windows... Processed 300 out of 401 windows... Processed 350 out of 401 windows... Processed 400 out of 401 windows... Total processed windows:401 Number of reads: 4395 Number of valid reads: 4747 Number of correct strand reads:0 Inside of regions... Num mapped reads: 4395 Num mapped first of pair: 0 Num mapped second of pair: 0 Num singletons: 0 Time taken to analyze reads: 12 Computing descriptors... numberOfMappedBases: 69947563 referenceSize: 2089554 numberOfSequencedBases: 69812688 numberOfAs: 23541478 Computing per chromosome statistics... Computing histograms... Overall analysis time: 12 end of bam qc Computing report... Writing HTML report... HTML report created successfully Finished For easier comparison, we can again collapse the two reports into a single one using MultiQC , in the same way we did for putting together the other reports from fastQC . In [9]: Copied! %% bash #run multiqc multiqc -- outdir results / qualimap_output results / qualimap_output %%bash #run multiqc multiqc --outdir results/qualimap_output results/qualimap_output / / / \u001b]8;id=941002;https://multiqc.info\u001b\\ MultiQC \u001b]8;;\u001b\\ \ud83d\udd0d | v1.14 | multiqc | Search path : /work/SamueleSoraggi/Notebooks/results/qualimap_output | searching | \u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501 100% 93/93 nt.t\u2026 | qualimap | Found 2 BamQC reports | multiqc | Compressing plot data | multiqc | Report : results/qualimap_output/multiqc_report.html | multiqc | Data : results/qualimap_output/multiqc_data | multiqc | MultiQC complete Now you can visualize the report generated, which is in results/qualimap_output/multiqc_report.html . Next, we map the white clover PacBio Hifi reads to contig1 and contig2 separately, using the setting you selected at the previous step (let's say map-pb was chosen, but you are free to change this setting in the commands). As the two contigs represent the two white clover subgenomes, this mapping will allow you to see the two subgenome haplotypes and call subgenome SNPs. In [10]: Copied! %% bash minimap2 - a - x map - pb - o results / HIFI_alignment / PacBio_clover_alignment_1 . sam \\ reference_data / DNA_Contig1 . fasta \\ ../ Data / Clover_Data / Hifi_reads_white_clover . fastq %%bash minimap2 -a -x map-pb -o results/HIFI_alignment/PacBio_clover_alignment_1.sam \\ reference_data/DNA_Contig1.fasta \\ ../Data/Clover_Data/Hifi_reads_white_clover.fastq [M::mm_idx_gen::0.050*0.96] collected minimizers [M::mm_idx_gen::0.071*1.55] sorted minimizers [M::main::0.071*1.55] loaded/built the index for 1 target sequence(s) [M::mm_mapopt_update::0.079*1.50] mid_occ = 10 [M::mm_idx_stat] kmer size: 19; skip: 10; is_hpc: 1; #seq: 1 [M::mm_idx_stat::0.082*1.48] distinct minimizers: 112547 (91.45% are singletons); average occurrences: 1.141; average spacing: 8.031; total length: 1031631 [M::worker_pipeline::27.024*2.90] mapped 4395 sequences [M::main] Version: 2.24-r1122 [M::main] CMD: minimap2 -a -x map-pb -o results/HIFI_alignment/PacBio_clover_alignment_1.sam reference_data/DNA_Contig1.fasta ../Data/Clover_Data/Hifi_reads_white_clover.fastq [M::main] Real time: 27.035 sec; CPU: 78.431 sec; Peak RSS: 2.258 GB In [11]: Copied! %% bash minimap2 - a - x map - pb - o results / HIFI_alignment / PacBio_clover_alignment_2 . sam \\ reference_data / DNA_Contig2 . fasta \\ ../ Data / Clover_Data / Hifi_reads_white_clover . fastq %%bash minimap2 -a -x map-pb -o results/HIFI_alignment/PacBio_clover_alignment_2.sam \\ reference_data/DNA_Contig2.fasta \\ ../Data/Clover_Data/Hifi_reads_white_clover.fastq [M::mm_idx_gen::0.048*1.04] collected minimizers [M::mm_idx_gen::0.069*1.61] sorted minimizers [M::main::0.069*1.61] loaded/built the index for 1 target sequence(s) [M::mm_mapopt_update::0.076*1.55] mid_occ = 10 [M::mm_idx_stat] kmer size: 19; skip: 10; is_hpc: 1; #seq: 1 [M::mm_idx_stat::0.080*1.53] distinct minimizers: 120722 (93.18% are singletons); average occurrences: 1.087; average spacing: 8.062; total length: 1057923 [M::worker_pipeline::27.150*2.94] mapped 4395 sequences [M::main] Version: 2.24-r1122 [M::main] CMD: minimap2 -a -x map-pb -o results/HIFI_alignment/PacBio_clover_alignment_2.sam reference_data/DNA_Contig2.fasta ../Data/Clover_Data/Hifi_reads_white_clover.fastq [M::main] Real time: 27.162 sec; CPU: 79.819 sec; Peak RSS: 2.058 GB Sort the bam files and create their index using samtools In [12]: Copied! %% bash samtools sort results / HIFI_alignment / PacBio_clover_alignment_1 . sam - o results / HIFI_alignment / PacBio_clover_alignment_1 . sort . bam samtools sort results / HIFI_alignment / PacBio_clover_alignment_2 . sam - o results / HIFI_alignment / PacBio_clover_alignment_2 . sort . bam %%bash samtools sort results/HIFI_alignment/PacBio_clover_alignment_1.sam -o results/HIFI_alignment/PacBio_clover_alignment_1.sort.bam samtools sort results/HIFI_alignment/PacBio_clover_alignment_2.sam -o results/HIFI_alignment/PacBio_clover_alignment_2.sort.bam In [13]: Copied! %% bash samtools index results / HIFI_alignment / PacBio_clover_alignment_1 . sort . bam samtools index results / HIFI_alignment / PacBio_clover_alignment_2 . sort . bam %%bash samtools index results/HIFI_alignment/PacBio_clover_alignment_1.sort.bam samtools index results/HIFI_alignment/PacBio_clover_alignment_2.sort.bam Perform quality control In [14]: Copied! %% bash mkdir - p results / qualimap_output qualimap bamqc - bam results / HIFI_alignment / PacBio_clover_alignment_1 . sort . bam - outdir results / qualimap_output / PacBio_clover_alignment_1 %%bash mkdir -p results/qualimap_output qualimap bamqc -bam results/HIFI_alignment/PacBio_clover_alignment_1.sort.bam -outdir results/qualimap_output/PacBio_clover_alignment_1 Java memory size is set to 1200M Launching application... QualiMap v.2.2.2-dev Built on 2019-11-11 14:05 Selected tool: bamqc Available memory (Mb): 33 Max memory (Mb): 1258 Starting bam qc.... Loading sam header... Loading locator... Loading reference... Number of windows: 400, effective number of windows: 400 Chunk of reads size: 1000 Number of threads: 8 Processed 50 out of 400 windows... Processed 100 out of 400 windows... Processed 150 out of 400 windows... Processed 200 out of 400 windows... Processed 250 out of 400 windows... Processed 300 out of 400 windows... Processed 350 out of 400 windows... Processed 400 out of 400 windows... Total processed windows:400 Number of reads: 4395 Number of valid reads: 9070 Number of correct strand reads:0 Inside of regions... Num mapped reads: 4356 Num mapped first of pair: 0 Num mapped second of pair: 0 Num singletons: 0 Time taken to analyze reads: 10 Computing descriptors... numberOfMappedBases: 59058058 referenceSize: 1031631 numberOfSequencedBases: 54917933 numberOfAs: 18508479 Computing per chromosome statistics... Computing histograms... Overall analysis time: 11 end of bam qc Computing report... Writing HTML report... HTML report created successfully Finished In [15]: Copied! %% bash qualimap bamqc - bam results / HIFI_alignment / PacBio_clover_alignment_2 . sort . bam - outdir results / qualimap_output / PacBio_clover_alignment_2 %%bash qualimap bamqc -bam results/HIFI_alignment/PacBio_clover_alignment_2.sort.bam -outdir results/qualimap_output/PacBio_clover_alignment_2 Java memory size is set to 1200M Launching application... QualiMap v.2.2.2-dev Built on 2019-11-11 14:05 Selected tool: bamqc Available memory (Mb): 33 Max memory (Mb): 1258 Starting bam qc.... Loading sam header... Loading locator... Loading reference... Number of windows: 400, effective number of windows: 400 Chunk of reads size: 1000 Number of threads: 8 Processed 50 out of 400 windows... Processed 100 out of 400 windows... Processed 150 out of 400 windows... Processed 200 out of 400 windows... Processed 250 out of 400 windows... Processed 300 out of 400 windows... Processed 350 out of 400 windows... Processed 400 out of 400 windows... Total processed windows:400 Number of reads: 4395 Number of valid reads: 9462 Number of correct strand reads:0 Inside of regions... Num mapped reads: 4394 Num mapped first of pair: 0 Num mapped second of pair: 0 Num singletons: 0 Time taken to analyze reads: 13 Computing descriptors... numberOfMappedBases: 59388453 referenceSize: 1057923 numberOfSequencedBases: 55867300 numberOfAs: 18822184 Computing per chromosome statistics... Computing histograms... Overall analysis time: 13 end of bam qc Computing report... Writing HTML report... HTML report created successfully Finished In [16]: Copied! %% bash #run multiqc multiqc -- outdir results / qualimap_output results / qualimap_output %%bash #run multiqc multiqc --outdir results/qualimap_output results/qualimap_output / / / \u001b]8;id=999624;https://multiqc.info\u001b\\ MultiQC \u001b]8;;\u001b\\ \ud83d\udd0d | v1.14 | multiqc | Search path : /work/SamueleSoraggi/Notebooks/results/qualimap_output | searching | \u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501 100% 187/187 .t\u2026 | qualimap | Found 4 BamQC reports | multiqc | Compressing plot data | multiqc | Previous MultiQC output found! Adjusting filenames.. | multiqc | Use -f or --force to overwrite existing reports instead | multiqc | Report : results/qualimap_output/multiqc_report_1.html | multiqc | Data : results/qualimap_output/multiqc_data_1 | multiqc | MultiQC complete Task: IGV visualization and Questions Now you can inspect the alignment files in IGV . First, you will need to download the reference fasta sequence in ../Data/Clover_Data/DNA_Contig1_2.fasta and import it into IGV. You can do the same for the files DNA_Contig1.fasta and DNA_Contig2.fasta that you might need later. In IGV, this is done with the menu Genomes --> Load Genome from file menu and by selecting the relevant fasta file. Then, choose the reference you need from the drop-down menu (see figure below). You will not yet see much, but you can choose one of the two subgenomes (contig 1 or 2) and double click on a chromosome position to inspect the reference sequence. The next step will visualize the mapped files on IGV. Each mapped genome can be seen in IGV against the reference file of choice. To load an aligned file, first download it together with the index file in .bai format. For example, you need to download both results/HIFI_alignment/PacBio_clover_alignment_1.sort.bam and results/HIFI_alignment/PacBio_clover_alignment_1.sort.bam.bai to see this alignment (you need to open only the .bam file with IGV). If you open more files, their alignments will be distributed in the IGV interface, and you can change the size of each visualization yourself (below shown with only one opened alignment). Now compare in IGV the two bam files PacBio_clover_alignment_1.sort.bam and PacBio_clover_alignment_2.sort.bam . What do you observe when comparing the two BAM files? Have a look at the polymorphic regions in IGV. Are they true polymorphisms? Add to the visualization the third alignment PacBio_clover_alignment_1_2_mappb.sort.bam in IGV. Why do you see fluctuations in coverage and large regions without any apparent subgenome SNPs? What are the major differences between the stats for the reads mapped to Contigs1&2 versus contig1 and contig2? What is your interpretation of the differences?","title":"Hifi data mapping"},{"location":"nb/align.html#rna-seq-mapping","text":"In the ../Data folder you will find 24 RNA-seq libraries, 12 S10 libraries and 12 Tienshan libraries. Each library is paired-end, which is denoted by R1 and R2 at the end of two files having the same name, such as S10_1_1.R1.fastq and S10_1_1.R2.fastq . We will align each library separately and then merge the alignments to create two final samples for S10 and Tienshan . First, we need to create a genome file for the reference fasta file of contigs 1 and 2 . This is done with STAR , using the option --runMode genomeGenerate . We also need to convert the gene annotation from gff to gtf format with gffread to allow counting gene transcripts. STAR is a very complex tool with many options, so it is always useful to have a reference manual . In [17]: Copied! %% bash gffread - T - o reference_data / white_clover_genes . gtf ../ Data / Clover_Data / white_clover_genes . gff %%bash gffread -T -o reference_data/white_clover_genes.gtf ../Data/Clover_Data/white_clover_genes.gff In [18]: Copied! %% bash STAR -- runThreadN 8 \\ -- runMode genomeGenerate \\ -- genomeDir results / STAR_output / indexing_contigs_1_2 \\ -- genomeFastaFiles reference_data / DNA_Contig1_2 . fasta \\ -- sjdbGTFfile reference_data / white_clover_genes . gtf %%bash STAR --runThreadN 8 \\ --runMode genomeGenerate \\ --genomeDir results/STAR_output/indexing_contigs_1_2 \\ --genomeFastaFiles reference_data/DNA_Contig1_2.fasta \\ --sjdbGTFfile reference_data/white_clover_genes.gtf STAR --runThreadN 8 --runMode genomeGenerate --genomeDir results/STAR_output/indexing_contigs_1_2 --genomeFastaFiles reference_data/DNA_Contig1_2.fasta --sjdbGTFfile reference_data/white_clover_genes.gtf STAR version: 2.7.10a compiled: 2022-01-14T18:50:00-05:00 :/home/dobin/data/STAR/STARcode/STAR.master/source Jun 19 10:23:03 ..... started STAR run Jun 19 10:23:03 ... starting to generate Genome files Jun 19 10:23:03 ..... processing annotations GTF !!!!! WARNING: --genomeSAindexNbases 14 is too large for the genome size=2089554, which may cause seg-fault at the mapping step. Re-run genome generation with recommended --genomeSAindexNbases 9 Jun 19 10:23:03 ... starting to sort Suffix Array. This may take a long time... Jun 19 10:23:03 ... sorting Suffix Array chunks and saving them to disk... Jun 19 10:23:04 ... loading chunks from disk, packing SA... Jun 19 10:23:04 ... finished generating suffix array Jun 19 10:23:04 ... generating Suffix Array index Jun 19 10:23:08 ... completed Suffix Array index Jun 19 10:23:08 ..... inserting junctions into the genome indices Jun 19 10:23:16 ... writing Genome to disk ... Jun 19 10:23:16 ... writing Suffix Array to disk ... Jun 19 10:23:17 ... writing SAindex to disk Jun 19 10:23:18 ..... finished successfully We got a warning saying !!!!! WARNING: --genomeSAindexNbases 14 is too large for the genome size=2089554, which may cause seg-fault at the mapping step. Re-run genome generation with recommended --genomeSAindexNbases 9 meaning we need shorter strings of bases (9 bases instead of 14) to be indexed, as our reference genome is very short, and too long strings would cause many alignment errors. So we rerun the command with the suggested option. In [19]: Copied! %% bash STAR -- runThreadN 8 \\ -- runMode genomeGenerate \\ -- genomeDir results / STAR_output / indexing_contigs_1_2 \\ -- genomeFastaFiles reference_data / DNA_Contig1_2 . fasta \\ -- sjdbGTFfile reference_data / white_clover_genes . gtf \\ -- genomeSAindexNbases 9 %%bash STAR --runThreadN 8 \\ --runMode genomeGenerate \\ --genomeDir results/STAR_output/indexing_contigs_1_2 \\ --genomeFastaFiles reference_data/DNA_Contig1_2.fasta \\ --sjdbGTFfile reference_data/white_clover_genes.gtf \\ --genomeSAindexNbases 9 STAR --runThreadN 8 --runMode genomeGenerate --genomeDir results/STAR_output/indexing_contigs_1_2 --genomeFastaFiles reference_data/DNA_Contig1_2.fasta --sjdbGTFfile reference_data/white_clover_genes.gtf --genomeSAindexNbases 9 STAR version: 2.7.10a compiled: 2022-01-14T18:50:00-05:00 :/home/dobin/data/STAR/STARcode/STAR.master/source Jun 19 10:23:18 ..... started STAR run Jun 19 10:23:18 ... starting to generate Genome files Jun 19 10:23:18 ..... processing annotations GTF Jun 19 10:23:20 ... starting to sort Suffix Array. This may take a long time... Jun 19 10:23:20 ... sorting Suffix Array chunks and saving them to disk... Jun 19 10:23:21 ... loading chunks from disk, packing SA... Jun 19 10:23:21 ... finished generating suffix array Jun 19 10:23:21 ... generating Suffix Array index Jun 19 10:23:21 ... completed Suffix Array index Jun 19 10:23:22 ..... inserting junctions into the genome indices Jun 19 10:23:23 ... writing Genome to disk ... Jun 19 10:23:23 ... writing Suffix Array to disk ... Jun 19 10:23:23 ... writing SAindex to disk Jun 19 10:23:24 ..... finished successfully We use again STAR to align every single library for S10 . We extract the library name of each file and run STAR through each pair of files. Note that plant introns are very rarely more than 5000 bp and that you are mapping to two homoeologous contigs that show high similarity, especially in genic regions. We set the maximum size to 5000 using --alignIntronMax 5000 . In [20]: Copied! %% bash for i in ` ls ../ Data / Clover_Data / S10 *. R1 . fastq ` do PREFIXNAME = ` basename $ i . R1 . fastq ` echo \"###############################################\" echo \"##### ALIGNING PAIRED-END READS \" $ PREFIXNAME echo \"###############################################\" STAR -- genomeDir results / STAR_output / indexing_contigs_1_2 / \\ -- runThreadN 8 \\ -- runMode alignReads \\ -- readFilesIn ../ Data / Clover_Data / $ PREFIXNAME . R1 . fastq ../ Data / Clover_Data / $ PREFIXNAME . R2 . fastq \\ -- outFileNamePrefix results / STAR_output / S10_align_contigs_1_2 / $ PREFIXNAME \\ -- outSAMtype BAM SortedByCoordinate \\ -- outSAMattributes Standard \\ -- quantMode GeneCounts \\ -- alignIntronMax 5000 done %%bash for i in `ls ../Data/Clover_Data/S10*.R1.fastq` do PREFIXNAME=`basename $i .R1.fastq` echo \"###############################################\" echo \"##### ALIGNING PAIRED-END READS \"$PREFIXNAME echo \"###############################################\" STAR --genomeDir results/STAR_output/indexing_contigs_1_2/ \\ --runThreadN 8 \\ --runMode alignReads \\ --readFilesIn ../Data/Clover_Data/$PREFIXNAME.R1.fastq ../Data/Clover_Data/$PREFIXNAME.R2.fastq \\ --outFileNamePrefix results/STAR_output/S10_align_contigs_1_2/$PREFIXNAME \\ --outSAMtype BAM SortedByCoordinate \\ --outSAMattributes Standard \\ --quantMode GeneCounts \\ --alignIntronMax 5000 done ############################################### ##### ALIGNING PAIRED-END READS S10_1_1 ############################################### STAR --genomeDir results/STAR_output/indexing_contigs_1_2/ --runThreadN 8 --runMode alignReads --readFilesIn ../Data/Clover_Data/S10_1_1.R1.fastq ../Data/Clover_Data/S10_1_1.R2.fastq --outFileNamePrefix results/STAR_output/S10_align_contigs_1_2/S10_1_1 --outSAMtype BAM SortedByCoordinate --outSAMattributes Standard --quantMode GeneCounts --alignIntronMax 5000 STAR version: 2.7.10a compiled: 2022-01-14T18:50:00-05:00 :/home/dobin/data/STAR/STARcode/STAR.master/source Jun 19 10:23:24 ..... started STAR run Jun 19 10:23:24 ..... loading genome Jun 19 10:23:24 ..... started mapping Jun 19 10:23:35 ..... finished mapping Jun 19 10:23:35 ..... started sorting BAM Jun 19 10:23:37 ..... finished successfully ############################################### ##### ALIGNING PAIRED-END READS S10_1_2 ############################################### STAR --genomeDir results/STAR_output/indexing_contigs_1_2/ --runThreadN 8 --runMode alignReads --readFilesIn ../Data/Clover_Data/S10_1_2.R1.fastq ../Data/Clover_Data/S10_1_2.R2.fastq --outFileNamePrefix results/STAR_output/S10_align_contigs_1_2/S10_1_2 --outSAMtype BAM SortedByCoordinate --outSAMattributes Standard --quantMode GeneCounts --alignIntronMax 5000 STAR version: 2.7.10a compiled: 2022-01-14T18:50:00-05:00 :/home/dobin/data/STAR/STARcode/STAR.master/source Jun 19 10:23:38 ..... started STAR run Jun 19 10:23:38 ..... loading genome Jun 19 10:23:38 ..... started mapping Jun 19 10:23:48 ..... finished mapping Jun 19 10:23:48 ..... started sorting BAM Jun 19 10:23:51 ..... finished successfully ############################################### ##### ALIGNING PAIRED-END READS S10_1_3 ############################################### STAR --genomeDir results/STAR_output/indexing_contigs_1_2/ --runThreadN 8 --runMode alignReads --readFilesIn ../Data/Clover_Data/S10_1_3.R1.fastq ../Data/Clover_Data/S10_1_3.R2.fastq --outFileNamePrefix results/STAR_output/S10_align_contigs_1_2/S10_1_3 --outSAMtype BAM SortedByCoordinate --outSAMattributes Standard --quantMode GeneCounts --alignIntronMax 5000 STAR version: 2.7.10a compiled: 2022-01-14T18:50:00-05:00 :/home/dobin/data/STAR/STARcode/STAR.master/source Jun 19 10:23:52 ..... started STAR run Jun 19 10:23:52 ..... loading genome Jun 19 10:23:52 ..... started mapping Jun 19 10:24:03 ..... finished mapping Jun 19 10:24:03 ..... started sorting BAM Jun 19 10:24:06 ..... finished successfully ############################################### ##### ALIGNING PAIRED-END READS S10_2_1 ############################################### STAR --genomeDir results/STAR_output/indexing_contigs_1_2/ --runThreadN 8 --runMode alignReads --readFilesIn ../Data/Clover_Data/S10_2_1.R1.fastq ../Data/Clover_Data/S10_2_1.R2.fastq --outFileNamePrefix results/STAR_output/S10_align_contigs_1_2/S10_2_1 --outSAMtype BAM SortedByCoordinate --outSAMattributes Standard --quantMode GeneCounts --alignIntronMax 5000 STAR version: 2.7.10a compiled: 2022-01-14T18:50:00-05:00 :/home/dobin/data/STAR/STARcode/STAR.master/source Jun 19 10:24:06 ..... started STAR run Jun 19 10:24:06 ..... loading genome Jun 19 10:24:06 ..... started mapping Jun 19 10:24:16 ..... finished mapping Jun 19 10:24:16 ..... started sorting BAM Jun 19 10:24:18 ..... finished successfully ############################################### ##### ALIGNING PAIRED-END READS S10_2_2 ############################################### STAR --genomeDir results/STAR_output/indexing_contigs_1_2/ --runThreadN 8 --runMode alignReads --readFilesIn ../Data/Clover_Data/S10_2_2.R1.fastq ../Data/Clover_Data/S10_2_2.R2.fastq --outFileNamePrefix results/STAR_output/S10_align_contigs_1_2/S10_2_2 --outSAMtype BAM SortedByCoordinate --outSAMattributes Standard --quantMode GeneCounts --alignIntronMax 5000 STAR version: 2.7.10a compiled: 2022-01-14T18:50:00-05:00 :/home/dobin/data/STAR/STARcode/STAR.master/source Jun 19 10:24:20 ..... started STAR run Jun 19 10:24:20 ..... loading genome Jun 19 10:24:20 ..... started mapping Jun 19 10:24:31 ..... finished mapping Jun 19 10:24:31 ..... started sorting BAM Jun 19 10:24:33 ..... finished successfully ############################################### ##### ALIGNING PAIRED-END READS S10_2_3 ############################################### STAR --genomeDir results/STAR_output/indexing_contigs_1_2/ --runThreadN 8 --runMode alignReads --readFilesIn ../Data/Clover_Data/S10_2_3.R1.fastq ../Data/Clover_Data/S10_2_3.R2.fastq --outFileNamePrefix results/STAR_output/S10_align_contigs_1_2/S10_2_3 --outSAMtype BAM SortedByCoordinate --outSAMattributes Standard --quantMode GeneCounts --alignIntronMax 5000 STAR version: 2.7.10a compiled: 2022-01-14T18:50:00-05:00 :/home/dobin/data/STAR/STARcode/STAR.master/source Jun 19 10:24:33 ..... started STAR run Jun 19 10:24:33 ..... loading genome Jun 19 10:24:33 ..... started mapping Jun 19 10:24:45 ..... finished mapping Jun 19 10:24:45 ..... started sorting BAM Jun 19 10:24:48 ..... finished successfully Do the same alignment for Tienshan libraries In [21]: Copied! %% bash for i in ` ls ../ Data / Clover_Data / TI *. R1 . fastq ` do PREFIXNAME = ` basename $ i . R1 . fastq ` echo \"###############################################\" echo \"##### ALIGNING PAIRED-END READS \" $ PREFIXNAME echo \"###############################################\" STAR -- genomeDir results / STAR_output / indexing_contigs_1_2 / \\ -- runThreadN 8 \\ -- readFilesIn ../ Data / Clover_Data / $ PREFIXNAME . R1 . fastq ../ Data / Clover_Data / $ PREFIXNAME . R2 . fastq \\ -- outFileNamePrefix results / STAR_output / TI_align_contigs_1_2 / $ PREFIXNAME \\ -- outSAMtype BAM SortedByCoordinate \\ -- outSAMattributes Standard \\ -- quantMode GeneCounts \\ -- alignIntronMax 5000 done %%bash for i in `ls ../Data/Clover_Data/TI*.R1.fastq` do PREFIXNAME=`basename $i .R1.fastq` echo \"###############################################\" echo \"##### ALIGNING PAIRED-END READS \"$PREFIXNAME echo \"###############################################\" STAR --genomeDir results/STAR_output/indexing_contigs_1_2/ \\ --runThreadN 8 \\ --readFilesIn ../Data/Clover_Data/$PREFIXNAME.R1.fastq ../Data/Clover_Data/$PREFIXNAME.R2.fastq \\ --outFileNamePrefix results/STAR_output/TI_align_contigs_1_2/$PREFIXNAME \\ --outSAMtype BAM SortedByCoordinate \\ --outSAMattributes Standard \\ --quantMode GeneCounts \\ --alignIntronMax 5000 done ############################################### ##### ALIGNING PAIRED-END READS TI_1_1 ############################################### STAR --genomeDir results/STAR_output/indexing_contigs_1_2/ --runThreadN 8 --readFilesIn ../Data/Clover_Data/TI_1_1.R1.fastq ../Data/Clover_Data/TI_1_1.R2.fastq --outFileNamePrefix results/STAR_output/TI_align_contigs_1_2/TI_1_1 --outSAMtype BAM SortedByCoordinate --outSAMattributes Standard --quantMode GeneCounts --alignIntronMax 5000 STAR version: 2.7.10a compiled: 2022-01-14T18:50:00-05:00 :/home/dobin/data/STAR/STARcode/STAR.master/source Jun 19 10:24:50 ..... started STAR run Jun 19 10:24:50 ..... loading genome Jun 19 10:24:50 ..... started mapping Jun 19 10:25:03 ..... finished mapping Jun 19 10:25:04 ..... started sorting BAM Jun 19 10:25:18 ..... finished successfully ############################################### ##### ALIGNING PAIRED-END READS TI_1_2 ############################################### STAR --genomeDir results/STAR_output/indexing_contigs_1_2/ --runThreadN 8 --readFilesIn ../Data/Clover_Data/TI_1_2.R1.fastq ../Data/Clover_Data/TI_1_2.R2.fastq --outFileNamePrefix results/STAR_output/TI_align_contigs_1_2/TI_1_2 --outSAMtype BAM SortedByCoordinate --outSAMattributes Standard --quantMode GeneCounts --alignIntronMax 5000 STAR version: 2.7.10a compiled: 2022-01-14T18:50:00-05:00 :/home/dobin/data/STAR/STARcode/STAR.master/source Jun 19 10:25:18 ..... started STAR run Jun 19 10:25:18 ..... loading genome Jun 19 10:25:18 ..... started mapping Jun 19 10:25:30 ..... finished mapping Jun 19 10:25:30 ..... started sorting BAM Jun 19 10:25:33 ..... finished successfully ############################################### ##### ALIGNING PAIRED-END READS TI_1_3 ############################################### STAR --genomeDir results/STAR_output/indexing_contigs_1_2/ --runThreadN 8 --readFilesIn ../Data/Clover_Data/TI_1_3.R1.fastq ../Data/Clover_Data/TI_1_3.R2.fastq --outFileNamePrefix results/STAR_output/TI_align_contigs_1_2/TI_1_3 --outSAMtype BAM SortedByCoordinate --outSAMattributes Standard --quantMode GeneCounts --alignIntronMax 5000 STAR version: 2.7.10a compiled: 2022-01-14T18:50:00-05:00 :/home/dobin/data/STAR/STARcode/STAR.master/source Jun 19 10:25:36 ..... started STAR run Jun 19 10:25:36 ..... loading genome Jun 19 10:25:36 ..... started mapping Jun 19 10:25:50 ..... finished mapping Jun 19 10:25:50 ..... started sorting BAM Jun 19 10:25:51 ..... finished successfully ############################################### ##### ALIGNING PAIRED-END READS TI_2_1 ############################################### STAR --genomeDir results/STAR_output/indexing_contigs_1_2/ --runThreadN 8 --readFilesIn ../Data/Clover_Data/TI_2_1.R1.fastq ../Data/Clover_Data/TI_2_1.R2.fastq --outFileNamePrefix results/STAR_output/TI_align_contigs_1_2/TI_2_1 --outSAMtype BAM SortedByCoordinate --outSAMattributes Standard --quantMode GeneCounts --alignIntronMax 5000 STAR version: 2.7.10a compiled: 2022-01-14T18:50:00-05:00 :/home/dobin/data/STAR/STARcode/STAR.master/source Jun 19 10:25:51 ..... started STAR run Jun 19 10:25:51 ..... loading genome Jun 19 10:25:52 ..... started mapping Jun 19 10:26:06 ..... finished mapping Jun 19 10:26:06 ..... started sorting BAM Jun 19 10:26:09 ..... finished successfully ############################################### ##### ALIGNING PAIRED-END READS TI_2_2 ############################################### STAR --genomeDir results/STAR_output/indexing_contigs_1_2/ --runThreadN 8 --readFilesIn ../Data/Clover_Data/TI_2_2.R1.fastq ../Data/Clover_Data/TI_2_2.R2.fastq --outFileNamePrefix results/STAR_output/TI_align_contigs_1_2/TI_2_2 --outSAMtype BAM SortedByCoordinate --outSAMattributes Standard --quantMode GeneCounts --alignIntronMax 5000 STAR version: 2.7.10a compiled: 2022-01-14T18:50:00-05:00 :/home/dobin/data/STAR/STARcode/STAR.master/source Jun 19 10:26:09 ..... started STAR run Jun 19 10:26:09 ..... loading genome Jun 19 10:26:09 ..... started mapping Jun 19 10:26:23 ..... finished mapping Jun 19 10:26:23 ..... started sorting BAM Jun 19 10:26:31 ..... finished successfully ############################################### ##### ALIGNING PAIRED-END READS TI_2_3 ############################################### STAR --genomeDir results/STAR_output/indexing_contigs_1_2/ --runThreadN 8 --readFilesIn ../Data/Clover_Data/TI_2_3.R1.fastq ../Data/Clover_Data/TI_2_3.R2.fastq --outFileNamePrefix results/STAR_output/TI_align_contigs_1_2/TI_2_3 --outSAMtype BAM SortedByCoordinate --outSAMattributes Standard --quantMode GeneCounts --alignIntronMax 5000 STAR version: 2.7.10a compiled: 2022-01-14T18:50:00-05:00 :/home/dobin/data/STAR/STARcode/STAR.master/source Jun 19 10:26:31 ..... started STAR run Jun 19 10:26:31 ..... loading genome Jun 19 10:26:31 ..... started mapping Jun 19 10:26:46 ..... finished mapping Jun 19 10:26:46 ..... started sorting BAM Jun 19 10:26:49 ..... finished successfully Run quality control on each aligned library with MultiQC . In this way there will be a whole report to compare S10 files and Tienshan files. In [22]: Copied! %% bash multiqc -- outdir results / multiqc_output / TI_STAR_align_1_2 \\ results / STAR_output / TI_align_contigs_1_2 / %%bash multiqc --outdir results/multiqc_output/TI_STAR_align_1_2 \\ results/STAR_output/TI_align_contigs_1_2/ / / / \u001b]8;id=121575;https://multiqc.info\u001b\\ MultiQC \u001b]8;;\u001b\\ \ud83d\udd0d | v1.14 | multiqc | Search path : /work/SamueleSoraggi/Notebooks/results/STAR_output/TI_align_contigs_1_2 | searching | \u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501 100% 36/36 og.o\u2026 | star | Found 6 reports and 6 gene count files | multiqc | Compressing plot data | multiqc | Report : results/multiqc_output/TI_STAR_align_1_2/multiqc_report.html | multiqc | Data : results/multiqc_output/TI_STAR_align_1_2/multiqc_data | multiqc | MultiQC complete In [23]: Copied! %% bash multiqc -- outdir results / multiqc_output / S10_STAR_align_1_2 \\ results / STAR_output / S10_align_contigs_1_2 / %%bash multiqc --outdir results/multiqc_output/S10_STAR_align_1_2 \\ results/STAR_output/S10_align_contigs_1_2/ / / / \u001b]8;id=596946;https://multiqc.info\u001b\\ MultiQC \u001b]8;;\u001b\\ \ud83d\udd0d | v1.14 | multiqc | Search path : /work/SamueleSoraggi/Notebooks/results/STAR_output/S10_align_contigs_1_2 | searching | \u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501 100% 36/36 ut.t\u2026 | star | Found 6 reports and 6 gene count files | multiqc | Compressing plot data | multiqc | Report : results/multiqc_output/S10_STAR_align_1_2/multiqc_report.html | multiqc | Data : results/multiqc_output/S10_STAR_align_1_2/multiqc_data | multiqc | MultiQC complete We merge the outputs of each group of aligned libraries. Here is how the files look like for the Tienshan . In [24]: Copied! ! ls - lh results / STAR_output / TI_align_contigs_1_2 / TI_ *. sortedByCoord . out . bam !ls -lh results/STAR_output/TI_align_contigs_1_2/TI_*.sortedByCoord.out.bam -rw-r--r--. 1 ucloud users 6.6M Jun 19 10:25 results/STAR_output/TI_align_contigs_1_2/TI_1_1Aligned.sortedByCoord.out.bam -rw-r--r--. 1 ucloud users 6.0M Jun 19 10:25 results/STAR_output/TI_align_contigs_1_2/TI_1_2Aligned.sortedByCoord.out.bam -rw-r--r--. 1 ucloud users 7.1M Jun 19 10:25 results/STAR_output/TI_align_contigs_1_2/TI_1_3Aligned.sortedByCoord.out.bam -rw-r--r--. 1 ucloud users 9.6M Jun 19 10:26 results/STAR_output/TI_align_contigs_1_2/TI_2_1Aligned.sortedByCoord.out.bam -rw-r--r--. 1 ucloud users 7.2M Jun 19 10:26 results/STAR_output/TI_align_contigs_1_2/TI_2_2Aligned.sortedByCoord.out.bam -rw-r--r--. 1 ucloud users 8.2M Jun 19 10:26 results/STAR_output/TI_align_contigs_1_2/TI_2_3Aligned.sortedByCoord.out.bam Apply samtools merge In [25]: Copied! %% bash mkdir - p results / STAR_output / TI_align_contigs_1_2_merge / samtools merge - f results / STAR_output / TI_align_contigs_1_2_merge / TI . sorted . bam results / STAR_output / TI_align_contigs_1_2 / TI_ *. sortedByCoord . out . bam %%bash mkdir -p results/STAR_output/TI_align_contigs_1_2_merge/ samtools merge -f results/STAR_output/TI_align_contigs_1_2_merge/TI.sorted.bam results/STAR_output/TI_align_contigs_1_2/TI_*.sortedByCoord.out.bam In [26]: Copied! %% bash mkdir - p results / STAR_output / S10_align_contigs_1_2_merge / samtools merge - f results / STAR_output / S10_align_contigs_1_2_merge / S10 . sorted . bam results / STAR_output / S10_align_contigs_1_2 / S10_ *. sortedByCoord . out . bam %%bash mkdir -p results/STAR_output/S10_align_contigs_1_2_merge/ samtools merge -f results/STAR_output/S10_align_contigs_1_2_merge/S10.sorted.bam results/STAR_output/S10_align_contigs_1_2/S10_*.sortedByCoord.out.bam Index both merging outputs. A file in format bam.bai will appear in their respective folders. In [27]: Copied! %% bash samtools index results / STAR_output / S10_align_contigs_1_2_merge / S10 . sorted . bam %%bash samtools index results/STAR_output/S10_align_contigs_1_2_merge/S10.sorted.bam In [28]: Copied! %% bash samtools index results / STAR_output / TI_align_contigs_1_2_merge / TI . sorted . bam %%bash samtools index results/STAR_output/TI_align_contigs_1_2_merge/TI.sorted.bam","title":"RNA-seq mapping"},{"location":"nb/align.html#wrapping-up","text":"In this exercise, you learnt to align various types of data after performing quality control for raw data. We looked at some of the options for the aligners and at how to use some of the basic samtools manipulation programs. 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padding-left: 5px; padding-right: 5px; } td.linenos .special { color: #000000; background-color: #ffffc0; padding-left: 5px; padding-right: 5px; } span.linenos.special { color: #000000; background-color: #ffffc0; padding-left: 5px; padding-right: 5px; } .highlight-ipynb .hll { background-color: var(--jp-cell-editor-active-background) } .highlight-ipynb { background: var(--jp-cell-editor-background); color: var(--jp-mirror-editor-variable-color) } .highlight-ipynb .c { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment */ .highlight-ipynb .err { color: var(--jp-mirror-editor-error-color) } /* Error */ .highlight-ipynb .k { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword */ .highlight-ipynb .o { color: var(--jp-mirror-editor-operator-color); font-weight: bold } /* Operator */ .highlight-ipynb .p { color: var(--jp-mirror-editor-punctuation-color) } /* Punctuation */ .highlight-ipynb .ch { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.Hashbang */ .highlight-ipynb .cm { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.Multiline */ .highlight-ipynb .cp { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.Preproc */ .highlight-ipynb .cpf { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.PreprocFile */ .highlight-ipynb .c1 { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.Single */ .highlight-ipynb .cs { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.Special */ .highlight-ipynb .kc { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Constant */ .highlight-ipynb .kd { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Declaration */ .highlight-ipynb .kn { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Namespace */ .highlight-ipynb .kp { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Pseudo */ .highlight-ipynb .kr { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Reserved */ .highlight-ipynb .kt { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Type */ .highlight-ipynb .m { color: var(--jp-mirror-editor-number-color) } /* Literal.Number */ .highlight-ipynb .s { color: var(--jp-mirror-editor-string-color) } /* Literal.String */ .highlight-ipynb .ow { color: var(--jp-mirror-editor-operator-color); font-weight: bold } /* Operator.Word */ .highlight-ipynb .w { color: var(--jp-mirror-editor-variable-color) } /* Text.Whitespace */ .highlight-ipynb .mb { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Bin */ .highlight-ipynb .mf { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Float */ .highlight-ipynb .mh { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Hex */ .highlight-ipynb .mi { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Integer */ .highlight-ipynb .mo { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Oct */ .highlight-ipynb .sa { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Affix */ .highlight-ipynb .sb { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Backtick */ .highlight-ipynb .sc { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Char */ .highlight-ipynb .dl { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Delimiter */ .highlight-ipynb .sd { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Doc */ .highlight-ipynb .s2 { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Double */ .highlight-ipynb .se { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Escape */ .highlight-ipynb .sh { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Heredoc */ .highlight-ipynb .si { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Interpol */ .highlight-ipynb .sx { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Other */ .highlight-ipynb .sr { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Regex */ .highlight-ipynb .s1 { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Single */ .highlight-ipynb .ss { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Symbol */ .highlight-ipynb .il { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Integer.Long */ /* This file is taken from the built JupyterLab theme.css Found on share/nbconvert/templates/lab/static Some changes have been made and marked with CHANGE */ .jupyter-wrapper { /* Elevation * * We style box-shadows using Material Design's idea of elevation. These particular numbers are taken from here: * * https://github.com/material-components/material-components-web * https://material-components-web.appspot.com/elevation.html */ --jp-shadow-base-lightness: 0; --jp-shadow-umbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.2 ); --jp-shadow-penumbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.14 ); --jp-shadow-ambient-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.12 ); --jp-elevation-z0: none; --jp-elevation-z1: 0px 2px 1px -1px var(--jp-shadow-umbra-color), 0px 1px 1px 0px var(--jp-shadow-penumbra-color), 0px 1px 3px 0px var(--jp-shadow-ambient-color); --jp-elevation-z2: 0px 3px 1px -2px var(--jp-shadow-umbra-color), 0px 2px 2px 0px var(--jp-shadow-penumbra-color), 0px 1px 5px 0px var(--jp-shadow-ambient-color); --jp-elevation-z4: 0px 2px 4px -1px var(--jp-shadow-umbra-color), 0px 4px 5px 0px var(--jp-shadow-penumbra-color), 0px 1px 10px 0px var(--jp-shadow-ambient-color); --jp-elevation-z6: 0px 3px 5px -1px var(--jp-shadow-umbra-color), 0px 6px 10px 0px var(--jp-shadow-penumbra-color), 0px 1px 18px 0px var(--jp-shadow-ambient-color); --jp-elevation-z8: 0px 5px 5px -3px var(--jp-shadow-umbra-color), 0px 8px 10px 1px var(--jp-shadow-penumbra-color), 0px 3px 14px 2px var(--jp-shadow-ambient-color); --jp-elevation-z12: 0px 7px 8px -4px var(--jp-shadow-umbra-color), 0px 12px 17px 2px var(--jp-shadow-penumbra-color), 0px 5px 22px 4px var(--jp-shadow-ambient-color); --jp-elevation-z16: 0px 8px 10px -5px var(--jp-shadow-umbra-color), 0px 16px 24px 2px var(--jp-shadow-penumbra-color), 0px 6px 30px 5px var(--jp-shadow-ambient-color); --jp-elevation-z20: 0px 10px 13px -6px var(--jp-shadow-umbra-color), 0px 20px 31px 3px var(--jp-shadow-penumbra-color), 0px 8px 38px 7px var(--jp-shadow-ambient-color); --jp-elevation-z24: 0px 11px 15px -7px var(--jp-shadow-umbra-color), 0px 24px 38px 3px var(--jp-shadow-penumbra-color), 0px 9px 46px 8px var(--jp-shadow-ambient-color); /* Borders * * The following variables, specify the visual styling of borders in JupyterLab. */ --jp-border-width: 1px; --jp-border-color0: var(--md-grey-400); --jp-border-color1: var(--md-grey-400); --jp-border-color2: var(--md-grey-300); --jp-border-color3: var(--md-grey-200); --jp-border-radius: 2px; /* UI Fonts * * The UI font CSS variables are used for the typography all of the JupyterLab * user interface elements that are not directly user generated content. * * The font sizing here is done assuming that the body font size of --jp-ui-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-ui-font-scale-factor: 1.2; --jp-ui-font-size0: 0.83333em; --jp-ui-font-size1: 13px; /* Base font size */ --jp-ui-font-size2: 1.2em; --jp-ui-font-size3: 1.44em; --jp-ui-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Use these font colors against the corresponding main layout colors. * In a light theme, these go from dark to light. */ /* Defaults use Material Design specification */ --jp-ui-font-color0: rgba(0, 0, 0, 1); --jp-ui-font-color1: rgba(0, 0, 0, 0.87); --jp-ui-font-color2: rgba(0, 0, 0, 0.54); --jp-ui-font-color3: rgba(0, 0, 0, 0.38); /* * Use these against the brand/accent/warn/error colors. * These will typically go from light to darker, in both a dark and light theme. */ --jp-ui-inverse-font-color0: rgba(255, 255, 255, 1); --jp-ui-inverse-font-color1: rgba(255, 255, 255, 1); --jp-ui-inverse-font-color2: rgba(255, 255, 255, 0.7); --jp-ui-inverse-font-color3: rgba(255, 255, 255, 0.5); /* Content Fonts * * Content font variables are used for typography of user generated content. * * The font sizing here is done assuming that the body font size of --jp-content-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-content-line-height: 1.6; --jp-content-font-scale-factor: 1.2; --jp-content-font-size0: 0.83333em; --jp-content-font-size1: 14px; /* Base font size */ --jp-content-font-size2: 1.2em; --jp-content-font-size3: 1.44em; --jp-content-font-size4: 1.728em; --jp-content-font-size5: 2.0736em; /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-content-presentation-font-size1: 17px; --jp-content-heading-line-height: 1; --jp-content-heading-margin-top: 1.2em; --jp-content-heading-margin-bottom: 0.8em; --jp-content-heading-font-weight: 500; /* Defaults use Material Design specification */ --jp-content-font-color0: rgba(0, 0, 0, 1); --jp-content-font-color1: rgba(0, 0, 0, 0.87); --jp-content-font-color2: rgba(0, 0, 0, 0.54); --jp-content-font-color3: rgba(0, 0, 0, 0.38); --jp-content-link-color: var(--md-blue-700); --jp-content-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Code Fonts * * Code font variables are used for typography of code and other monospaces content. */ --jp-code-font-size: 13px; --jp-code-line-height: 1.3077; /* 17px for 13px base */ --jp-code-padding: 5px; /* 5px for 13px base, codemirror highlighting needs integer px value */ --jp-code-font-family-default: Menlo, Consolas, \"DejaVu Sans Mono\", monospace; --jp-code-font-family: var(--jp-code-font-family-default); /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-code-presentation-font-size: 16px; /* may need to tweak cursor width if you change font size */ --jp-code-cursor-width0: 1.4px; --jp-code-cursor-width1: 2px; --jp-code-cursor-width2: 4px; /* Layout * * The following are the main layout colors use in JupyterLab. In a light * theme these would go from light to dark. */ --jp-layout-color0: white; --jp-layout-color1: white; --jp-layout-color2: var(--md-grey-200); --jp-layout-color3: var(--md-grey-400); --jp-layout-color4: var(--md-grey-600); /* Inverse Layout * * The following are the inverse layout colors use in JupyterLab. In a light * theme these would go from dark to light. */ --jp-inverse-layout-color0: #111111; --jp-inverse-layout-color1: var(--md-grey-900); --jp-inverse-layout-color2: var(--md-grey-800); --jp-inverse-layout-color3: var(--md-grey-700); --jp-inverse-layout-color4: var(--md-grey-600); /* Brand/accent */ --jp-brand-color0: var(--md-blue-900); --jp-brand-color1: var(--md-blue-700); --jp-brand-color2: var(--md-blue-300); --jp-brand-color3: var(--md-blue-100); --jp-brand-color4: var(--md-blue-50); --jp-accent-color0: var(--md-green-900); --jp-accent-color1: var(--md-green-700); --jp-accent-color2: var(--md-green-300); --jp-accent-color3: var(--md-green-100); /* State colors (warn, error, success, info) */ --jp-warn-color0: var(--md-orange-900); --jp-warn-color1: var(--md-orange-700); --jp-warn-color2: var(--md-orange-300); --jp-warn-color3: var(--md-orange-100); --jp-error-color0: var(--md-red-900); --jp-error-color1: var(--md-red-700); --jp-error-color2: var(--md-red-300); --jp-error-color3: var(--md-red-100); --jp-success-color0: var(--md-green-900); --jp-success-color1: var(--md-green-700); --jp-success-color2: var(--md-green-300); --jp-success-color3: var(--md-green-100); --jp-info-color0: var(--md-cyan-900); --jp-info-color1: var(--md-cyan-700); --jp-info-color2: var(--md-cyan-300); --jp-info-color3: var(--md-cyan-100); /* Cell specific styles */ --jp-cell-padding: 5px; --jp-cell-collapser-width: 8px; --jp-cell-collapser-min-height: 20px; --jp-cell-collapser-not-active-hover-opacity: 0.6; --jp-cell-editor-background: var(--md-grey-100); --jp-cell-editor-border-color: var(--md-grey-300); --jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-cell-editor-active-background: var(--jp-layout-color0); --jp-cell-editor-active-border-color: var(--jp-brand-color1); --jp-cell-prompt-width: 64px; --jp-cell-prompt-font-family: var(--jp-code-font-family-default); --jp-cell-prompt-letter-spacing: 0px; --jp-cell-prompt-opacity: 1; --jp-cell-prompt-not-active-opacity: 0.5; --jp-cell-prompt-not-active-font-color: var(--md-grey-700); /* A custom blend of MD grey and blue 600 * See https://meyerweb.com/eric/tools/color-blend/#546E7A:1E88E5:5:hex */ --jp-cell-inprompt-font-color: #307fc1; /* A custom blend of MD grey and orange 600 * https://meyerweb.com/eric/tools/color-blend/#546E7A:F4511E:5:hex */ --jp-cell-outprompt-font-color: #bf5b3d; /* Notebook specific styles */ --jp-notebook-padding: 10px; --jp-notebook-select-background: var(--jp-layout-color1); --jp-notebook-multiselected-color: var(--md-blue-50); /* The scroll padding is calculated to fill enough space at the bottom of the notebook to show one single-line cell (with appropriate padding) at the top when the notebook is scrolled all the way to the bottom. We also subtract one pixel so that no scrollbar appears if we have just one single-line cell in the notebook. This padding is to enable a 'scroll past end' feature in a notebook. */ --jp-notebook-scroll-padding: calc( 100% - var(--jp-code-font-size) * var(--jp-code-line-height) - var(--jp-code-padding) - var(--jp-cell-padding) - 1px ); /* Rendermime styles */ --jp-rendermime-error-background: #fdd; --jp-rendermime-table-row-background: var(--md-grey-100); --jp-rendermime-table-row-hover-background: var(--md-light-blue-50); /* Dialog specific styles */ --jp-dialog-background: rgba(0, 0, 0, 0.25); /* Console specific styles */ --jp-console-padding: 10px; /* Toolbar specific styles */ --jp-toolbar-border-color: var(--jp-border-color1); --jp-toolbar-micro-height: 8px; --jp-toolbar-background: var(--jp-layout-color1); --jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, 0.24); --jp-toolbar-header-margin: 4px 4px 0px 4px; --jp-toolbar-active-background: var(--md-grey-300); /* Statusbar specific styles */ --jp-statusbar-height: 24px; /* Input field styles */ --jp-input-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-input-active-background: var(--jp-layout-color1); --jp-input-hover-background: var(--jp-layout-color1); --jp-input-background: var(--md-grey-100); --jp-input-border-color: var(--jp-border-color1); --jp-input-active-border-color: var(--jp-brand-color1); --jp-input-active-box-shadow-color: rgba(19, 124, 189, 0.3); /* General editor styles */ --jp-editor-selected-background: #d9d9d9; --jp-editor-selected-focused-background: #d7d4f0; --jp-editor-cursor-color: var(--jp-ui-font-color0); /* Code mirror specific styles */ --jp-mirror-editor-keyword-color: #008000; --jp-mirror-editor-atom-color: #88f; --jp-mirror-editor-number-color: #080; --jp-mirror-editor-def-color: #00f; --jp-mirror-editor-variable-color: var(--md-grey-900); --jp-mirror-editor-variable-2-color: #05a; --jp-mirror-editor-variable-3-color: #085; --jp-mirror-editor-punctuation-color: #05a; --jp-mirror-editor-property-color: #05a; --jp-mirror-editor-operator-color: #aa22ff; --jp-mirror-editor-comment-color: #408080; --jp-mirror-editor-string-color: #ba2121; --jp-mirror-editor-string-2-color: #708; --jp-mirror-editor-meta-color: #aa22ff; --jp-mirror-editor-qualifier-color: #555; --jp-mirror-editor-builtin-color: #008000; --jp-mirror-editor-bracket-color: #997; --jp-mirror-editor-tag-color: #170; --jp-mirror-editor-attribute-color: #00c; --jp-mirror-editor-header-color: blue; --jp-mirror-editor-quote-color: #090; --jp-mirror-editor-link-color: #00c; --jp-mirror-editor-error-color: #f00; --jp-mirror-editor-hr-color: #999; /* Vega extension styles */ --jp-vega-background: white; /* Sidebar-related styles */ --jp-sidebar-min-width: 250px; /* Search-related styles */ --jp-search-toggle-off-opacity: 0.5; --jp-search-toggle-hover-opacity: 0.8; --jp-search-toggle-on-opacity: 1; --jp-search-selected-match-background-color: rgb(245, 200, 0); --jp-search-selected-match-color: black; --jp-search-unselected-match-background-color: var( --jp-inverse-layout-color0 ); --jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0); /* Icon colors that work well with light or dark backgrounds */ --jp-icon-contrast-color0: var(--md-purple-600); --jp-icon-contrast-color1: var(--md-green-600); --jp-icon-contrast-color2: var(--md-pink-600); --jp-icon-contrast-color3: var(--md-blue-600); } [data-md-color-scheme=\"slate\"] .jupyter-wrapper { /* Elevation * * We style box-shadows using Material Design's idea of elevation. These particular numbers are taken from here: * * https://github.com/material-components/material-components-web * https://material-components-web.appspot.com/elevation.html */ /* The dark theme shadows need a bit of work, but this will probably also require work on the core layout * colors used in the theme as well. */ --jp-shadow-base-lightness: 32; --jp-shadow-umbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.2 ); --jp-shadow-penumbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.14 ); --jp-shadow-ambient-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.12 ); --jp-elevation-z0: none; --jp-elevation-z1: 0px 2px 1px -1px var(--jp-shadow-umbra-color), 0px 1px 1px 0px var(--jp-shadow-penumbra-color), 0px 1px 3px 0px var(--jp-shadow-ambient-color); --jp-elevation-z2: 0px 3px 1px -2px var(--jp-shadow-umbra-color), 0px 2px 2px 0px var(--jp-shadow-penumbra-color), 0px 1px 5px 0px var(--jp-shadow-ambient-color); --jp-elevation-z4: 0px 2px 4px -1px var(--jp-shadow-umbra-color), 0px 4px 5px 0px var(--jp-shadow-penumbra-color), 0px 1px 10px 0px var(--jp-shadow-ambient-color); --jp-elevation-z6: 0px 3px 5px -1px var(--jp-shadow-umbra-color), 0px 6px 10px 0px var(--jp-shadow-penumbra-color), 0px 1px 18px 0px var(--jp-shadow-ambient-color); --jp-elevation-z8: 0px 5px 5px -3px var(--jp-shadow-umbra-color), 0px 8px 10px 1px var(--jp-shadow-penumbra-color), 0px 3px 14px 2px var(--jp-shadow-ambient-color); --jp-elevation-z12: 0px 7px 8px -4px var(--jp-shadow-umbra-color), 0px 12px 17px 2px var(--jp-shadow-penumbra-color), 0px 5px 22px 4px var(--jp-shadow-ambient-color); --jp-elevation-z16: 0px 8px 10px -5px var(--jp-shadow-umbra-color), 0px 16px 24px 2px var(--jp-shadow-penumbra-color), 0px 6px 30px 5px var(--jp-shadow-ambient-color); --jp-elevation-z20: 0px 10px 13px -6px var(--jp-shadow-umbra-color), 0px 20px 31px 3px var(--jp-shadow-penumbra-color), 0px 8px 38px 7px var(--jp-shadow-ambient-color); --jp-elevation-z24: 0px 11px 15px -7px var(--jp-shadow-umbra-color), 0px 24px 38px 3px var(--jp-shadow-penumbra-color), 0px 9px 46px 8px var(--jp-shadow-ambient-color); /* Borders * * The following variables, specify the visual styling of borders in JupyterLab. */ --jp-border-width: 1px; --jp-border-color0: var(--md-grey-700); --jp-border-color1: var(--md-grey-700); --jp-border-color2: var(--md-grey-800); --jp-border-color3: var(--md-grey-900); --jp-border-radius: 2px; /* UI Fonts * * The UI font CSS variables are used for the typography all of the JupyterLab * user interface elements that are not directly user generated content. * * The font sizing here is done assuming that the body font size of --jp-ui-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-ui-font-scale-factor: 1.2; --jp-ui-font-size0: 0.83333em; --jp-ui-font-size1: 13px; /* Base font size */ --jp-ui-font-size2: 1.2em; --jp-ui-font-size3: 1.44em; --jp-ui-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Use these font colors against the corresponding main layout colors. * In a light theme, these go from dark to light. */ /* Defaults use Material Design specification */ --jp-ui-font-color0: rgba(255, 255, 255, 1); --jp-ui-font-color1: rgba(255, 255, 255, 0.87); --jp-ui-font-color2: rgba(255, 255, 255, 0.54); --jp-ui-font-color3: rgba(255, 255, 255, 0.38); /* * Use these against the brand/accent/warn/error colors. * These will typically go from light to darker, in both a dark and light theme. */ --jp-ui-inverse-font-color0: rgba(0, 0, 0, 1); --jp-ui-inverse-font-color1: rgba(0, 0, 0, 0.8); --jp-ui-inverse-font-color2: rgba(0, 0, 0, 0.5); --jp-ui-inverse-font-color3: rgba(0, 0, 0, 0.3); /* Content Fonts * * Content font variables are used for typography of user generated content. * * The font sizing here is done assuming that the body font size of --jp-content-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-content-line-height: 1.6; --jp-content-font-scale-factor: 1.2; --jp-content-font-size0: 0.83333em; --jp-content-font-size1: 14px; /* Base font size */ --jp-content-font-size2: 1.2em; --jp-content-font-size3: 1.44em; --jp-content-font-size4: 1.728em; --jp-content-font-size5: 2.0736em; /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-content-presentation-font-size1: 17px; --jp-content-heading-line-height: 1; --jp-content-heading-margin-top: 1.2em; --jp-content-heading-margin-bottom: 0.8em; --jp-content-heading-font-weight: 500; /* Defaults use Material Design specification */ --jp-content-font-color0: rgba(255, 255, 255, 1); --jp-content-font-color1: rgba(255, 255, 255, 1); --jp-content-font-color2: rgba(255, 255, 255, 0.7); --jp-content-font-color3: rgba(255, 255, 255, 0.5); --jp-content-link-color: var(--md-blue-300); --jp-content-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Code Fonts * * Code font variables are used for typography of code and other monospaces content. */ --jp-code-font-size: 13px; --jp-code-line-height: 1.3077; /* 17px for 13px base */ --jp-code-padding: 5px; /* 5px for 13px base, codemirror highlighting needs integer px value */ --jp-code-font-family-default: Menlo, Consolas, \"DejaVu Sans Mono\", monospace; --jp-code-font-family: var(--jp-code-font-family-default); /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-code-presentation-font-size: 16px; /* may need to tweak cursor width if you change font size */ --jp-code-cursor-width0: 1.4px; --jp-code-cursor-width1: 2px; --jp-code-cursor-width2: 4px; /* Layout * * The following are the main layout colors use in JupyterLab. In a light * theme these would go from light to dark. */ --jp-layout-color0: #111111; --jp-layout-color1: var(--md-grey-900); --jp-layout-color2: var(--md-grey-800); --jp-layout-color3: var(--md-grey-700); --jp-layout-color4: var(--md-grey-600); /* Inverse Layout * * The following are the inverse layout colors use in JupyterLab. In a light * theme these would go from dark to light. */ --jp-inverse-layout-color0: white; --jp-inverse-layout-color1: white; --jp-inverse-layout-color2: var(--md-grey-200); --jp-inverse-layout-color3: var(--md-grey-400); --jp-inverse-layout-color4: var(--md-grey-600); /* Brand/accent */ --jp-brand-color0: var(--md-blue-700); --jp-brand-color1: var(--md-blue-500); --jp-brand-color2: var(--md-blue-300); --jp-brand-color3: var(--md-blue-100); --jp-brand-color4: var(--md-blue-50); --jp-accent-color0: var(--md-green-700); --jp-accent-color1: var(--md-green-500); --jp-accent-color2: var(--md-green-300); --jp-accent-color3: var(--md-green-100); /* State colors (warn, error, success, info) */ --jp-warn-color0: var(--md-orange-700); --jp-warn-color1: var(--md-orange-500); --jp-warn-color2: var(--md-orange-300); --jp-warn-color3: var(--md-orange-100); --jp-error-color0: var(--md-red-700); --jp-error-color1: var(--md-red-500); --jp-error-color2: var(--md-red-300); --jp-error-color3: var(--md-red-100); --jp-success-color0: var(--md-green-700); --jp-success-color1: var(--md-green-500); --jp-success-color2: var(--md-green-300); --jp-success-color3: var(--md-green-100); --jp-info-color0: var(--md-cyan-700); --jp-info-color1: var(--md-cyan-500); --jp-info-color2: var(--md-cyan-300); --jp-info-color3: var(--md-cyan-100); /* Cell specific styles */ --jp-cell-padding: 5px; --jp-cell-collapser-width: 8px; --jp-cell-collapser-min-height: 20px; --jp-cell-collapser-not-active-hover-opacity: 0.6; --jp-cell-editor-background: var(--jp-layout-color1); --jp-cell-editor-border-color: var(--md-grey-700); --jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-cell-editor-active-background: var(--jp-layout-color0); --jp-cell-editor-active-border-color: var(--jp-brand-color1); --jp-cell-prompt-width: 64px; --jp-cell-prompt-font-family: var(--jp-code-font-family-default); --jp-cell-prompt-letter-spacing: 0px; --jp-cell-prompt-opacity: 1; --jp-cell-prompt-not-active-opacity: 1; --jp-cell-prompt-not-active-font-color: var(--md-grey-300); /* A custom blend of MD grey and blue 600 * See https://meyerweb.com/eric/tools/color-blend/#546E7A:1E88E5:5:hex */ --jp-cell-inprompt-font-color: #307fc1; /* A custom blend of MD grey and orange 600 * https://meyerweb.com/eric/tools/color-blend/#546E7A:F4511E:5:hex */ --jp-cell-outprompt-font-color: #bf5b3d; /* Notebook specific styles */ --jp-notebook-padding: 10px; --jp-notebook-select-background: var(--jp-layout-color1); --jp-notebook-multiselected-color: rgba(33, 150, 243, 0.24); /* The scroll padding is calculated to fill enough space at the bottom of the notebook to show one single-line cell (with appropriate padding) at the top when the notebook is scrolled all the way to the bottom. We also subtract one pixel so that no scrollbar appears if we have just one single-line cell in the notebook. This padding is to enable a 'scroll past end' feature in a notebook. */ --jp-notebook-scroll-padding: calc( 100% - var(--jp-code-font-size) * var(--jp-code-line-height) - var(--jp-code-padding) - var(--jp-cell-padding) - 1px ); /* Rendermime styles */ --jp-rendermime-error-background: rgba(244, 67, 54, 0.28); --jp-rendermime-table-row-background: var(--md-grey-900); --jp-rendermime-table-row-hover-background: rgba(3, 169, 244, 0.2); /* Dialog specific styles */ --jp-dialog-background: rgba(0, 0, 0, 0.6); /* Console specific styles */ --jp-console-padding: 10px; /* Toolbar specific styles */ --jp-toolbar-border-color: var(--jp-border-color2); --jp-toolbar-micro-height: 8px; --jp-toolbar-background: var(--jp-layout-color1); --jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, 0.8); --jp-toolbar-header-margin: 4px 4px 0px 4px; --jp-toolbar-active-background: var(--jp-layout-color0); /* Statusbar specific styles */ --jp-statusbar-height: 24px; /* Input field styles */ --jp-input-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-input-active-background: var(--jp-layout-color0); --jp-input-hover-background: var(--jp-layout-color2); --jp-input-background: var(--md-grey-800); --jp-input-border-color: var(--jp-border-color1); --jp-input-active-border-color: var(--jp-brand-color1); --jp-input-active-box-shadow-color: rgba(19, 124, 189, 0.3); /* General editor styles */ --jp-editor-selected-background: var(--jp-layout-color2); --jp-editor-selected-focused-background: rgba(33, 150, 243, 0.24); --jp-editor-cursor-color: var(--jp-ui-font-color0); /* Code mirror specific styles */ --jp-mirror-editor-keyword-color: var(--md-green-500); --jp-mirror-editor-atom-color: var(--md-blue-300); --jp-mirror-editor-number-color: var(--md-green-400); --jp-mirror-editor-def-color: var(--md-blue-600); --jp-mirror-editor-variable-color: var(--md-grey-300); --jp-mirror-editor-variable-2-color: var(--md-blue-400); --jp-mirror-editor-variable-3-color: var(--md-green-600); --jp-mirror-editor-punctuation-color: var(--md-blue-400); --jp-mirror-editor-property-color: var(--md-blue-400); --jp-mirror-editor-operator-color: #aa22ff; --jp-mirror-editor-comment-color: #408080; --jp-mirror-editor-string-color: #ff7070; --jp-mirror-editor-string-2-color: var(--md-purple-300); --jp-mirror-editor-meta-color: #aa22ff; --jp-mirror-editor-qualifier-color: #555; --jp-mirror-editor-builtin-color: var(--md-green-600); --jp-mirror-editor-bracket-color: #997; --jp-mirror-editor-tag-color: var(--md-green-700); --jp-mirror-editor-attribute-color: var(--md-blue-700); --jp-mirror-editor-header-color: var(--md-blue-500); --jp-mirror-editor-quote-color: var(--md-green-300); --jp-mirror-editor-link-color: var(--md-blue-700); --jp-mirror-editor-error-color: #f00; --jp-mirror-editor-hr-color: #999; /* Vega extension styles */ --jp-vega-background: var(--md-grey-400); /* Sidebar-related styles */ --jp-sidebar-min-width: 250px; /* Search-related styles */ --jp-search-toggle-off-opacity: 0.6; --jp-search-toggle-hover-opacity: 0.8; --jp-search-toggle-on-opacity: 1; --jp-search-selected-match-background-color: rgb(255, 225, 0); --jp-search-selected-match-color: black; --jp-search-unselected-match-background-color: var( --jp-inverse-layout-color0 ); --jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0); /* scrollbar related styles. Supports every browser except Edge. */ /* colors based on JetBrain's Darcula theme */ --jp-scrollbar-background-color: #3f4244; --jp-scrollbar-thumb-color: 88, 96, 97; /* need to specify thumb color as an RGB triplet */ --jp-scrollbar-endpad: 3px; /* the minimum gap between the thumb and the ends of a scrollbar */ /* hacks for setting the thumb shape. These do nothing in Firefox */ --jp-scrollbar-thumb-margin: 3.5px; /* the space in between the sides of the thumb and the track */ --jp-scrollbar-thumb-radius: 9px; /* set to a large-ish value for rounded endcaps on the thumb */ /* Icon colors that work well with light or dark backgrounds */ --jp-icon-contrast-color0: var(--md-purple-600); --jp-icon-contrast-color1: var(--md-green-600); --jp-icon-contrast-color2: var(--md-pink-600); --jp-icon-contrast-color3: var(--md-blue-600); } :root{--md-red-50: #ffebee;--md-red-100: #ffcdd2;--md-red-200: #ef9a9a;--md-red-300: #e57373;--md-red-400: #ef5350;--md-red-500: #f44336;--md-red-600: #e53935;--md-red-700: #d32f2f;--md-red-800: #c62828;--md-red-900: #b71c1c;--md-red-A100: #ff8a80;--md-red-A200: #ff5252;--md-red-A400: #ff1744;--md-red-A700: #d50000;--md-pink-50: #fce4ec;--md-pink-100: #f8bbd0;--md-pink-200: #f48fb1;--md-pink-300: #f06292;--md-pink-400: #ec407a;--md-pink-500: #e91e63;--md-pink-600: #d81b60;--md-pink-700: #c2185b;--md-pink-800: #ad1457;--md-pink-900: #880e4f;--md-pink-A100: #ff80ab;--md-pink-A200: #ff4081;--md-pink-A400: #f50057;--md-pink-A700: #c51162;--md-purple-50: #f3e5f5;--md-purple-100: #e1bee7;--md-purple-200: #ce93d8;--md-purple-300: #ba68c8;--md-purple-400: #ab47bc;--md-purple-500: #9c27b0;--md-purple-600: #8e24aa;--md-purple-700: #7b1fa2;--md-purple-800: #6a1b9a;--md-purple-900: #4a148c;--md-purple-A100: #ea80fc;--md-purple-A200: #e040fb;--md-purple-A400: #d500f9;--md-purple-A700: #aa00ff;--md-deep-purple-50: #ede7f6;--md-deep-purple-100: #d1c4e9;--md-deep-purple-200: #b39ddb;--md-deep-purple-300: #9575cd;--md-deep-purple-400: #7e57c2;--md-deep-purple-500: #673ab7;--md-deep-purple-600: #5e35b1;--md-deep-purple-700: #512da8;--md-deep-purple-800: #4527a0;--md-deep-purple-900: #311b92;--md-deep-purple-A100: #b388ff;--md-deep-purple-A200: #7c4dff;--md-deep-purple-A400: #651fff;--md-deep-purple-A700: #6200ea;--md-indigo-50: #e8eaf6;--md-indigo-100: #c5cae9;--md-indigo-200: #9fa8da;--md-indigo-300: #7986cb;--md-indigo-400: #5c6bc0;--md-indigo-500: #3f51b5;--md-indigo-600: #3949ab;--md-indigo-700: #303f9f;--md-indigo-800: #283593;--md-indigo-900: #1a237e;--md-indigo-A100: #8c9eff;--md-indigo-A200: #536dfe;--md-indigo-A400: #3d5afe;--md-indigo-A700: #304ffe;--md-blue-50: #e3f2fd;--md-blue-100: #bbdefb;--md-blue-200: #90caf9;--md-blue-300: #64b5f6;--md-blue-400: #42a5f5;--md-blue-500: #2196f3;--md-blue-600: #1e88e5;--md-blue-700: #1976d2;--md-blue-800: #1565c0;--md-blue-900: #0d47a1;--md-blue-A100: #82b1ff;--md-blue-A200: #448aff;--md-blue-A400: #2979ff;--md-blue-A700: #2962ff;--md-light-blue-50: #e1f5fe;--md-light-blue-100: #b3e5fc;--md-light-blue-200: #81d4fa;--md-light-blue-300: #4fc3f7;--md-light-blue-400: #29b6f6;--md-light-blue-500: #03a9f4;--md-light-blue-600: #039be5;--md-light-blue-700: #0288d1;--md-light-blue-800: #0277bd;--md-light-blue-900: #01579b;--md-light-blue-A100: #80d8ff;--md-light-blue-A200: #40c4ff;--md-light-blue-A400: #00b0ff;--md-light-blue-A700: #0091ea;--md-cyan-50: #e0f7fa;--md-cyan-100: #b2ebf2;--md-cyan-200: #80deea;--md-cyan-300: #4dd0e1;--md-cyan-400: #26c6da;--md-cyan-500: #00bcd4;--md-cyan-600: #00acc1;--md-cyan-700: #0097a7;--md-cyan-800: #00838f;--md-cyan-900: #006064;--md-cyan-A100: #84ffff;--md-cyan-A200: #18ffff;--md-cyan-A400: #00e5ff;--md-cyan-A700: #00b8d4;--md-teal-50: #e0f2f1;--md-teal-100: #b2dfdb;--md-teal-200: #80cbc4;--md-teal-300: #4db6ac;--md-teal-400: #26a69a;--md-teal-500: #009688;--md-teal-600: #00897b;--md-teal-700: #00796b;--md-teal-800: #00695c;--md-teal-900: #004d40;--md-teal-A100: #a7ffeb;--md-teal-A200: #64ffda;--md-teal-A400: #1de9b6;--md-teal-A700: #00bfa5;--md-green-50: #e8f5e9;--md-green-100: #c8e6c9;--md-green-200: #a5d6a7;--md-green-300: #81c784;--md-green-400: #66bb6a;--md-green-500: #4caf50;--md-green-600: #43a047;--md-green-700: #388e3c;--md-green-800: #2e7d32;--md-green-900: #1b5e20;--md-green-A100: #b9f6ca;--md-green-A200: #69f0ae;--md-green-A400: #00e676;--md-green-A700: #00c853;--md-light-green-50: #f1f8e9;--md-light-green-100: #dcedc8;--md-light-green-200: #c5e1a5;--md-light-green-300: #aed581;--md-light-green-400: #9ccc65;--md-light-green-500: #8bc34a;--md-light-green-600: #7cb342;--md-light-green-700: #689f38;--md-light-green-800: #558b2f;--md-light-green-900: #33691e;--md-light-green-A100: #ccff90;--md-light-green-A200: #b2ff59;--md-light-green-A400: #76ff03;--md-light-green-A700: #64dd17;--md-lime-50: #f9fbe7;--md-lime-100: #f0f4c3;--md-lime-200: #e6ee9c;--md-lime-300: #dce775;--md-lime-400: #d4e157;--md-lime-500: #cddc39;--md-lime-600: #c0ca33;--md-lime-700: #afb42b;--md-lime-800: #9e9d24;--md-lime-900: #827717;--md-lime-A100: #f4ff81;--md-lime-A200: #eeff41;--md-lime-A400: #c6ff00;--md-lime-A700: #aeea00;--md-yellow-50: #fffde7;--md-yellow-100: #fff9c4;--md-yellow-200: #fff59d;--md-yellow-300: #fff176;--md-yellow-400: #ffee58;--md-yellow-500: #ffeb3b;--md-yellow-600: #fdd835;--md-yellow-700: #fbc02d;--md-yellow-800: #f9a825;--md-yellow-900: #f57f17;--md-yellow-A100: #ffff8d;--md-yellow-A200: #ffff00;--md-yellow-A400: #ffea00;--md-yellow-A700: #ffd600;--md-amber-50: #fff8e1;--md-amber-100: #ffecb3;--md-amber-200: #ffe082;--md-amber-300: #ffd54f;--md-amber-400: #ffca28;--md-amber-500: #ffc107;--md-amber-600: #ffb300;--md-amber-700: #ffa000;--md-amber-800: #ff8f00;--md-amber-900: #ff6f00;--md-amber-A100: #ffe57f;--md-amber-A200: #ffd740;--md-amber-A400: #ffc400;--md-amber-A700: #ffab00;--md-orange-50: #fff3e0;--md-orange-100: #ffe0b2;--md-orange-200: #ffcc80;--md-orange-300: #ffb74d;--md-orange-400: #ffa726;--md-orange-500: #ff9800;--md-orange-600: #fb8c00;--md-orange-700: #f57c00;--md-orange-800: #ef6c00;--md-orange-900: #e65100;--md-orange-A100: #ffd180;--md-orange-A200: #ffab40;--md-orange-A400: #ff9100;--md-orange-A700: #ff6d00;--md-deep-orange-50: #fbe9e7;--md-deep-orange-100: #ffccbc;--md-deep-orange-200: #ffab91;--md-deep-orange-300: #ff8a65;--md-deep-orange-400: #ff7043;--md-deep-orange-500: 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sourceMappingURL=mkdocs-jupyter.css.map*/ init_mathjax = function() { if (window.MathJax) { // MathJax loaded MathJax.Hub.Config({ TeX: { equationNumbers: { autoNumber: \"AMS\", useLabelIds: true } }, tex2jax: { inlineMath: [ ['$','$'], [\"\\\\(\",\"\\\\)\"] ], displayMath: [ ['$$','$$'], [\"\\\\[\",\"\\\\]\"] ], processEscapes: true, processEnvironments: true }, displayAlign: 'center', CommonHTML: { linebreaks: { automatic: true } } }); MathJax.Hub.Queue([\"Typeset\", MathJax.Hub]); } } init_mathjax(); RNA-seq differential gene expression This tutorial will cover the steps for performing Differential Gene Expression on the RNA-seq data obtained from Galaxy. At the end of this tutorial you will be able to use R to Perform preliminary analyses of the RNA-seq results Find differentially expressed genes between two conditions using edgeR The present tutorial, like the rest of the course material, is available at our open-source github repository . To use this notebook, use the NGS (R) kernel that contains the packages. Choose it by selecting Kernel -> Change Kernel in the menu on top of the window. A few introductory points to run this notebook (click to show): To use this notebook, use the NGS (R) kernel that contains the packages. Choose it by selecting Kernel -> Change Kernel in the menu on top of the window. In this notebook you will use only R commands On some computers, you might see the result of the commands once they are done running. This means you will wait some time while the computer is crunching, and only afterwards you will see the result of the command you have executed You can run the code in each cell by clicking on the run cell button, or by pressing Shift + Enter . When the code is done running, a small green check sign will appear on the left side You need to run the cells in sequential order, please do not run a cell until the one above finished running and do not skip any cells Each cell contains a short description of the code and the output you should get. Please try not to focus on understanding the code for each command in too much detail, but rather try to focus on the output You can create new code cells by pressing + in the Menu bar above. Load the necessary R libraries In [1]: Copied! library ( VennDiagram ) library ( dplyr ) library ( tibble ) library ( formattable ) library ( mixOmics ) library ( pheatmap ) library ( edgeR ) library(VennDiagram) library(dplyr) library(tibble) library(formattable) library(mixOmics) library(pheatmap) library(edgeR) Warning message: \u201cpackage \u2018VennDiagram\u2019 was built under R version 4.1.3\u201d Loading required package: grid Loading required package: futile.logger Warning message: \u201cpackage \u2018futile.logger\u2019 was built under R version 4.1.3\u201d Warning message: \u201cpackage \u2018dplyr\u2019 was built under R version 4.1.3\u201d Attaching package: \u2018dplyr\u2019 The following objects are masked from \u2018package:stats\u2019: filter, lag The following objects are masked from \u2018package:base\u2019: intersect, setdiff, setequal, union Warning message: \u201cpackage \u2018tibble\u2019 was built under R version 4.1.3\u201d Warning message: \u201cpackage \u2018formattable\u2019 was built under R version 4.1.3\u201d Loading required package: MASS Warning message: \u201cpackage \u2018MASS\u2019 was built under R version 4.1.3\u201d Attaching package: \u2018MASS\u2019 The following object is masked from \u2018package:formattable\u2019: area The following object is masked from \u2018package:dplyr\u2019: select Loading required package: lattice Warning message: \u201cpackage \u2018lattice\u2019 was built under R version 4.1.3\u201d Loading required package: ggplot2 Warning message: \u201cpackage \u2018ggplot2\u2019 was built under R version 4.1.3\u201d Loaded mixOmics 6.17.26 Thank you for using mixOmics! Tutorials: http://mixomics.org Bookdown vignette: https://mixomicsteam.github.io/Bookdown Questions, issues: Follow the prompts at http://mixomics.org/contact-us Cite us: citation('mixOmics') Warning message: \u201cpackage \u2018pheatmap\u2019 was built under R version 4.1.3\u201d Warning message: \u201cpackage \u2018edgeR\u2019 was built under R version 4.1.3\u201d Loading required package: limma Warning message: \u201cpackage \u2018limma\u2019 was built under R version 4.1.3\u201d EdgeR - Data filtering and Normalization \u00b6 We will use the edgeR package to test for differentially expressed genes between our Control and Treatment Samples. You can read more about the edgeR package and its functionalities here . This exercise largely follows their user manual. File processing \u00b6 The data for this exercise comes from the 12 tabular files with Reads per Gene counts generated by STAR Mapping in the raw-data alignment part of this course. We want to create a table where each column is a sample, and the content of the table are the read counts from STAR. We must merge the 12 files with Reads per Gene information into a single file. If you aligned datasets in the first notebook with jupyterlab, then you will find the files using the following command: In [2]: Copied! samples <- sort ( system ( \"find results/STAR_output/*_align_contigs_1_2 -name \\\"*ReadsPerGene.out.tab\\\"\" , intern = TRUE )) print ( samples ) Read_counts <- do.call ( cbind , lapply ( samples , function ( x ) read.delim ( file = x , header = FALSE ))) samples < - sort(system(\"find results/STAR_output/*_align_contigs_1_2 -name \\\"*ReadsPerGene.out.tab\\\"\", intern=TRUE)) print(samples) Read_counts < - do.call(cbind, lapply(samples, function(x) read.delim(file=x, header = FALSE))) [1] \"results/STAR_output/S10_align_contigs_1_2/S10_1_1ReadsPerGene.out.tab\" [2] \"results/STAR_output/S10_align_contigs_1_2/S10_1_2ReadsPerGene.out.tab\" [3] \"results/STAR_output/S10_align_contigs_1_2/S10_1_3ReadsPerGene.out.tab\" [4] \"results/STAR_output/S10_align_contigs_1_2/S10_2_1ReadsPerGene.out.tab\" [5] \"results/STAR_output/S10_align_contigs_1_2/S10_2_2ReadsPerGene.out.tab\" [6] \"results/STAR_output/S10_align_contigs_1_2/S10_2_3ReadsPerGene.out.tab\" [7] \"results/STAR_output/TI_align_contigs_1_2/TI_1_1ReadsPerGene.out.tab\" [8] \"results/STAR_output/TI_align_contigs_1_2/TI_1_2ReadsPerGene.out.tab\" [9] \"results/STAR_output/TI_align_contigs_1_2/TI_1_3ReadsPerGene.out.tab\" [10] \"results/STAR_output/TI_align_contigs_1_2/TI_2_1ReadsPerGene.out.tab\" [11] \"results/STAR_output/TI_align_contigs_1_2/TI_2_2ReadsPerGene.out.tab\" [12] \"results/STAR_output/TI_align_contigs_1_2/TI_2_3ReadsPerGene.out.tab\" If you aligned datasets interactively with Galaxy , then you will need to create the folder tabular_files into the results folder copy the tabular files from STAR into the created folder. Each file must have the sample name and end by ReadsPerGene.out.tab . For example S10_1_1ReadsPerGene.out.tab run the following commands removing the # symbol In [ ]: Copied! #samples <- sort(system(\"find results/tabular_files/ -name \\\"*ReadsPerGene.out.tab\\\"\", intern=TRUE)) #print(samples) #Read_counts <- do.call(cbind, lapply(samples, function(x) read.delim(file=x, header = FALSE))) #samples < - sort(system(\"find results/tabular_files/ -name \\\"*ReadsPerGene.out.tab\\\"\", intern=TRUE)) #print(samples) #Read_counts < - do.call(cbind, lapply(samples, function(x) read.delim(file=x, header = FALSE))) The data frame has genes as rows and samples as columns and stores the gene expression counts (value representing the total number of sequence reads that originated from a particular gene in a sample) for each of the 12 samples. This data frame should have 12 columns and 366 rows. In [3]: Copied! rownames ( Read_counts ) <- Read_counts [, 1 ] Read_counts <- Read_counts [ c ( 5 : nrow ( Read_counts )), c ( seq ( 2 , 46 , by = 4 ))] colnames ( Read_counts ) <- c ( \"S10_1_1\" , \"S10_1_2\" , \"S10_1_3\" , \"S10_2_1\" , \"S10_2_2\" , \"S10_2_3\" , \"TI_1_1\" , \"TI_1_2\" , \"TI_1_3\" , \"TI_2_1\" , \"TI_2_2\" , \"TI_2_3\" ) head ( Read_counts , n = 10 ) dim ( Read_counts ) # dimensions of the data frame rownames(Read_counts) < - Read_counts[,1] Read_counts < - Read_counts[c(5:nrow(Read_counts)), c(seq(2, 46, by=4))] colnames(Read_counts) < - c(\"S10_1_1\", \"S10_1_2\", \"S10_1_3\", \"S10_2_1\", \"S10_2_2\", \"S10_2_3\", \"TI_1_1\", \"TI_1_2\", \"TI_1_3\", \"TI_2_1\", \"TI_2_2\", \"TI_2_3\") head(Read_counts, n=10) dim(Read_counts) # dimensions of the data frame A data.frame: 10 \u00d7 12 S10_1_1 S10_1_2 S10_1_3 S10_2_1 S10_2_2 S10_2_3 TI_1_1 TI_1_2 TI_1_3 TI_2_1 TI_2_2 TI_2_3 g175 143 226 227 217 206 193 234 205 198 221 206 165 g176 17 12 15 8 14 18 29 19 30 6 11 23 g177 0 0 0 0 0 0 0 0 0 0 0 0 g178 0 0 0 0 0 0 0 0 2 2 1 2 g179 0 0 0 0 0 0 0 0 0 0 0 0 g180 2773 5223 4350 4139 3209 4644 1015 1313 889 2675 1023 2565 g181 473 679 583 437 390 450 642 700 697 650 459 410 g182 19 15 20 9 8 4 13 7 8 6 5 4 g183 0 0 0 0 0 0 0 0 0 0 0 0 g184 30 22 50 57 103 83 36 29 18 107 69 89 .list-inline {list-style: none; margin:0; padding: 0} .list-inline>li {display: inline-block} .list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex} 366 12 Import the Metadata table. This file, as its name suggests, contains information about each of the 12 RNA-seq samples, such as the treatment (Condition), genotype and replicate. Note that the order of the rows in the Metadata table should be the same as the columns in the Read_counts file generated above. In [4]: Copied! metadata <- read.csv ( \"../Data/Clover_Data/metadata.csv\" , sep = \";\" , row.names = 1 , stringsAsFactors = TRUE ) metadata metadata < - read.csv(\"../Data/Clover_Data/metadata.csv\", sep =\";\", row.names=1, stringsAsFactors=TRUE) metadata A data.frame: 12 \u00d7 3 Condition Genotype Replicate S10_1_1 Control S10 1 S10_1_2 Control S10 2 S10_1_3 Control S10 3 S10_2_1 Treatment S10 1 S10_2_2 Treatment S10 2 S10_2_3 Treatment S10 3 TI_1_1 Control Tienshan 1 TI_1_2 Control Tienshan 2 TI_1_3 Control Tienshan 3 TI_2_1 Treatment Tienshan 1 TI_2_2 Treatment Tienshan 2 TI_2_3 Treatment Tienshan 3 In order to aid the following steps, we will create a Group for each sample (a new column in the metadata) based on the Genotype&Condition of each sample and assign the three replicates to this group. In [5]: Copied! Group <- factor ( paste ( metadata $ Genotype , metadata $ Condition , sep = \"_\" )) metadata <- cbind ( metadata , Group = Group ) metadata Group < - factor(paste(metadata$Genotype, metadata$Condition, sep=\"_\")) metadata < - cbind(metadata,Group=Group) metadata A data.frame: 12 \u00d7 4 Condition Genotype Replicate Group S10_1_1 Control S10 1 S10_Control S10_1_2 Control S10 2 S10_Control S10_1_3 Control S10 3 S10_Control S10_2_1 Treatment S10 1 S10_Treatment S10_2_2 Treatment S10 2 S10_Treatment S10_2_3 Treatment S10 3 S10_Treatment TI_1_1 Control Tienshan 1 Tienshan_Control TI_1_2 Control Tienshan 2 Tienshan_Control TI_1_3 Control Tienshan 3 Tienshan_Control TI_2_1 Treatment Tienshan 1 Tienshan_Treatment TI_2_2 Treatment Tienshan 2 Tienshan_Treatment TI_2_3 Treatment Tienshan 3 Tienshan_Treatment Create the DGEList object \u00b6 We will merge the read counts and the metadata into a list-based data object named DGEList, which can be manipulated as any list object in R. The main components of the DGEList object are the matrix \"counts\" containing our read per gene counts and a data.frame \"samples\" containing the metadata. Note that all the genes with zero counts across all samples were eliminated. In [6]: Copied! DGEList <- DGEList ( Read_counts , remove.zeros = TRUE ) DGEList $ samples $ Condition <- relevel ( metadata $ Condition , ref = \"Control\" ) DGEList $ samples $ Genotype <- metadata $ Genotype DGEList $ samples $ group <- metadata $ Group DGEList DGEList < - DGEList(Read_counts, remove.zeros = TRUE) DGEList$samples$Condition < - relevel(metadata$Condition, ref = \"Control\") DGEList$samples$Genotype < - metadata$Genotype DGEList$samples$group < - metadata$Group DGEList Removing 99 rows with all zero counts $counts A matrix: 267 \u00d7 12 of type int S10_1_1 S10_1_2 S10_1_3 S10_2_1 S10_2_2 S10_2_3 TI_1_1 TI_1_2 TI_1_3 TI_2_1 TI_2_2 TI_2_3 g175 143 226 227 217 206 193 234 205 198 221 206 165 g176 17 12 15 8 14 18 29 19 30 6 11 23 g178 0 0 0 0 0 0 0 0 2 2 1 2 g180 2773 5223 4350 4139 3209 4644 1015 1313 889 2675 1023 2565 g181 473 679 583 437 390 450 642 700 697 650 459 410 g182 19 15 20 9 8 4 13 7 8 6 5 4 g184 30 22 50 57 103 83 36 29 18 107 69 89 g185 0 2 0 0 0 0 0 1 0 0 0 1 g186 1 0 0 0 0 0 0 0 0 0 0 0 g187 1 1 2 0 1 1 58 49 57 28 32 29 g188 316 285 417 200 184 322 1276 855 1341 402 809 547 g190 14 18 29 25 17 31 7 6 12 20 7 6 g191 2 2 1 1 0 0 0 0 0 0 0 0 g192 1 1 1 1 1 0 6 10 2 3 8 2 g193 208 231 301 289 161 279 70 61 60 128 160 91 g194 40 41 37 44 17 43 8 10 7 8 105 25 g195 54 85 85 71 54 43 56 54 42 67 68 56 g196 77 120 118 186 152 177 5 9 5 42 20 34 g197 1 6 5 18 14 21 15 44 12 32 16 17 g198 14 27 10 11 2 2 6 6 1 2 2 0 g199 0 0 0 0 0 0 0 0 0 0 1 0 g200 57 80 94 11 8 23 89 47 67 5 47 5 g203 815 1188 1412 1139 1034 1127 911 852 662 892 781 784 g204 250 312 348 277 275 264 125 104 133 119 91 130 g205 109 132 181 140 114 151 89 73 66 78 46 50 g206 0 1 2 7 4 9 4 0 7 0 1 2 g207 0 1 0 0 4 3 1 3 4 4 3 3 g208 0 1 0 0 0 0 0 1 0 1 0 0 g209 0 0 0 0 0 0 0 0 1 0 0 0 g210 52 76 60 12 12 13 61 73 94 54 45 21 \u22ee \u22ee \u22ee \u22ee \u22ee \u22ee \u22ee \u22ee \u22ee \u22ee \u22ee \u22ee \u22ee g135 0 0 0 1 0 0 5 1 0 2 0 0 g139 0 1 1 0 0 0 12 13 11 4 3 3 g140 0 5 1 0 0 2 5 4 0 0 2 0 g141 177 217 224 131 117 166 117 85 67 80 79 77 g142 488 713 557 241 154 228 983 1057 1023 1075 794 933 g144 3319 3987 4763 2249 1996 2260 4405 3880 4585 5072 3618 3590 g146 293 330 350 913 775 731 108 92 115 431 151 226 g147 6 3 3 0 8 10 1 1 1 0 1 0 g148 19 30 24 15 4 12 23 19 25 10 19 8 g149 35 62 65 57 58 53 114 103 91 92 79 93 g150 263 411 278 752 1378 1300 106 112 70 516 200 508 g151 44 44 50 92 92 84 23 26 25 56 43 31 g152 242 258 294 425 381 521 15 8 8 16 21 15 g153 142 144 181 233 177 233 31 49 50 50 45 57 g155 0 4 2 1 0 0 1 0 3 0 0 1 g156 0 0 0 0 0 0 0 3 2 0 0 1 g157 6 1 3 223 52 152 4 7 4 153 47 155 g158 0 0 0 7 1 3 0 0 1 1 0 0 g161 25 38 18 1 9 2 63 23 43 0 24 6 g162 31 43 38 43 25 28 0 0 0 0 0 0 g163 1 0 12 37 8 18 9 4 8 16 13 15 g164 69 58 97 294 123 168 56 58 60 146 101 135 g165 203 376 594 1343 2062 1610 265 229 214 488 270 490 g167 2 0 0 2 0 0 0 0 0 0 0 0 g168 2 14 6 0 0 5 31 26 38 3 27 10 g169 0 0 1 0 1 1 0 0 0 0 0 0 g170 0 0 0 0 0 0 0 1 0 0 0 0 g171 0 5 0 0 0 5 0 0 0 1 0 0 g172 1 2 2 6 4 3 3 1 2 3 3 0 g173 80 107 120 78 138 121 67 86 69 96 105 105 $samples A data.frame: 12 \u00d7 5 group lib.size norm.factors Condition Genotype S10_1_1 S10_Control 43614 1 Control S10 S10_1_2 S10_Control 58803 1 Control S10 S10_1_3 S10_Control 62654 1 Control S10 S10_2_1 S10_Treatment 59909 1 Treatment S10 S10_2_2 S10_Treatment 56761 1 Treatment S10 S10_2_3 S10_Treatment 63152 1 Treatment S10 TI_1_1 Tienshan_Control 43387 1 Control Tienshan TI_1_2 Tienshan_Control 38355 1 Control Tienshan TI_1_3 Tienshan_Control 40304 1 Control Tienshan TI_2_1 Tienshan_Treatment 55509 1 Treatment Tienshan TI_2_2 Tienshan_Treatment 39909 1 Treatment Tienshan TI_2_3 Tienshan_Treatment 45008 1 Treatment Tienshan Preliminary data analysis \u00b6 First, we will calculate the \"pseudoCounts\" as log2 of the reads per gene counts. This is not part of the actual differential gene expression analysis but is helpful for data exploration and quality assessment. We will look at a histogram of one of the samples and a boxplot representation of the log2 counts for all the 12 samples. Note that there are many genes with a low number of mapped reads and that there are differences between the average read counts for each library. In [7]: Copied! pseudoCounts <- log2 ( DGEList $ counts + 1 ) head ( pseudoCounts ) hist ( pseudoCounts [ , \"S10_1_1\" ], main = \"\" , xlab = \"Read counts\" ) boxplot ( pseudoCounts , col = \"gray\" , las = 3 , cex.names = 1 ) pseudoCounts < - log2(DGEList$counts + 1) head(pseudoCounts) hist(pseudoCounts[ ,\"S10_1_1\"], main = \"\", xlab = \"Read counts\") boxplot(pseudoCounts, col = \"gray\", las = 3, cex.names = 1) A matrix: 6 \u00d7 12 of type dbl S10_1_1 S10_1_2 S10_1_3 S10_2_1 S10_2_2 S10_2_3 TI_1_1 TI_1_2 TI_1_3 TI_2_1 TI_2_2 TI_2_3 g175 7.169925 7.826548 7.832890 7.768184 7.693487 7.599913 7.876517 7.686501 7.636625 7.794416 7.693487 7.375039 g176 4.169925 3.700440 4.000000 3.169925 3.906891 4.247928 4.906891 4.321928 4.954196 2.807355 3.584963 4.584963 g178 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 1.584963 1.584963 1.000000 1.584963 g180 11.437752 12.350939 12.087131 12.015415 11.648358 12.181463 9.988685 10.359750 9.797662 11.385862 10.000000 11.325305 g181 8.888743 9.409391 9.189825 8.774787 8.611025 8.816984 9.328675 9.453271 9.447083 9.346514 8.845490 8.682995 g182 4.321928 4.000000 4.392317 3.321928 3.169925 2.321928 3.807355 3.000000 3.169925 2.807355 2.584963 2.321928 We can also create a PCA plot of the samples in order to assess the differences between the Genotypes and Conditions, but also between the replicates. In this plot the samples that are similar cluster together, while samples that are different are further apart. In this type of plot, we would expect samples from the same group (the three replicates for each sample) to exhibit a similar gene expression profile thus clustering together while being separated from the other samples. In [8]: Copied! resPCA <- pca ( t ( pseudoCounts ), ncomp = 6 ) plotIndiv ( resPCA , group = metadata $ Genotype , pch = metadata $ Condition , legend = T , legend.title = 'Genotype' , legend.title.pch = 'Condition' , title = 'PCA plot raw counts' , style = 'ggplot2' , size.xlabel = 10 , size.ylabel = 10 ) resPCA < - pca(t(pseudoCounts), ncomp = 6) plotIndiv(resPCA, group = metadata$Genotype, pch=metadata$Condition, legend = T, legend.title = 'Genotype', legend.title.pch = 'Condition', title = 'PCA plot raw counts', style = 'ggplot2', size.xlabel = 10, size.ylabel = 10) Filtering the lowly expressed genes \u00b6 As seen previously, many genes have a low number of read counts in our samples. The genes with very low counts across all libraries provide little evidence for differential expression, thus we should eliminate these genes before the analysis. One of the filtering methods we can use is the \"filterByExpr\" function provided by the edgeR package. By default, this function keeps only the genes that have at least 10 reads per group, but other cutoffs can also be applied. In [9]: Copied! keep <- filterByExpr ( DGEList , group = metadata $ Group ) #create the filter DGEList <- DGEList [ keep , , keep.lib.sizes = FALSE ] #apply the filter to on the DGEList object table ( keep ) #Check the number of genes that passed the filter keep < - filterByExpr(DGEList, group=metadata$Group) #create the filter DGEList < - DGEList[keep, , keep.lib.sizes=FALSE] #apply the filter to on the DGEList object table(keep) #Check the number of genes that passed the filter keep FALSE TRUE 104 163 Normalization \u00b6 As we are working with multiple samples we need to normalize the read counts per gene in order to account for compositional and technical differences between the 12 RNA-seq libraries. For this, we will calculate normalization factors using the trimmed mean of M-values (TMM) method. You can read more about different normalization methods in the user manual. Note that running \"calcNormFactors\" does not change the actual reads per gene counts, it just fills the \"norm.factors\" column in DGEList$samples . These factors will be used to scale the read counts for each library. From the user guide: \"A normalization factor below one indicates that a small number of high count genes are monopolizing the sequencing, causing the counts for other genes to be lower than would be usual given the library size.\" In [10]: Copied! DGEList <- calcNormFactors ( DGEList , method = \"RLE\" ) DGEList $ samples DGEList < - calcNormFactors(DGEList, method=\"RLE\") DGEList$samples A data.frame: 12 \u00d7 5 group lib.size norm.factors Condition Genotype S10_1_1 S10_Control 43506 1.0897129 Control S10 S10_1_2 S10_Control 58660 1.0385093 Control S10 S10_1_3 S10_Control 62512 1.0362962 Control S10 S10_2_1 S10_Treatment 59741 0.9802860 Treatment S10 S10_2_2 S10_Treatment 56653 0.8418644 Treatment S10 S10_2_3 S10_Treatment 63008 0.8949144 Treatment S10 TI_1_1 Tienshan_Control 43174 1.0618440 Control Tienshan TI_1_2 Tienshan_Control 38203 1.0431678 Control Tienshan TI_1_3 Tienshan_Control 40178 1.0676351 Control Tienshan TI_2_1 Tienshan_Treatment 55391 0.9048196 Treatment Tienshan TI_2_2 Tienshan_Treatment 39810 1.1235077 Treatment Tienshan TI_2_3 Tienshan_Treatment 44910 0.9603761 Treatment Tienshan Normalized counts - Exploratory Data analysis \u00b6 For data analysis purposes normalized log2 counts can be extracted from the DGEList object using the function CPM (counts per million). We will generate the same plots as for the raw counts in order to compare the data before and after normalization. Do the plots for normalized counts look different compared with the plots computed before data filtering and normalization? In [11]: Copied! pseudoNormCounts <- cpm ( DGEList , log = TRUE , prior.count = 1 ) head ( pseudoNormCounts ) hist ( pseudoNormCounts [ , \"S10_1_1\" ], main = \"\" , xlab = \"counts\" ) boxplot ( pseudoNormCounts , col = \"gray\" , las = 3 , cex.names = 1 ) resPCA <- pca ( t ( pseudoNormCounts ), ncomp = 6 ) plotIndiv ( resPCA , group = metadata $ Genotype , pch = metadata $ Condition , legend = T , legend.title = 'Genotype' , legend.title.pch = 'Condition' , title = 'PCA plot normalized counts' , style = 'ggplot2' , size.xlabel = 10 , size.ylabel = 10 ) pseudoNormCounts < - cpm(DGEList, log = TRUE, prior.count = 1) head(pseudoNormCounts) hist(pseudoNormCounts[ ,\"S10_1_1\"], main = \"\", xlab = \"counts\") boxplot(pseudoNormCounts, col = \"gray\", las = 3, cex.names = 1) resPCA < - pca(t(pseudoNormCounts), ncomp = 6) plotIndiv(resPCA, group = metadata$Genotype, pch=metadata$Condition, legend = T, legend.title = 'Genotype', legend.title.pch = 'Condition', title = 'PCA plot normalized counts', style = 'ggplot2', size.xlabel = 10, size.ylabel = 10) A matrix: 6 \u00d7 12 of type dbl S10_1_1 S10_1_2 S10_1_3 S10_2_1 S10_2_2 S10_2_3 TI_1_1 TI_1_2 TI_1_3 TI_2_1 TI_2_2 TI_2_3 g175 11.568005 11.864813 11.782957 11.863090 12.083127 11.749272 12.323049 12.334198 12.178544 12.112851 12.175386 11.908906 g176 8.564108 7.760868 7.974199 7.290213 8.292080 8.405708 9.349782 8.956131 9.490388 7.125494 8.054467 9.111569 g180 15.836359 16.387906 16.035457 16.109276 16.038322 16.329943 14.435626 15.008665 14.340400 15.704307 14.482504 15.860317 g181 13.287211 13.446752 13.138771 12.869139 13.000828 12.965820 13.775545 14.101988 13.989757 13.664955 13.327806 13.217591 g182 8.716557 8.056048 8.360376 7.439590 7.551908 6.505541 8.245462 7.611500 7.689402 7.125494 7.041192 6.815835 g184 9.350249 8.573811 9.628852 9.955925 11.089735 10.542877 9.653135 9.546090 8.779819 11.073313 10.609695 11.024686 EdgeR - Testing for Differentially expressed genes (DEGs) \u00b6 We use edgeR for Differential Gene Expression using the GLM method (Generalized linear model) with treatment and genotype effects After we concluded the exploratory data analysis and the filtering and normalization steps we can now start testing for differentially expressed genes between our samples. For this, we first need to define a \"design matrix\" which describes our experimental design. We construct a matrix containing samples as rows and our previously defined groups (S10_Control, S10_Treatment, Tienshan_Control, Tienshan_Treatment) as columns. The samples belonging to each group are assigned a \"1\" in this matrix. In [12]: Copied! design.matrix <- model.matrix ( ~ 0 + Group ) rownames ( design.matrix ) <- colnames ( DGEList ) colnames ( design.matrix ) <- levels ( metadata $ Group ) design.matrix design.matrix < - model.matrix(~0+Group) rownames(design.matrix) < - colnames(DGEList) colnames(design.matrix) < - levels(metadata$Group) design.matrix A matrix: 12 \u00d7 4 of type dbl S10_Control S10_Treatment Tienshan_Control Tienshan_Treatment S10_1_1 1 0 0 0 S10_1_2 1 0 0 0 S10_1_3 1 0 0 0 S10_2_1 0 1 0 0 S10_2_2 0 1 0 0 S10_2_3 0 1 0 0 TI_1_1 0 0 1 0 TI_1_2 0 0 1 0 TI_1_3 0 0 1 0 TI_2_1 0 0 0 1 TI_2_2 0 0 0 1 TI_2_3 0 0 0 1 For easier pairwise comparisons between the groups we can create some \"contrasts\" using the \"makeContrasts\" function. This will tell the program which samples we will like to consider as our \"Control\" and which as our \"Treatment\" groups. The Control groups will be assigned a \"-1\" and the Treatment groups a \"1\". When using multiple Control and Treatment samples in the same contrast we need to devide by the number of samples used as for example in the contrast S10_Tienshan, where we use both S10 and Ti samples. For example, the contrast S10 = S10_Treatment - S10_Control will test for differentially expressed genes between the S10 treatment samples vs the S10 Control samples. In [13]: Copied! contrasts <- makeContrasts ( S10 = S10_Treatment - S10_Control , Tienshan = Tienshan_Treatment - Tienshan_Control , S10_Tienshan = ( S10_Treatment + Tienshan_Treatment ) / 2 - ( S10_Control + Tienshan_Control ) / 2 , levels = design.matrix ) contrasts contrasts < - makeContrasts( S10 = S10_Treatment-S10_Control, Tienshan = Tienshan_Treatment-Tienshan_Control, S10_Tienshan= (S10_Treatment+Tienshan_Treatment)/2 - (S10_Control+Tienshan_Control)/2, levels=design.matrix) contrasts A matrix: 4 \u00d7 3 of type dbl S10 Tienshan S10_Tienshan S10_Control -1 0 -0.5 S10_Treatment 1 0 0.5 Tienshan_Control 0 -1 -0.5 Tienshan_Treatment 0 1 0.5 Any ideas for other contrasts that migth be interesting to explore? The following cell will run two mandatory steps in order to fit a model in edgeR. First, the dispersion needs to be estimated, this is a measure of the degree of inter-library variation for each gene. Afterwards, a QL ( quasi-likelihood) model representing the sample design is fitted using the \"glmQLFit\" function. You can read more about these steps in the edgeR user guide. In [14]: Copied! DGEList <- estimateDisp ( DGEList , design.matrix ) fit <- glmQLFit ( DGEList , design.matrix ) DGEList < - estimateDisp(DGEList, design.matrix) fit < - glmQLFit(DGEList, design.matrix) DEGs for White clover S10 plants Treatment vs Control \u00b6 Find DEGs for White clover S10 plants in Treatment condition compared with the Control condition We will test for differentially expressed genes using the quasi-likelihood F-tests method. This is easily done just by running the \"glmQFTest\" function on the fit model and selecting one of the contrasts we created above (in this case the \"S10\" contrast). Next, we will use the topTags function to extract the information about all the genes. In [15]: Copied! glmqlf_S10 <- glmQLFTest ( fit , contrast = contrasts [, \"S10\" ]) DEG_S10 <- topTags ( glmqlf_S10 , n = nrow ( DGEList $ counts )) DEG_S10 glmqlf_S10 < - glmQLFTest(fit, contrast=contrasts[,\"S10\"]) DEG_S10 < - topTags(glmqlf_S10, n = nrow(DGEList$counts)) DEG_S10 $table A data.frame: 163 \u00d7 5 logFC logCPM F PValue FDR g39 3.5861702 14.124630 336.66158 5.227331e-10 8.520550e-08 g232 4.0494125 11.950441 157.50997 3.685988e-08 2.591779e-06 g6 2.3225347 12.999541 150.27854 4.770146e-08 2.591779e-06 g350 2.0530029 13.324566 97.56453 4.890245e-07 1.992775e-05 g99 -1.5387874 11.681004 85.17478 9.976659e-07 3.195090e-05 g142 -1.4235276 13.830692 82.52308 1.176107e-06 3.195090e-05 g240 1.3603712 14.708794 79.98114 1.383201e-06 3.220882e-05 g269 -1.4191681 11.834217 75.66563 1.841279e-06 3.751606e-05 g26 2.1254241 11.366827 68.55898 3.047273e-06 5.518949e-05 g129 -5.3324794 8.679594 60.45459 5.738115e-06 9.353127e-05 g272 3.1405978 13.860652 59.08187 6.432977e-06 9.532503e-05 g157 5.3813801 10.414972 57.03712 7.659515e-06 1.040417e-04 g42 -1.9856547 9.962762 53.97164 1.005146e-05 1.260298e-04 g165 2.2666761 13.690016 51.66777 1.243606e-05 1.447913e-04 g101 3.1488142 13.181625 48.21727 1.736838e-05 1.887363e-04 g15 2.7410696 10.684840 47.29143 1.906062e-05 1.941800e-04 g210 -2.2430064 9.993002 46.52627 2.060621e-05 1.975772e-04 g299 -2.3509276 10.984491 45.75893 2.230566e-05 2.019901e-04 g103 2.6810410 10.579777 44.26963 2.609599e-05 2.238762e-04 g144 -0.7963477 16.186987 43.17707 2.936057e-05 2.392886e-04 g77 1.0984806 14.118231 41.46740 3.548119e-05 2.754016e-04 g125 -1.3099534 10.195970 39.85537 4.266319e-05 3.160955e-04 g298 -2.0471647 9.837072 33.49832 9.395967e-05 6.658881e-04 g104 2.4081173 9.219160 38.54240 1.121281e-04 7.615370e-04 g122 -0.6485441 12.457152 30.90261 1.341148e-04 8.744287e-04 g164 1.4415064 11.156457 30.50383 1.419308e-04 8.897970e-04 g146 1.3983889 12.824184 29.92911 1.541544e-04 9.306356e-04 g313 2.2492040 13.614815 28.41728 1.926822e-04 1.121686e-03 g150 1.9650641 13.229706 27.90020 2.083770e-04 1.171222e-03 g13 -0.6522187 13.101579 26.45593 2.608176e-04 1.417109e-03 \u22ee \u22ee \u22ee \u22ee \u22ee \u22ee g96 -1.873792302 8.939397 0.4844512655 0.4999546 0.6041584 g10 0.233134670 14.382992 0.4834790913 0.5003766 0.6041584 g253 0.052426914 13.741219 0.3075042687 0.5896191 0.7066759 g289 -0.212573283 9.390864 0.2887857862 0.6010328 0.7150974 g312 0.093543041 10.849110 0.2666731965 0.6151559 0.7265972 g301 -0.102000599 10.135184 0.2572520652 0.6214023 0.7274503 g221 -0.277095720 8.469909 0.2522121463 0.6248039 0.7274503 g162 -0.145155616 8.517263 0.2278112475 0.6463283 0.7413085 g50 -0.170907352 8.749274 0.2158314533 0.6507284 0.7413085 g203 0.062836598 14.227601 0.2124612164 0.6532634 0.7413085 g88 0.045949497 14.235850 0.2103049178 0.6548983 0.7413085 g355 -0.287897714 8.890754 0.1803234432 0.6787703 0.7630314 g204 -0.055975779 11.947887 0.1408988543 0.7140799 0.7972262 g306 0.054269738 11.294732 0.0923937800 0.7664733 0.8498990 g193 0.049946343 11.682386 0.0642557849 0.8042743 0.8857886 g205 0.037877227 10.986961 0.0473457745 0.8314821 0.9029713 g194 -0.142990391 9.403497 0.0447552162 0.8360811 0.9029713 g362 0.025629967 11.336529 0.0415198735 0.8420254 0.9029713 g315 0.027958374 11.878331 0.0409766489 0.8430470 0.9029713 g180 0.059680256 15.711409 0.0386140448 0.8475743 0.9029713 g282 -0.026492114 11.756078 0.0247991469 0.8775434 0.9288284 g176 -0.061055115 8.587203 0.0144510429 0.9063467 0.9531258 g126 0.061707217 8.311212 0.0024786868 0.9648334 1.0000000 g128 0.063825269 7.671043 0.0019414812 0.9655950 1.0000000 g227 -0.025740630 7.939955 0.0011908008 0.9730516 1.0000000 g121 0.002660852 12.896620 0.0005233398 0.9821327 1.0000000 g284 0.000000000 8.538720 0.0000000000 1.0000000 1.0000000 g116 0.000000000 7.180297 0.0000000000 1.0000000 1.0000000 g117 0.000000000 7.033307 0.0000000000 1.0000000 1.0000000 g118 0.000000000 7.515646 0.0000000000 1.0000000 1.0000000 $adjust.method 'BH' $comparison '-1*S10_Control 1*S10_Treatment' $test 'glm' We have now created a table with certain parameters calculated for each of the genes analysed. logFC represents the base 2 logarithm of the fold change and it shows us how much the expression of the gene has changed between the two conditions. A logFC of 1 means a doubling in the read per gene count between the control and treatment samples. The genes with a logFC higher than 0 are upregulated while the genes with a logFC lower than 0 are downregulated between the control and treatment samples. logCPM represents the average log2-counts-per-million, the abundance of the gene. F - F-statistic. PValue is the raw p-value. FDR (The false discovery rate) represents the adjusted p-value and is calculated using Benjamini and Hochberg's algorithm. It controls the rate of false positive values under multiple testing. Usually, a threshold of under 5% is set for the FDR. The important information for us in this table is stored in the \"logFC\" and the \"FDR\" columns. The top DE genes have small FDR values and large fold changes Many of the genes in the samples are uninteresting for us, as they have a high FDR and/or low logFC values so we cannot consider them as differentially expressed. We will apply a filtering step in order to keep only the statistically significant genes. We will filter out the genes with an FDR higher than 0.05 and an absolute logFC lower than 1. In [16]: Copied! DEG_S10_filtered <- DEG_S10 $ table [ DEG_S10 $ table $ FDR < 0.05 & abs ( DEG_S10 $ table $ logFC ) > 1 ,] #Filtering DEG_S10_filtered <- rownames_to_column ( DEG_S10_filtered ) %>% rename ( gene_ID = rowname ) #Adding the gene_ID column head ( DEG_S10_filtered ) nrow ( DEG_S10_filtered ) # finding the number of genes that passed the filter DEG_S10_filtered < - DEG_S10$table[DEG_S10$table$FDR < 0.05 & abs(DEG_S10$table$logFC) > 1,] #Filtering DEG_S10_filtered < - rownames_to_column(DEG_S10_filtered) %>% rename(gene_ID = rowname) #Adding the gene_ID column head(DEG_S10_filtered) nrow(DEG_S10_filtered) # finding the number of genes that passed the filter A data.frame: 6 \u00d7 6 gene_ID logFC logCPM F PValue FDR 1 g39 3.586170 14.12463 336.66158 5.227331e-10 8.520550e-08 2 g232 4.049413 11.95044 157.50997 3.685988e-08 2.591779e-06 3 g6 2.322535 12.99954 150.27854 4.770146e-08 2.591779e-06 4 g350 2.053003 13.32457 97.56453 4.890245e-07 1.992775e-05 5 g99 -1.538787 11.68100 85.17478 9.976659e-07 3.195090e-05 6 g142 -1.423528 13.83069 82.52308 1.176107e-06 3.195090e-05 52 We can also visualize the selected genes by plotting a Smear plot or a Volcano plot. The Genes that passed the filter are coloured in red, and the top 10 genes with the lowest FDR value are labelled with their gene ID. We can see that the majority of the genes analysed are either not statistically significant or have a very small logFC. In [17]: Copied! plotSmear ( glmqlf_S10 , de.tags = rownames ( DEG_S10 $ table )[ which ( DEG_S10 $ table $ FDR < 0.05 & abs ( DEG_S10 $ table $ logFC ) > 1 )]) text ( x = DEG_S10_filtered $ logCPM [ 1 : 10 ], y = DEG_S10_filtered $ logFC [ 1 : 10 ], labels = DEG_S10_filtered $ gene_ID [ 1 : 10 ], cex = 0.7 , pos = 1 ) abline ( h = c ( -1 , 1 ), col = \"blue\" ) plotSmear(glmqlf_S10, de.tags = rownames(DEG_S10$table)[which(DEG_S10$table$FDR < 0.05 & abs(DEG_S10$table$logFC) > 1)]) text(x=DEG_S10_filtered$logCPM[1:10], y=DEG_S10_filtered$logFC[1:10], labels=DEG_S10_filtered$gene_ID[1:10], cex=0.7, pos=1) abline(h = c(-1, 1), col = \"blue\") We can also create a heatmap with the log2 read counts of the selected differentially expressed genes so that we can visualise the differences in normalized counts between the Control and the Treatment samples. The genes are ordered by the FDR value. In [18]: Copied! annot_col <- data.frame ( row.names = colnames ( pseudoNormCounts )[ 1 : 6 ], Condition = c ( rep ( \"Control\" , 3 ), rep ( \"Treatment\" , 3 ))) pheatmap ( as.matrix ( pseudoNormCounts [ DEG_S10_filtered $ gene_ID , c ( 1 : 6 )]), cluster_rows = F , cluster_col = F , annotation_col = annot_col ) annot_col < - data.frame(row.names = colnames(pseudoNormCounts)[1:6], Condition = c(rep(\"Control\", 3),rep( \"Treatment\", 3))) pheatmap(as.matrix(pseudoNormCounts[DEG_S10_filtered$gene_ID,c(1:6)]), cluster_rows = F, cluster_col = F, annotation_col = annot_col) DEGs for White clover Tienshan Treatment vs Control \u00b6 We can do this using the same functions as above and changing the contrast. This time we will directly filter the differentially expressed genes using the same parameters as for the S10 samples. In [19]: Copied! glmqlf_Ti <- glmQLFTest ( fit , contrast = contrasts [, \"Tienshan\" ]) DEG_Ti <- topTags ( glmqlf_Ti , n = nrow ( DGEList $ counts )) DEG_Ti_filtered <- DEG_Ti $ table [ DEG_Ti $ table $ FDR < 0.05 & abs ( DEG_Ti $ table $ logFC ) > 1 ,] DEG_Ti_filtered <- rownames_to_column ( DEG_Ti_filtered ) %>% rename ( gene_ID = rowname ) print ( head ( DEG_Ti_filtered )) print ( \"Nr of differentially expressed genes:\" ) print ( nrow ( DEG_Ti_filtered )) glmqlf_Ti < - glmQLFTest(fit, contrast=contrasts[,\"Tienshan\"]) DEG_Ti < - topTags(glmqlf_Ti, n = nrow(DGEList$counts)) DEG_Ti_filtered < - DEG_Ti$table[DEG_Ti$table$FDR < 0.05 & abs(DEG_Ti$table$logFC) > 1,] DEG_Ti_filtered < - rownames_to_column(DEG_Ti_filtered) %>% rename(gene_ID = rowname) print(head(DEG_Ti_filtered)) print(\"Nr of differentially expressed genes:\") print(nrow(DEG_Ti_filtered)) gene_ID logFC logCPM F PValue FDR 1 g234 -3.348667 13.00417 278.01679 1.547626e-09 2.522630e-07 2 g269 -1.885843 11.83422 127.39949 1.172628e-07 9.556922e-06 3 g26 2.423725 11.36683 74.33983 2.016212e-06 8.910761e-05 4 g101 4.303290 13.18162 73.17140 2.186690e-06 8.910761e-05 5 g272 3.391225 13.86065 66.80131 3.475790e-06 9.840357e-05 6 g99 -1.430395 11.68100 66.25814 3.622217e-06 9.840357e-05 [1] \"Nr of differentially expressed genes:\" [1] 42 We can now plot a Venn diagram with the DEGs for the two genotypes in order to observe the number of common and specific differentially expressed genes between the two genotypes as response to the cold exposure. You can see that a high percentage of the identified genes are common for the two genotypes, while each genotype has also specific genes. In [20]: Copied! vd <- venn.diagram ( x = list ( DEG_S10_filtered $ gene_ID , DEG_Ti_filtered $ gene_ID ), category.names = c ( \"S10\" , \"Tienshan\" ), lwd = 4 , fill = c ( \"cornflowerblue\" , \"yellowgreen\" ), filename = NULL , cat.cex = 1 , cat.fontface = \"bold\" , output = TRUE ) grid.draw ( vd ) vd < - venn.diagram( x = list(DEG_S10_filtered$gene_ID, DEG_Ti_filtered$gene_ID), category.names = c(\"S10\" , \"Tienshan\"), lwd = 4, fill = c(\"cornflowerblue\", \"yellowgreen\"), filename = NULL, cat.cex = 1, cat.fontface = \"bold\", output=TRUE ) grid.draw(vd) DEGs for S10+Tienshan Treatment vs Control w/o genotype effects \u00b6 Until now we tested for DEGs specific for each of the two genotypes under cold treatment. We can also run a test where we ignore the genotype and just test for the differences in the cold response. Consider the counts for both genotypes as a single dataset using the previously created contrast \"S10_Tienshan\". Do the results look different compared with the previous tests? In [21]: Copied! glmqlf_S10_Ti <- glmQLFTest ( fit , contrast = contrasts [, \"S10_Tienshan\" ]) DEG_S10_Ti <- topTags ( glmqlf_S10_Ti , n = nrow ( DGEList $ counts )) DEG_S10_Ti_filtered <- DEG_S10_Ti $ table [ DEG_S10_Ti $ table $ FDR < 0.05 & abs ( DEG_S10_Ti $ table $ logFC ) > 1 ,] DEG_S10_Ti_filtered <- rownames_to_column ( DEG_S10_Ti_filtered ) %>% rename ( gene_ID = rowname ) print ( head ( DEG_S10_Ti_filtered )) print ( \"Nr of differentially expressed genes:\" ) print ( nrow ( DEG_S10_Ti_filtered )) glmqlf_S10_Ti < - glmQLFTest(fit, contrast=contrasts[,\"S10_Tienshan\"]) DEG_S10_Ti < - topTags(glmqlf_S10_Ti, n = nrow(DGEList$counts)) DEG_S10_Ti_filtered < - DEG_S10_Ti$table[DEG_S10_Ti$table$FDR < 0.05 & abs(DEG_S10_Ti$table$logFC) > 1,] DEG_S10_Ti_filtered < - rownames_to_column(DEG_S10_Ti_filtered) %>% rename(gene_ID = rowname) print(head(DEG_S10_Ti_filtered)) print(\"Nr of differentially expressed genes:\") print(nrow(DEG_S10_Ti_filtered)) gene_ID logFC logCPM F PValue FDR 1 g39 2.057128 14.12463 246.6519 3.039491e-09 4.954370e-07 2 g232 3.261008 11.95044 203.4580 8.934889e-09 5.288546e-07 3 g269 -1.652505 11.83422 200.3564 9.733520e-09 5.288546e-07 4 g6 1.714849 12.99954 166.2297 2.740066e-08 1.116577e-06 5 g99 -1.484591 11.68100 150.3246 4.762148e-08 1.552460e-06 6 g26 2.274575 11.36683 142.8625 6.290957e-08 1.623160e-06 [1] \"Nr of differentially expressed genes:\" [1] 52 Plot the results from the 3 tests in a Venn diagram to visualize the number of common and unique genes In [22]: Copied! vd <- venn.diagram ( x = list ( DEG_S10_filtered $ gene_ID , DEG_Ti_filtered $ gene_ID , DEG_S10_Ti_filtered $ gene_ID ), category.names = c ( \"S10\" , \"Tienshan\" , \"S10_Tienshan\" ), lwd = 3 , fill = c ( \"cornflowerblue\" , \"yellowgreen\" , \"thistle3\" ), filename = NULL , cat.cex = 1 , cat.fontface = \"bold\" , output = TRUE ) grid.draw ( vd ) vd < - venn.diagram( x = list(DEG_S10_filtered$gene_ID, DEG_Ti_filtered$gene_ID, DEG_S10_Ti_filtered$gene_ID), category.names = c(\"S10\" , \"Tienshan\", \"S10_Tienshan\"), lwd = 3, fill = c(\"cornflowerblue\", \"yellowgreen\", \"thistle3\"), filename = NULL, cat.cex = 1, cat.fontface = \"bold\", output=TRUE ) grid.draw(vd) Explore DGE results \u00b6 Select the genes which appear only in the analysis using both genotypes for further examination. \u00b6 We can use the \"anti_join\" function from the dplyr package to keep only the unique genes that appear only when using both genotypes. In [23]: Copied! S10_Ti_unique <- anti_join ( DEG_S10_Ti_filtered , DEG_S10_filtered , by = \"gene_ID\" ) %>% anti_join ( DEG_Ti_filtered , by = \"gene_ID\" ) S10_Ti_unique S10_Ti_unique < - anti_join(DEG_S10_Ti_filtered, DEG_S10_filtered, by=\"gene_ID\") %>% anti_join(DEG_Ti_filtered, by=\"gene_ID\") S10_Ti_unique A data.frame: 2 \u00d7 6 gene_ID logFC logCPM F PValue FDR g247 -1.412652 8.424523 10.877350 0.006535994 0.01500517 g360 1.180849 7.900076 9.804067 0.013809376 0.02813660 Linking the genes selected as differentially expressed back to the raw read counts \u00b6 How do the counts look for these genes, does it make sense that they are differentially expressed only when using the two genotypes? In [24]: Copied! Read_counts <- rownames_to_column ( Read_counts ) %>% rename ( gene_ID = rowname ) S10_Ti_unique_counts <- inner_join ( S10_Ti_unique [, c ( 1 , 2 , 6 )], Read_counts , by = \"gene_ID\" ) S10_Ti_unique_counts Read_counts < - rownames_to_column(Read_counts) %>% rename(gene_ID = rowname) S10_Ti_unique_counts < - inner_join(S10_Ti_unique[,c(1,2,6)], Read_counts, by=\"gene_ID\") S10_Ti_unique_counts A data.frame: 2 \u00d7 15 gene_ID logFC FDR S10_1_1 S10_1_2 S10_1_3 S10_2_1 S10_2_2 S10_2_3 TI_1_1 TI_1_2 TI_1_3 TI_2_1 TI_2_2 TI_2_3 g247 -1.412652 0.01500517 4 9 3 1 2 1 42 27 32 8 22 22 g360 1.180849 0.02813660 1 0 1 3 2 3 16 10 16 22 19 22 Adding functional annotation to DEGs \u00b6 Until now we looked only at gene IDs, but we can also add functional annotations to the DEGs. The functional annotations were generated using protein sequences and the EggNOG software. Identifying the molecular function of the differentially expressed genes can help us do a literature survey in order to check if any of the genes discovered have been previously identified as being involved in the cold response. In [25]: Copied! Functional_annotations <- read.delim ( \"../Data/Clover_Data/Functional_Annotations.txt\" ) S10_Ti_unique_FA <- inner_join ( S10_Ti_unique [, c ( 1 , 2 , 6 )], Functional_annotations , by = \"gene_ID\" ) S10_Ti_unique_FA Functional_annotations < - read.delim(\"../Data/Clover_Data/Functional_Annotations.txt\") S10_Ti_unique_FA < - inner_join(S10_Ti_unique[,c(1,2,6)], Functional_annotations, by=\"gene_ID\") S10_Ti_unique_FA A data.frame: 2 \u00d7 4 gene_ID logFC FDR Human.Readable.Description g247 -1.412652 0.01500517 Nitrogen regulatory protein P-II homolog g360 1.180849 0.02813660 Unknown protein Tasks and Questions Based on the results obtained in the analysis so far, would you change the cut-off for the FDR and logFC to be more strict or more permissive? Look back at the raw counts for different FDR and logFC values and set the thresholds as you find appropriate. You can also plot histograms with the FDR and logFC values. In [ ]: Copied! hist ( DEG_S10 $ table $ FDR , main = \"\" , xlab = \"FDR\" , breaks = 200 , xlim = range ( c ( 0 , 0.1 ))) hist(DEG_S10$table$FDR , main = \"\", xlab = \"FDR\", breaks= 200, xlim = range(c(0, 0.1))) In [ ]: Copied! hist ( DEG_S10 $ table $ logFC , main = \"\" , xlab = \"logFC\" , breaks = 50 , xlim = range ( c ( -6 , 6 ))) hist(DEG_S10$table$logFC , main = \"\", xlab = \"logFC\", breaks= 50, xlim = range(c(-6, 6))) Separate the upregulated and downregulated genes for each genotype and append functional annotations to them. Identify the genes that are commonly upregulated in S10 and Tienshan samples and the uniquely upregulated genes for each genotype. Why do you think some of the proteins appear in duplicates? To answer these questions, it may be convenient to save summary tables from R and open them in excel. See the code below for examples of how to do this. Files can be downloaded by right-clicking on the file name. If you are familiar with R functions, you are welcome to use those for counting. In [ ]: Copied! dir.create ( \"DEG_Output_tables\" , showWarnings = FALSE ) dir.create(\"DEG_Output_tables\", showWarnings = FALSE) In [ ]: Copied! #Create the table with the DEGs, Raw counts(just for the Ti samples in this case, change to columns (2:7) for the S10 samples) and Functional annotations DEG_Ti_counts_FA <- inner_join ( DEG_Ti_filtered [, c ( 1 , 2 , 6 )], Read_counts [, c ( 1 , 8 : 13 )], by = \"gene_ID\" ) %>% inner_join ( Functional_annotations , by = \"gene_ID\" ) #Write the table to file write.table ( DEG_Ti_counts_FA , file = \"DEG_Output_tables/Ti_Treatment_Control_DGE.txt\" , quote = FALSE , row.names = FALSE , sep = \"\\t\" ) #Display the first 10 rows of the table head ( DEG_Ti_counts_FA , n = 10 ) #Create the table with the DEGs, Raw counts(just for the Ti samples in this case, change to columns (2:7) for the S10 samples) and Functional annotations DEG_Ti_counts_FA < - inner_join(DEG_Ti_filtered[,c(1, 2, 6)], Read_counts[, c(1, 8:13)], by=\"gene_ID\") %>% inner_join(Functional_annotations, by=\"gene_ID\") #Write the table to file write.table(DEG_Ti_counts_FA, file = \"DEG_Output_tables/Ti_Treatment_Control_DGE.txt\", quote = FALSE, row.names = FALSE, sep = \"\\t\") #Display the first 10 rows of the table head(DEG_Ti_counts_FA, n=10) Example for filtering and counting the Up/Down genes. You can easily filter using the \"filter()\" function just by specifying the dataframe and a logical argument. In [ ]: Copied! DEG_Ti_up <- filter ( DEG_Ti_counts_FA , logFC > 0 ) print ( \"Nr of upregulated genes:\" ) print ( nrow ( DEG_Ti_up )) DEG_Ti_down <- filter ( DEG_Ti_counts_FA , logFC < 0 ) print ( \"Nr of downregulated genes:\" ) print ( nrow ( DEG_Ti_down )) DEG_Ti_up < - filter(DEG_Ti_counts_FA, logFC > 0) print(\"Nr of upregulated genes:\") print(nrow(DEG_Ti_up)) DEG_Ti_down < - filter(DEG_Ti_counts_FA, logFC < 0) print(\"Nr of downregulated genes:\") print(nrow(DEG_Ti_down)) You can use the \"inner_join\" and the \"anti_join\" functions from the dplyr package to select the common and unique genes for each genotype: example_file_joined <- inner_join(file1, file2, by=\"gene_ID\") Wrapping up \ud83c\udf89 \ud83c\udf89 \ud83c\udf89 \u00b6 In this notebook, you have worked with RNA-seq results for two white clover genotypes exposed to one night of cold treatment, aiming to identify genes that change their expression in response to the cold treatment. You have learned to perform exploratory data analysis of raw and normalized RNA-seq read counts. You have also performed differential gene expression using edgeR .","title":"3.3.Bulk RNA data analysis"},{"location":"nb/bulk.html#edger-data-filtering-and-normalization","text":"We will use the edgeR package to test for differentially expressed genes between our Control and Treatment Samples. You can read more about the edgeR package and its functionalities here . This exercise largely follows their user manual.","title":"EdgeR - Data filtering and Normalization"},{"location":"nb/bulk.html#file-processing","text":"The data for this exercise comes from the 12 tabular files with Reads per Gene counts generated by STAR Mapping in the raw-data alignment part of this course. We want to create a table where each column is a sample, and the content of the table are the read counts from STAR. We must merge the 12 files with Reads per Gene information into a single file. If you aligned datasets in the first notebook with jupyterlab, then you will find the files using the following command: In [2]: Copied! samples <- sort ( system ( \"find results/STAR_output/*_align_contigs_1_2 -name \\\"*ReadsPerGene.out.tab\\\"\" , intern = TRUE )) print ( samples ) Read_counts <- do.call ( cbind , lapply ( samples , function ( x ) read.delim ( file = x , header = FALSE ))) samples < - sort(system(\"find results/STAR_output/*_align_contigs_1_2 -name \\\"*ReadsPerGene.out.tab\\\"\", intern=TRUE)) print(samples) Read_counts < - do.call(cbind, lapply(samples, function(x) read.delim(file=x, header = FALSE))) [1] \"results/STAR_output/S10_align_contigs_1_2/S10_1_1ReadsPerGene.out.tab\" [2] \"results/STAR_output/S10_align_contigs_1_2/S10_1_2ReadsPerGene.out.tab\" [3] \"results/STAR_output/S10_align_contigs_1_2/S10_1_3ReadsPerGene.out.tab\" [4] \"results/STAR_output/S10_align_contigs_1_2/S10_2_1ReadsPerGene.out.tab\" [5] \"results/STAR_output/S10_align_contigs_1_2/S10_2_2ReadsPerGene.out.tab\" [6] \"results/STAR_output/S10_align_contigs_1_2/S10_2_3ReadsPerGene.out.tab\" [7] \"results/STAR_output/TI_align_contigs_1_2/TI_1_1ReadsPerGene.out.tab\" [8] \"results/STAR_output/TI_align_contigs_1_2/TI_1_2ReadsPerGene.out.tab\" [9] \"results/STAR_output/TI_align_contigs_1_2/TI_1_3ReadsPerGene.out.tab\" [10] \"results/STAR_output/TI_align_contigs_1_2/TI_2_1ReadsPerGene.out.tab\" [11] \"results/STAR_output/TI_align_contigs_1_2/TI_2_2ReadsPerGene.out.tab\" [12] \"results/STAR_output/TI_align_contigs_1_2/TI_2_3ReadsPerGene.out.tab\" If you aligned datasets interactively with Galaxy , then you will need to create the folder tabular_files into the results folder copy the tabular files from STAR into the created folder. Each file must have the sample name and end by ReadsPerGene.out.tab . For example S10_1_1ReadsPerGene.out.tab run the following commands removing the # symbol In [ ]: Copied! #samples <- sort(system(\"find results/tabular_files/ -name \\\"*ReadsPerGene.out.tab\\\"\", intern=TRUE)) #print(samples) #Read_counts <- do.call(cbind, lapply(samples, function(x) read.delim(file=x, header = FALSE))) #samples < - sort(system(\"find results/tabular_files/ -name \\\"*ReadsPerGene.out.tab\\\"\", intern=TRUE)) #print(samples) #Read_counts < - do.call(cbind, lapply(samples, function(x) read.delim(file=x, header = FALSE))) The data frame has genes as rows and samples as columns and stores the gene expression counts (value representing the total number of sequence reads that originated from a particular gene in a sample) for each of the 12 samples. This data frame should have 12 columns and 366 rows. In [3]: Copied! rownames ( Read_counts ) <- Read_counts [, 1 ] Read_counts <- Read_counts [ c ( 5 : nrow ( Read_counts )), c ( seq ( 2 , 46 , by = 4 ))] colnames ( Read_counts ) <- c ( \"S10_1_1\" , \"S10_1_2\" , \"S10_1_3\" , \"S10_2_1\" , \"S10_2_2\" , \"S10_2_3\" , \"TI_1_1\" , \"TI_1_2\" , \"TI_1_3\" , \"TI_2_1\" , \"TI_2_2\" , \"TI_2_3\" ) head ( Read_counts , n = 10 ) dim ( Read_counts ) # dimensions of the data frame rownames(Read_counts) < - Read_counts[,1] Read_counts < - Read_counts[c(5:nrow(Read_counts)), c(seq(2, 46, by=4))] colnames(Read_counts) < - c(\"S10_1_1\", \"S10_1_2\", \"S10_1_3\", \"S10_2_1\", \"S10_2_2\", \"S10_2_3\", \"TI_1_1\", \"TI_1_2\", \"TI_1_3\", \"TI_2_1\", \"TI_2_2\", \"TI_2_3\") head(Read_counts, n=10) dim(Read_counts) # dimensions of the data frame A data.frame: 10 \u00d7 12 S10_1_1 S10_1_2 S10_1_3 S10_2_1 S10_2_2 S10_2_3 TI_1_1 TI_1_2 TI_1_3 TI_2_1 TI_2_2 TI_2_3 g175 143 226 227 217 206 193 234 205 198 221 206 165 g176 17 12 15 8 14 18 29 19 30 6 11 23 g177 0 0 0 0 0 0 0 0 0 0 0 0 g178 0 0 0 0 0 0 0 0 2 2 1 2 g179 0 0 0 0 0 0 0 0 0 0 0 0 g180 2773 5223 4350 4139 3209 4644 1015 1313 889 2675 1023 2565 g181 473 679 583 437 390 450 642 700 697 650 459 410 g182 19 15 20 9 8 4 13 7 8 6 5 4 g183 0 0 0 0 0 0 0 0 0 0 0 0 g184 30 22 50 57 103 83 36 29 18 107 69 89 .list-inline {list-style: none; margin:0; padding: 0} .list-inline>li {display: inline-block} .list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex} 366 12 Import the Metadata table. This file, as its name suggests, contains information about each of the 12 RNA-seq samples, such as the treatment (Condition), genotype and replicate. Note that the order of the rows in the Metadata table should be the same as the columns in the Read_counts file generated above. In [4]: Copied! metadata <- read.csv ( \"../Data/Clover_Data/metadata.csv\" , sep = \";\" , row.names = 1 , stringsAsFactors = TRUE ) metadata metadata < - read.csv(\"../Data/Clover_Data/metadata.csv\", sep =\";\", row.names=1, stringsAsFactors=TRUE) metadata A data.frame: 12 \u00d7 3 Condition Genotype Replicate S10_1_1 Control S10 1 S10_1_2 Control S10 2 S10_1_3 Control S10 3 S10_2_1 Treatment S10 1 S10_2_2 Treatment S10 2 S10_2_3 Treatment S10 3 TI_1_1 Control Tienshan 1 TI_1_2 Control Tienshan 2 TI_1_3 Control Tienshan 3 TI_2_1 Treatment Tienshan 1 TI_2_2 Treatment Tienshan 2 TI_2_3 Treatment Tienshan 3 In order to aid the following steps, we will create a Group for each sample (a new column in the metadata) based on the Genotype&Condition of each sample and assign the three replicates to this group. In [5]: Copied! Group <- factor ( paste ( metadata $ Genotype , metadata $ Condition , sep = \"_\" )) metadata <- cbind ( metadata , Group = Group ) metadata Group < - factor(paste(metadata$Genotype, metadata$Condition, sep=\"_\")) metadata < - cbind(metadata,Group=Group) metadata A data.frame: 12 \u00d7 4 Condition Genotype Replicate Group S10_1_1 Control S10 1 S10_Control S10_1_2 Control S10 2 S10_Control S10_1_3 Control S10 3 S10_Control S10_2_1 Treatment S10 1 S10_Treatment S10_2_2 Treatment S10 2 S10_Treatment S10_2_3 Treatment S10 3 S10_Treatment TI_1_1 Control Tienshan 1 Tienshan_Control TI_1_2 Control Tienshan 2 Tienshan_Control TI_1_3 Control Tienshan 3 Tienshan_Control TI_2_1 Treatment Tienshan 1 Tienshan_Treatment TI_2_2 Treatment Tienshan 2 Tienshan_Treatment TI_2_3 Treatment Tienshan 3 Tienshan_Treatment","title":"File processing"},{"location":"nb/bulk.html#create-the-dgelist-object","text":"We will merge the read counts and the metadata into a list-based data object named DGEList, which can be manipulated as any list object in R. The main components of the DGEList object are the matrix \"counts\" containing our read per gene counts and a data.frame \"samples\" containing the metadata. Note that all the genes with zero counts across all samples were eliminated. In [6]: Copied! DGEList <- DGEList ( Read_counts , remove.zeros = TRUE ) DGEList $ samples $ Condition <- relevel ( metadata $ Condition , ref = \"Control\" ) DGEList $ samples $ Genotype <- metadata $ Genotype DGEList $ samples $ group <- metadata $ Group DGEList DGEList < - DGEList(Read_counts, remove.zeros = TRUE) DGEList$samples$Condition < - relevel(metadata$Condition, ref = \"Control\") DGEList$samples$Genotype < - metadata$Genotype DGEList$samples$group < - metadata$Group DGEList Removing 99 rows with all zero counts $counts A matrix: 267 \u00d7 12 of type int S10_1_1 S10_1_2 S10_1_3 S10_2_1 S10_2_2 S10_2_3 TI_1_1 TI_1_2 TI_1_3 TI_2_1 TI_2_2 TI_2_3 g175 143 226 227 217 206 193 234 205 198 221 206 165 g176 17 12 15 8 14 18 29 19 30 6 11 23 g178 0 0 0 0 0 0 0 0 2 2 1 2 g180 2773 5223 4350 4139 3209 4644 1015 1313 889 2675 1023 2565 g181 473 679 583 437 390 450 642 700 697 650 459 410 g182 19 15 20 9 8 4 13 7 8 6 5 4 g184 30 22 50 57 103 83 36 29 18 107 69 89 g185 0 2 0 0 0 0 0 1 0 0 0 1 g186 1 0 0 0 0 0 0 0 0 0 0 0 g187 1 1 2 0 1 1 58 49 57 28 32 29 g188 316 285 417 200 184 322 1276 855 1341 402 809 547 g190 14 18 29 25 17 31 7 6 12 20 7 6 g191 2 2 1 1 0 0 0 0 0 0 0 0 g192 1 1 1 1 1 0 6 10 2 3 8 2 g193 208 231 301 289 161 279 70 61 60 128 160 91 g194 40 41 37 44 17 43 8 10 7 8 105 25 g195 54 85 85 71 54 43 56 54 42 67 68 56 g196 77 120 118 186 152 177 5 9 5 42 20 34 g197 1 6 5 18 14 21 15 44 12 32 16 17 g198 14 27 10 11 2 2 6 6 1 2 2 0 g199 0 0 0 0 0 0 0 0 0 0 1 0 g200 57 80 94 11 8 23 89 47 67 5 47 5 g203 815 1188 1412 1139 1034 1127 911 852 662 892 781 784 g204 250 312 348 277 275 264 125 104 133 119 91 130 g205 109 132 181 140 114 151 89 73 66 78 46 50 g206 0 1 2 7 4 9 4 0 7 0 1 2 g207 0 1 0 0 4 3 1 3 4 4 3 3 g208 0 1 0 0 0 0 0 1 0 1 0 0 g209 0 0 0 0 0 0 0 0 1 0 0 0 g210 52 76 60 12 12 13 61 73 94 54 45 21 \u22ee \u22ee \u22ee \u22ee \u22ee \u22ee \u22ee \u22ee \u22ee \u22ee \u22ee \u22ee \u22ee g135 0 0 0 1 0 0 5 1 0 2 0 0 g139 0 1 1 0 0 0 12 13 11 4 3 3 g140 0 5 1 0 0 2 5 4 0 0 2 0 g141 177 217 224 131 117 166 117 85 67 80 79 77 g142 488 713 557 241 154 228 983 1057 1023 1075 794 933 g144 3319 3987 4763 2249 1996 2260 4405 3880 4585 5072 3618 3590 g146 293 330 350 913 775 731 108 92 115 431 151 226 g147 6 3 3 0 8 10 1 1 1 0 1 0 g148 19 30 24 15 4 12 23 19 25 10 19 8 g149 35 62 65 57 58 53 114 103 91 92 79 93 g150 263 411 278 752 1378 1300 106 112 70 516 200 508 g151 44 44 50 92 92 84 23 26 25 56 43 31 g152 242 258 294 425 381 521 15 8 8 16 21 15 g153 142 144 181 233 177 233 31 49 50 50 45 57 g155 0 4 2 1 0 0 1 0 3 0 0 1 g156 0 0 0 0 0 0 0 3 2 0 0 1 g157 6 1 3 223 52 152 4 7 4 153 47 155 g158 0 0 0 7 1 3 0 0 1 1 0 0 g161 25 38 18 1 9 2 63 23 43 0 24 6 g162 31 43 38 43 25 28 0 0 0 0 0 0 g163 1 0 12 37 8 18 9 4 8 16 13 15 g164 69 58 97 294 123 168 56 58 60 146 101 135 g165 203 376 594 1343 2062 1610 265 229 214 488 270 490 g167 2 0 0 2 0 0 0 0 0 0 0 0 g168 2 14 6 0 0 5 31 26 38 3 27 10 g169 0 0 1 0 1 1 0 0 0 0 0 0 g170 0 0 0 0 0 0 0 1 0 0 0 0 g171 0 5 0 0 0 5 0 0 0 1 0 0 g172 1 2 2 6 4 3 3 1 2 3 3 0 g173 80 107 120 78 138 121 67 86 69 96 105 105 $samples A data.frame: 12 \u00d7 5 group lib.size norm.factors Condition Genotype S10_1_1 S10_Control 43614 1 Control S10 S10_1_2 S10_Control 58803 1 Control S10 S10_1_3 S10_Control 62654 1 Control S10 S10_2_1 S10_Treatment 59909 1 Treatment S10 S10_2_2 S10_Treatment 56761 1 Treatment S10 S10_2_3 S10_Treatment 63152 1 Treatment S10 TI_1_1 Tienshan_Control 43387 1 Control Tienshan TI_1_2 Tienshan_Control 38355 1 Control Tienshan TI_1_3 Tienshan_Control 40304 1 Control Tienshan TI_2_1 Tienshan_Treatment 55509 1 Treatment Tienshan TI_2_2 Tienshan_Treatment 39909 1 Treatment Tienshan TI_2_3 Tienshan_Treatment 45008 1 Treatment Tienshan","title":"Create the DGEList object"},{"location":"nb/bulk.html#preliminary-data-analysis","text":"First, we will calculate the \"pseudoCounts\" as log2 of the reads per gene counts. This is not part of the actual differential gene expression analysis but is helpful for data exploration and quality assessment. We will look at a histogram of one of the samples and a boxplot representation of the log2 counts for all the 12 samples. Note that there are many genes with a low number of mapped reads and that there are differences between the average read counts for each library. In [7]: Copied! pseudoCounts <- log2 ( DGEList $ counts + 1 ) head ( pseudoCounts ) hist ( pseudoCounts [ , \"S10_1_1\" ], main = \"\" , xlab = \"Read counts\" ) boxplot ( pseudoCounts , col = \"gray\" , las = 3 , cex.names = 1 ) pseudoCounts < - log2(DGEList$counts + 1) head(pseudoCounts) hist(pseudoCounts[ ,\"S10_1_1\"], main = \"\", xlab = \"Read counts\") boxplot(pseudoCounts, col = \"gray\", las = 3, cex.names = 1) A matrix: 6 \u00d7 12 of type dbl S10_1_1 S10_1_2 S10_1_3 S10_2_1 S10_2_2 S10_2_3 TI_1_1 TI_1_2 TI_1_3 TI_2_1 TI_2_2 TI_2_3 g175 7.169925 7.826548 7.832890 7.768184 7.693487 7.599913 7.876517 7.686501 7.636625 7.794416 7.693487 7.375039 g176 4.169925 3.700440 4.000000 3.169925 3.906891 4.247928 4.906891 4.321928 4.954196 2.807355 3.584963 4.584963 g178 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 1.584963 1.584963 1.000000 1.584963 g180 11.437752 12.350939 12.087131 12.015415 11.648358 12.181463 9.988685 10.359750 9.797662 11.385862 10.000000 11.325305 g181 8.888743 9.409391 9.189825 8.774787 8.611025 8.816984 9.328675 9.453271 9.447083 9.346514 8.845490 8.682995 g182 4.321928 4.000000 4.392317 3.321928 3.169925 2.321928 3.807355 3.000000 3.169925 2.807355 2.584963 2.321928 We can also create a PCA plot of the samples in order to assess the differences between the Genotypes and Conditions, but also between the replicates. In this plot the samples that are similar cluster together, while samples that are different are further apart. In this type of plot, we would expect samples from the same group (the three replicates for each sample) to exhibit a similar gene expression profile thus clustering together while being separated from the other samples. In [8]: Copied! resPCA <- pca ( t ( pseudoCounts ), ncomp = 6 ) plotIndiv ( resPCA , group = metadata $ Genotype , pch = metadata $ Condition , legend = T , legend.title = 'Genotype' , legend.title.pch = 'Condition' , title = 'PCA plot raw counts' , style = 'ggplot2' , size.xlabel = 10 , size.ylabel = 10 ) resPCA < - pca(t(pseudoCounts), ncomp = 6) plotIndiv(resPCA, group = metadata$Genotype, pch=metadata$Condition, legend = T, legend.title = 'Genotype', legend.title.pch = 'Condition', title = 'PCA plot raw counts', style = 'ggplot2', size.xlabel = 10, size.ylabel = 10)","title":"Preliminary data analysis"},{"location":"nb/bulk.html#filtering-the-lowly-expressed-genes","text":"As seen previously, many genes have a low number of read counts in our samples. The genes with very low counts across all libraries provide little evidence for differential expression, thus we should eliminate these genes before the analysis. One of the filtering methods we can use is the \"filterByExpr\" function provided by the edgeR package. By default, this function keeps only the genes that have at least 10 reads per group, but other cutoffs can also be applied. In [9]: Copied! keep <- filterByExpr ( DGEList , group = metadata $ Group ) #create the filter DGEList <- DGEList [ keep , , keep.lib.sizes = FALSE ] #apply the filter to on the DGEList object table ( keep ) #Check the number of genes that passed the filter keep < - filterByExpr(DGEList, group=metadata$Group) #create the filter DGEList < - DGEList[keep, , keep.lib.sizes=FALSE] #apply the filter to on the DGEList object table(keep) #Check the number of genes that passed the filter keep FALSE TRUE 104 163","title":"Filtering the lowly expressed genes"},{"location":"nb/bulk.html#normalization","text":"As we are working with multiple samples we need to normalize the read counts per gene in order to account for compositional and technical differences between the 12 RNA-seq libraries. For this, we will calculate normalization factors using the trimmed mean of M-values (TMM) method. You can read more about different normalization methods in the user manual. Note that running \"calcNormFactors\" does not change the actual reads per gene counts, it just fills the \"norm.factors\" column in DGEList$samples . These factors will be used to scale the read counts for each library. From the user guide: \"A normalization factor below one indicates that a small number of high count genes are monopolizing the sequencing, causing the counts for other genes to be lower than would be usual given the library size.\" In [10]: Copied! DGEList <- calcNormFactors ( DGEList , method = \"RLE\" ) DGEList $ samples DGEList < - calcNormFactors(DGEList, method=\"RLE\") DGEList$samples A data.frame: 12 \u00d7 5 group lib.size norm.factors Condition Genotype S10_1_1 S10_Control 43506 1.0897129 Control S10 S10_1_2 S10_Control 58660 1.0385093 Control S10 S10_1_3 S10_Control 62512 1.0362962 Control S10 S10_2_1 S10_Treatment 59741 0.9802860 Treatment S10 S10_2_2 S10_Treatment 56653 0.8418644 Treatment S10 S10_2_3 S10_Treatment 63008 0.8949144 Treatment S10 TI_1_1 Tienshan_Control 43174 1.0618440 Control Tienshan TI_1_2 Tienshan_Control 38203 1.0431678 Control Tienshan TI_1_3 Tienshan_Control 40178 1.0676351 Control Tienshan TI_2_1 Tienshan_Treatment 55391 0.9048196 Treatment Tienshan TI_2_2 Tienshan_Treatment 39810 1.1235077 Treatment Tienshan TI_2_3 Tienshan_Treatment 44910 0.9603761 Treatment Tienshan","title":"Normalization"},{"location":"nb/bulk.html#normalized-counts-exploratory-data-analysis","text":"For data analysis purposes normalized log2 counts can be extracted from the DGEList object using the function CPM (counts per million). We will generate the same plots as for the raw counts in order to compare the data before and after normalization. Do the plots for normalized counts look different compared with the plots computed before data filtering and normalization? In [11]: Copied! pseudoNormCounts <- cpm ( DGEList , log = TRUE , prior.count = 1 ) head ( pseudoNormCounts ) hist ( pseudoNormCounts [ , \"S10_1_1\" ], main = \"\" , xlab = \"counts\" ) boxplot ( pseudoNormCounts , col = \"gray\" , las = 3 , cex.names = 1 ) resPCA <- pca ( t ( pseudoNormCounts ), ncomp = 6 ) plotIndiv ( resPCA , group = metadata $ Genotype , pch = metadata $ Condition , legend = T , legend.title = 'Genotype' , legend.title.pch = 'Condition' , title = 'PCA plot normalized counts' , style = 'ggplot2' , size.xlabel = 10 , size.ylabel = 10 ) pseudoNormCounts < - cpm(DGEList, log = TRUE, prior.count = 1) head(pseudoNormCounts) hist(pseudoNormCounts[ ,\"S10_1_1\"], main = \"\", xlab = \"counts\") boxplot(pseudoNormCounts, col = \"gray\", las = 3, cex.names = 1) resPCA < - pca(t(pseudoNormCounts), ncomp = 6) plotIndiv(resPCA, group = metadata$Genotype, pch=metadata$Condition, legend = T, legend.title = 'Genotype', legend.title.pch = 'Condition', title = 'PCA plot normalized counts', style = 'ggplot2', size.xlabel = 10, size.ylabel = 10) A matrix: 6 \u00d7 12 of type dbl S10_1_1 S10_1_2 S10_1_3 S10_2_1 S10_2_2 S10_2_3 TI_1_1 TI_1_2 TI_1_3 TI_2_1 TI_2_2 TI_2_3 g175 11.568005 11.864813 11.782957 11.863090 12.083127 11.749272 12.323049 12.334198 12.178544 12.112851 12.175386 11.908906 g176 8.564108 7.760868 7.974199 7.290213 8.292080 8.405708 9.349782 8.956131 9.490388 7.125494 8.054467 9.111569 g180 15.836359 16.387906 16.035457 16.109276 16.038322 16.329943 14.435626 15.008665 14.340400 15.704307 14.482504 15.860317 g181 13.287211 13.446752 13.138771 12.869139 13.000828 12.965820 13.775545 14.101988 13.989757 13.664955 13.327806 13.217591 g182 8.716557 8.056048 8.360376 7.439590 7.551908 6.505541 8.245462 7.611500 7.689402 7.125494 7.041192 6.815835 g184 9.350249 8.573811 9.628852 9.955925 11.089735 10.542877 9.653135 9.546090 8.779819 11.073313 10.609695 11.024686","title":"Normalized counts - Exploratory Data analysis"},{"location":"nb/bulk.html#edger-testing-for-differentially-expressed-genes-degs","text":"We use edgeR for Differential Gene Expression using the GLM method (Generalized linear model) with treatment and genotype effects After we concluded the exploratory data analysis and the filtering and normalization steps we can now start testing for differentially expressed genes between our samples. For this, we first need to define a \"design matrix\" which describes our experimental design. We construct a matrix containing samples as rows and our previously defined groups (S10_Control, S10_Treatment, Tienshan_Control, Tienshan_Treatment) as columns. The samples belonging to each group are assigned a \"1\" in this matrix. In [12]: Copied! design.matrix <- model.matrix ( ~ 0 + Group ) rownames ( design.matrix ) <- colnames ( DGEList ) colnames ( design.matrix ) <- levels ( metadata $ Group ) design.matrix design.matrix < - model.matrix(~0+Group) rownames(design.matrix) < - colnames(DGEList) colnames(design.matrix) < - levels(metadata$Group) design.matrix A matrix: 12 \u00d7 4 of type dbl S10_Control S10_Treatment Tienshan_Control Tienshan_Treatment S10_1_1 1 0 0 0 S10_1_2 1 0 0 0 S10_1_3 1 0 0 0 S10_2_1 0 1 0 0 S10_2_2 0 1 0 0 S10_2_3 0 1 0 0 TI_1_1 0 0 1 0 TI_1_2 0 0 1 0 TI_1_3 0 0 1 0 TI_2_1 0 0 0 1 TI_2_2 0 0 0 1 TI_2_3 0 0 0 1 For easier pairwise comparisons between the groups we can create some \"contrasts\" using the \"makeContrasts\" function. This will tell the program which samples we will like to consider as our \"Control\" and which as our \"Treatment\" groups. The Control groups will be assigned a \"-1\" and the Treatment groups a \"1\". When using multiple Control and Treatment samples in the same contrast we need to devide by the number of samples used as for example in the contrast S10_Tienshan, where we use both S10 and Ti samples. For example, the contrast S10 = S10_Treatment - S10_Control will test for differentially expressed genes between the S10 treatment samples vs the S10 Control samples. In [13]: Copied! contrasts <- makeContrasts ( S10 = S10_Treatment - S10_Control , Tienshan = Tienshan_Treatment - Tienshan_Control , S10_Tienshan = ( S10_Treatment + Tienshan_Treatment ) / 2 - ( S10_Control + Tienshan_Control ) / 2 , levels = design.matrix ) contrasts contrasts < - makeContrasts( S10 = S10_Treatment-S10_Control, Tienshan = Tienshan_Treatment-Tienshan_Control, S10_Tienshan= (S10_Treatment+Tienshan_Treatment)/2 - (S10_Control+Tienshan_Control)/2, levels=design.matrix) contrasts A matrix: 4 \u00d7 3 of type dbl S10 Tienshan S10_Tienshan S10_Control -1 0 -0.5 S10_Treatment 1 0 0.5 Tienshan_Control 0 -1 -0.5 Tienshan_Treatment 0 1 0.5 Any ideas for other contrasts that migth be interesting to explore? The following cell will run two mandatory steps in order to fit a model in edgeR. First, the dispersion needs to be estimated, this is a measure of the degree of inter-library variation for each gene. Afterwards, a QL ( quasi-likelihood) model representing the sample design is fitted using the \"glmQLFit\" function. You can read more about these steps in the edgeR user guide. In [14]: Copied! DGEList <- estimateDisp ( DGEList , design.matrix ) fit <- glmQLFit ( DGEList , design.matrix ) DGEList < - estimateDisp(DGEList, design.matrix) fit < - glmQLFit(DGEList, design.matrix)","title":"EdgeR - Testing for Differentially expressed genes (DEGs)"},{"location":"nb/bulk.html#degs-for-white-clover-s10-plants-treatment-vs-control","text":"Find DEGs for White clover S10 plants in Treatment condition compared with the Control condition We will test for differentially expressed genes using the quasi-likelihood F-tests method. This is easily done just by running the \"glmQFTest\" function on the fit model and selecting one of the contrasts we created above (in this case the \"S10\" contrast). Next, we will use the topTags function to extract the information about all the genes. In [15]: Copied! glmqlf_S10 <- glmQLFTest ( fit , contrast = contrasts [, \"S10\" ]) DEG_S10 <- topTags ( glmqlf_S10 , n = nrow ( DGEList $ counts )) DEG_S10 glmqlf_S10 < - glmQLFTest(fit, contrast=contrasts[,\"S10\"]) DEG_S10 < - topTags(glmqlf_S10, n = nrow(DGEList$counts)) DEG_S10 $table A data.frame: 163 \u00d7 5 logFC logCPM F PValue FDR g39 3.5861702 14.124630 336.66158 5.227331e-10 8.520550e-08 g232 4.0494125 11.950441 157.50997 3.685988e-08 2.591779e-06 g6 2.3225347 12.999541 150.27854 4.770146e-08 2.591779e-06 g350 2.0530029 13.324566 97.56453 4.890245e-07 1.992775e-05 g99 -1.5387874 11.681004 85.17478 9.976659e-07 3.195090e-05 g142 -1.4235276 13.830692 82.52308 1.176107e-06 3.195090e-05 g240 1.3603712 14.708794 79.98114 1.383201e-06 3.220882e-05 g269 -1.4191681 11.834217 75.66563 1.841279e-06 3.751606e-05 g26 2.1254241 11.366827 68.55898 3.047273e-06 5.518949e-05 g129 -5.3324794 8.679594 60.45459 5.738115e-06 9.353127e-05 g272 3.1405978 13.860652 59.08187 6.432977e-06 9.532503e-05 g157 5.3813801 10.414972 57.03712 7.659515e-06 1.040417e-04 g42 -1.9856547 9.962762 53.97164 1.005146e-05 1.260298e-04 g165 2.2666761 13.690016 51.66777 1.243606e-05 1.447913e-04 g101 3.1488142 13.181625 48.21727 1.736838e-05 1.887363e-04 g15 2.7410696 10.684840 47.29143 1.906062e-05 1.941800e-04 g210 -2.2430064 9.993002 46.52627 2.060621e-05 1.975772e-04 g299 -2.3509276 10.984491 45.75893 2.230566e-05 2.019901e-04 g103 2.6810410 10.579777 44.26963 2.609599e-05 2.238762e-04 g144 -0.7963477 16.186987 43.17707 2.936057e-05 2.392886e-04 g77 1.0984806 14.118231 41.46740 3.548119e-05 2.754016e-04 g125 -1.3099534 10.195970 39.85537 4.266319e-05 3.160955e-04 g298 -2.0471647 9.837072 33.49832 9.395967e-05 6.658881e-04 g104 2.4081173 9.219160 38.54240 1.121281e-04 7.615370e-04 g122 -0.6485441 12.457152 30.90261 1.341148e-04 8.744287e-04 g164 1.4415064 11.156457 30.50383 1.419308e-04 8.897970e-04 g146 1.3983889 12.824184 29.92911 1.541544e-04 9.306356e-04 g313 2.2492040 13.614815 28.41728 1.926822e-04 1.121686e-03 g150 1.9650641 13.229706 27.90020 2.083770e-04 1.171222e-03 g13 -0.6522187 13.101579 26.45593 2.608176e-04 1.417109e-03 \u22ee \u22ee \u22ee \u22ee \u22ee \u22ee g96 -1.873792302 8.939397 0.4844512655 0.4999546 0.6041584 g10 0.233134670 14.382992 0.4834790913 0.5003766 0.6041584 g253 0.052426914 13.741219 0.3075042687 0.5896191 0.7066759 g289 -0.212573283 9.390864 0.2887857862 0.6010328 0.7150974 g312 0.093543041 10.849110 0.2666731965 0.6151559 0.7265972 g301 -0.102000599 10.135184 0.2572520652 0.6214023 0.7274503 g221 -0.277095720 8.469909 0.2522121463 0.6248039 0.7274503 g162 -0.145155616 8.517263 0.2278112475 0.6463283 0.7413085 g50 -0.170907352 8.749274 0.2158314533 0.6507284 0.7413085 g203 0.062836598 14.227601 0.2124612164 0.6532634 0.7413085 g88 0.045949497 14.235850 0.2103049178 0.6548983 0.7413085 g355 -0.287897714 8.890754 0.1803234432 0.6787703 0.7630314 g204 -0.055975779 11.947887 0.1408988543 0.7140799 0.7972262 g306 0.054269738 11.294732 0.0923937800 0.7664733 0.8498990 g193 0.049946343 11.682386 0.0642557849 0.8042743 0.8857886 g205 0.037877227 10.986961 0.0473457745 0.8314821 0.9029713 g194 -0.142990391 9.403497 0.0447552162 0.8360811 0.9029713 g362 0.025629967 11.336529 0.0415198735 0.8420254 0.9029713 g315 0.027958374 11.878331 0.0409766489 0.8430470 0.9029713 g180 0.059680256 15.711409 0.0386140448 0.8475743 0.9029713 g282 -0.026492114 11.756078 0.0247991469 0.8775434 0.9288284 g176 -0.061055115 8.587203 0.0144510429 0.9063467 0.9531258 g126 0.061707217 8.311212 0.0024786868 0.9648334 1.0000000 g128 0.063825269 7.671043 0.0019414812 0.9655950 1.0000000 g227 -0.025740630 7.939955 0.0011908008 0.9730516 1.0000000 g121 0.002660852 12.896620 0.0005233398 0.9821327 1.0000000 g284 0.000000000 8.538720 0.0000000000 1.0000000 1.0000000 g116 0.000000000 7.180297 0.0000000000 1.0000000 1.0000000 g117 0.000000000 7.033307 0.0000000000 1.0000000 1.0000000 g118 0.000000000 7.515646 0.0000000000 1.0000000 1.0000000 $adjust.method 'BH' $comparison '-1*S10_Control 1*S10_Treatment' $test 'glm' We have now created a table with certain parameters calculated for each of the genes analysed. logFC represents the base 2 logarithm of the fold change and it shows us how much the expression of the gene has changed between the two conditions. A logFC of 1 means a doubling in the read per gene count between the control and treatment samples. The genes with a logFC higher than 0 are upregulated while the genes with a logFC lower than 0 are downregulated between the control and treatment samples. logCPM represents the average log2-counts-per-million, the abundance of the gene. F - F-statistic. PValue is the raw p-value. FDR (The false discovery rate) represents the adjusted p-value and is calculated using Benjamini and Hochberg's algorithm. It controls the rate of false positive values under multiple testing. Usually, a threshold of under 5% is set for the FDR. The important information for us in this table is stored in the \"logFC\" and the \"FDR\" columns. The top DE genes have small FDR values and large fold changes Many of the genes in the samples are uninteresting for us, as they have a high FDR and/or low logFC values so we cannot consider them as differentially expressed. We will apply a filtering step in order to keep only the statistically significant genes. We will filter out the genes with an FDR higher than 0.05 and an absolute logFC lower than 1. In [16]: Copied! DEG_S10_filtered <- DEG_S10 $ table [ DEG_S10 $ table $ FDR < 0.05 & abs ( DEG_S10 $ table $ logFC ) > 1 ,] #Filtering DEG_S10_filtered <- rownames_to_column ( DEG_S10_filtered ) %>% rename ( gene_ID = rowname ) #Adding the gene_ID column head ( DEG_S10_filtered ) nrow ( DEG_S10_filtered ) # finding the number of genes that passed the filter DEG_S10_filtered < - DEG_S10$table[DEG_S10$table$FDR < 0.05 & abs(DEG_S10$table$logFC) > 1,] #Filtering DEG_S10_filtered < - rownames_to_column(DEG_S10_filtered) %>% rename(gene_ID = rowname) #Adding the gene_ID column head(DEG_S10_filtered) nrow(DEG_S10_filtered) # finding the number of genes that passed the filter A data.frame: 6 \u00d7 6 gene_ID logFC logCPM F PValue FDR 1 g39 3.586170 14.12463 336.66158 5.227331e-10 8.520550e-08 2 g232 4.049413 11.95044 157.50997 3.685988e-08 2.591779e-06 3 g6 2.322535 12.99954 150.27854 4.770146e-08 2.591779e-06 4 g350 2.053003 13.32457 97.56453 4.890245e-07 1.992775e-05 5 g99 -1.538787 11.68100 85.17478 9.976659e-07 3.195090e-05 6 g142 -1.423528 13.83069 82.52308 1.176107e-06 3.195090e-05 52 We can also visualize the selected genes by plotting a Smear plot or a Volcano plot. The Genes that passed the filter are coloured in red, and the top 10 genes with the lowest FDR value are labelled with their gene ID. We can see that the majority of the genes analysed are either not statistically significant or have a very small logFC. In [17]: Copied! plotSmear ( glmqlf_S10 , de.tags = rownames ( DEG_S10 $ table )[ which ( DEG_S10 $ table $ FDR < 0.05 & abs ( DEG_S10 $ table $ logFC ) > 1 )]) text ( x = DEG_S10_filtered $ logCPM [ 1 : 10 ], y = DEG_S10_filtered $ logFC [ 1 : 10 ], labels = DEG_S10_filtered $ gene_ID [ 1 : 10 ], cex = 0.7 , pos = 1 ) abline ( h = c ( -1 , 1 ), col = \"blue\" ) plotSmear(glmqlf_S10, de.tags = rownames(DEG_S10$table)[which(DEG_S10$table$FDR < 0.05 & abs(DEG_S10$table$logFC) > 1)]) text(x=DEG_S10_filtered$logCPM[1:10], y=DEG_S10_filtered$logFC[1:10], labels=DEG_S10_filtered$gene_ID[1:10], cex=0.7, pos=1) abline(h = c(-1, 1), col = \"blue\") We can also create a heatmap with the log2 read counts of the selected differentially expressed genes so that we can visualise the differences in normalized counts between the Control and the Treatment samples. The genes are ordered by the FDR value. In [18]: Copied! annot_col <- data.frame ( row.names = colnames ( pseudoNormCounts )[ 1 : 6 ], Condition = c ( rep ( \"Control\" , 3 ), rep ( \"Treatment\" , 3 ))) pheatmap ( as.matrix ( pseudoNormCounts [ DEG_S10_filtered $ gene_ID , c ( 1 : 6 )]), cluster_rows = F , cluster_col = F , annotation_col = annot_col ) annot_col < - data.frame(row.names = colnames(pseudoNormCounts)[1:6], Condition = c(rep(\"Control\", 3),rep( \"Treatment\", 3))) pheatmap(as.matrix(pseudoNormCounts[DEG_S10_filtered$gene_ID,c(1:6)]), cluster_rows = F, cluster_col = F, annotation_col = annot_col)","title":"DEGs for White clover S10 plants Treatment vs Control"},{"location":"nb/bulk.html#degs-for-white-clover-tienshan-treatment-vs-control","text":"We can do this using the same functions as above and changing the contrast. This time we will directly filter the differentially expressed genes using the same parameters as for the S10 samples. In [19]: Copied! glmqlf_Ti <- glmQLFTest ( fit , contrast = contrasts [, \"Tienshan\" ]) DEG_Ti <- topTags ( glmqlf_Ti , n = nrow ( DGEList $ counts )) DEG_Ti_filtered <- DEG_Ti $ table [ DEG_Ti $ table $ FDR < 0.05 & abs ( DEG_Ti $ table $ logFC ) > 1 ,] DEG_Ti_filtered <- rownames_to_column ( DEG_Ti_filtered ) %>% rename ( gene_ID = rowname ) print ( head ( DEG_Ti_filtered )) print ( \"Nr of differentially expressed genes:\" ) print ( nrow ( DEG_Ti_filtered )) glmqlf_Ti < - glmQLFTest(fit, contrast=contrasts[,\"Tienshan\"]) DEG_Ti < - topTags(glmqlf_Ti, n = nrow(DGEList$counts)) DEG_Ti_filtered < - DEG_Ti$table[DEG_Ti$table$FDR < 0.05 & abs(DEG_Ti$table$logFC) > 1,] DEG_Ti_filtered < - rownames_to_column(DEG_Ti_filtered) %>% rename(gene_ID = rowname) print(head(DEG_Ti_filtered)) print(\"Nr of differentially expressed genes:\") print(nrow(DEG_Ti_filtered)) gene_ID logFC logCPM F PValue FDR 1 g234 -3.348667 13.00417 278.01679 1.547626e-09 2.522630e-07 2 g269 -1.885843 11.83422 127.39949 1.172628e-07 9.556922e-06 3 g26 2.423725 11.36683 74.33983 2.016212e-06 8.910761e-05 4 g101 4.303290 13.18162 73.17140 2.186690e-06 8.910761e-05 5 g272 3.391225 13.86065 66.80131 3.475790e-06 9.840357e-05 6 g99 -1.430395 11.68100 66.25814 3.622217e-06 9.840357e-05 [1] \"Nr of differentially expressed genes:\" [1] 42 We can now plot a Venn diagram with the DEGs for the two genotypes in order to observe the number of common and specific differentially expressed genes between the two genotypes as response to the cold exposure. You can see that a high percentage of the identified genes are common for the two genotypes, while each genotype has also specific genes. In [20]: Copied! vd <- venn.diagram ( x = list ( DEG_S10_filtered $ gene_ID , DEG_Ti_filtered $ gene_ID ), category.names = c ( \"S10\" , \"Tienshan\" ), lwd = 4 , fill = c ( \"cornflowerblue\" , \"yellowgreen\" ), filename = NULL , cat.cex = 1 , cat.fontface = \"bold\" , output = TRUE ) grid.draw ( vd ) vd < - venn.diagram( x = list(DEG_S10_filtered$gene_ID, DEG_Ti_filtered$gene_ID), category.names = c(\"S10\" , \"Tienshan\"), lwd = 4, fill = c(\"cornflowerblue\", \"yellowgreen\"), filename = NULL, cat.cex = 1, cat.fontface = \"bold\", output=TRUE ) grid.draw(vd)","title":"DEGs for White clover Tienshan Treatment vs Control"},{"location":"nb/bulk.html#degs-for-s10tienshan-treatment-vs-control-wo-genotype-effects","text":"Until now we tested for DEGs specific for each of the two genotypes under cold treatment. We can also run a test where we ignore the genotype and just test for the differences in the cold response. Consider the counts for both genotypes as a single dataset using the previously created contrast \"S10_Tienshan\". Do the results look different compared with the previous tests? In [21]: Copied! glmqlf_S10_Ti <- glmQLFTest ( fit , contrast = contrasts [, \"S10_Tienshan\" ]) DEG_S10_Ti <- topTags ( glmqlf_S10_Ti , n = nrow ( DGEList $ counts )) DEG_S10_Ti_filtered <- DEG_S10_Ti $ table [ DEG_S10_Ti $ table $ FDR < 0.05 & abs ( DEG_S10_Ti $ table $ logFC ) > 1 ,] DEG_S10_Ti_filtered <- rownames_to_column ( DEG_S10_Ti_filtered ) %>% rename ( gene_ID = rowname ) print ( head ( DEG_S10_Ti_filtered )) print ( \"Nr of differentially expressed genes:\" ) print ( nrow ( DEG_S10_Ti_filtered )) glmqlf_S10_Ti < - glmQLFTest(fit, contrast=contrasts[,\"S10_Tienshan\"]) DEG_S10_Ti < - topTags(glmqlf_S10_Ti, n = nrow(DGEList$counts)) DEG_S10_Ti_filtered < - DEG_S10_Ti$table[DEG_S10_Ti$table$FDR < 0.05 & abs(DEG_S10_Ti$table$logFC) > 1,] DEG_S10_Ti_filtered < - rownames_to_column(DEG_S10_Ti_filtered) %>% rename(gene_ID = rowname) print(head(DEG_S10_Ti_filtered)) print(\"Nr of differentially expressed genes:\") print(nrow(DEG_S10_Ti_filtered)) gene_ID logFC logCPM F PValue FDR 1 g39 2.057128 14.12463 246.6519 3.039491e-09 4.954370e-07 2 g232 3.261008 11.95044 203.4580 8.934889e-09 5.288546e-07 3 g269 -1.652505 11.83422 200.3564 9.733520e-09 5.288546e-07 4 g6 1.714849 12.99954 166.2297 2.740066e-08 1.116577e-06 5 g99 -1.484591 11.68100 150.3246 4.762148e-08 1.552460e-06 6 g26 2.274575 11.36683 142.8625 6.290957e-08 1.623160e-06 [1] \"Nr of differentially expressed genes:\" [1] 52 Plot the results from the 3 tests in a Venn diagram to visualize the number of common and unique genes In [22]: Copied! vd <- venn.diagram ( x = list ( DEG_S10_filtered $ gene_ID , DEG_Ti_filtered $ gene_ID , DEG_S10_Ti_filtered $ gene_ID ), category.names = c ( \"S10\" , \"Tienshan\" , \"S10_Tienshan\" ), lwd = 3 , fill = c ( \"cornflowerblue\" , \"yellowgreen\" , \"thistle3\" ), filename = NULL , cat.cex = 1 , cat.fontface = \"bold\" , output = TRUE ) grid.draw ( vd ) vd < - venn.diagram( x = list(DEG_S10_filtered$gene_ID, DEG_Ti_filtered$gene_ID, DEG_S10_Ti_filtered$gene_ID), category.names = c(\"S10\" , \"Tienshan\", \"S10_Tienshan\"), lwd = 3, fill = c(\"cornflowerblue\", \"yellowgreen\", \"thistle3\"), filename = NULL, cat.cex = 1, cat.fontface = \"bold\", output=TRUE ) grid.draw(vd)","title":"DEGs for S10+Tienshan Treatment vs Control w/o genotype effects"},{"location":"nb/bulk.html#explore-dge-results","text":"","title":"Explore DGE results"},{"location":"nb/bulk.html#select-the-genes-which-appear-only-in-the-analysis-using-both-genotypes-for-further-examination","text":"We can use the \"anti_join\" function from the dplyr package to keep only the unique genes that appear only when using both genotypes. In [23]: Copied! S10_Ti_unique <- anti_join ( DEG_S10_Ti_filtered , DEG_S10_filtered , by = \"gene_ID\" ) %>% anti_join ( DEG_Ti_filtered , by = \"gene_ID\" ) S10_Ti_unique S10_Ti_unique < - anti_join(DEG_S10_Ti_filtered, DEG_S10_filtered, by=\"gene_ID\") %>% anti_join(DEG_Ti_filtered, by=\"gene_ID\") S10_Ti_unique A data.frame: 2 \u00d7 6 gene_ID logFC logCPM F PValue FDR g247 -1.412652 8.424523 10.877350 0.006535994 0.01500517 g360 1.180849 7.900076 9.804067 0.013809376 0.02813660","title":"Select the genes which appear only in the analysis using both genotypes for further examination."},{"location":"nb/bulk.html#linking-the-genes-selected-as-differentially-expressed-back-to-the-raw-read-counts","text":"How do the counts look for these genes, does it make sense that they are differentially expressed only when using the two genotypes? In [24]: Copied! Read_counts <- rownames_to_column ( Read_counts ) %>% rename ( gene_ID = rowname ) S10_Ti_unique_counts <- inner_join ( S10_Ti_unique [, c ( 1 , 2 , 6 )], Read_counts , by = \"gene_ID\" ) S10_Ti_unique_counts Read_counts < - rownames_to_column(Read_counts) %>% rename(gene_ID = rowname) S10_Ti_unique_counts < - inner_join(S10_Ti_unique[,c(1,2,6)], Read_counts, by=\"gene_ID\") S10_Ti_unique_counts A data.frame: 2 \u00d7 15 gene_ID logFC FDR S10_1_1 S10_1_2 S10_1_3 S10_2_1 S10_2_2 S10_2_3 TI_1_1 TI_1_2 TI_1_3 TI_2_1 TI_2_2 TI_2_3 g247 -1.412652 0.01500517 4 9 3 1 2 1 42 27 32 8 22 22 g360 1.180849 0.02813660 1 0 1 3 2 3 16 10 16 22 19 22","title":"Linking the genes selected as differentially expressed back to the raw read counts"},{"location":"nb/bulk.html#adding-functional-annotation-to-degs","text":"Until now we looked only at gene IDs, but we can also add functional annotations to the DEGs. The functional annotations were generated using protein sequences and the EggNOG software. Identifying the molecular function of the differentially expressed genes can help us do a literature survey in order to check if any of the genes discovered have been previously identified as being involved in the cold response. In [25]: Copied! Functional_annotations <- read.delim ( \"../Data/Clover_Data/Functional_Annotations.txt\" ) S10_Ti_unique_FA <- inner_join ( S10_Ti_unique [, c ( 1 , 2 , 6 )], Functional_annotations , by = \"gene_ID\" ) S10_Ti_unique_FA Functional_annotations < - read.delim(\"../Data/Clover_Data/Functional_Annotations.txt\") S10_Ti_unique_FA < - inner_join(S10_Ti_unique[,c(1,2,6)], Functional_annotations, by=\"gene_ID\") S10_Ti_unique_FA A data.frame: 2 \u00d7 4 gene_ID logFC FDR Human.Readable.Description g247 -1.412652 0.01500517 Nitrogen regulatory protein P-II homolog g360 1.180849 0.02813660 Unknown protein Tasks and Questions Based on the results obtained in the analysis so far, would you change the cut-off for the FDR and logFC to be more strict or more permissive? Look back at the raw counts for different FDR and logFC values and set the thresholds as you find appropriate. You can also plot histograms with the FDR and logFC values. In [ ]: Copied! hist ( DEG_S10 $ table $ FDR , main = \"\" , xlab = \"FDR\" , breaks = 200 , xlim = range ( c ( 0 , 0.1 ))) hist(DEG_S10$table$FDR , main = \"\", xlab = \"FDR\", breaks= 200, xlim = range(c(0, 0.1))) In [ ]: Copied! hist ( DEG_S10 $ table $ logFC , main = \"\" , xlab = \"logFC\" , breaks = 50 , xlim = range ( c ( -6 , 6 ))) hist(DEG_S10$table$logFC , main = \"\", xlab = \"logFC\", breaks= 50, xlim = range(c(-6, 6))) Separate the upregulated and downregulated genes for each genotype and append functional annotations to them. Identify the genes that are commonly upregulated in S10 and Tienshan samples and the uniquely upregulated genes for each genotype. Why do you think some of the proteins appear in duplicates? To answer these questions, it may be convenient to save summary tables from R and open them in excel. See the code below for examples of how to do this. Files can be downloaded by right-clicking on the file name. If you are familiar with R functions, you are welcome to use those for counting. In [ ]: Copied! dir.create ( \"DEG_Output_tables\" , showWarnings = FALSE ) dir.create(\"DEG_Output_tables\", showWarnings = FALSE) In [ ]: Copied! #Create the table with the DEGs, Raw counts(just for the Ti samples in this case, change to columns (2:7) for the S10 samples) and Functional annotations DEG_Ti_counts_FA <- inner_join ( DEG_Ti_filtered [, c ( 1 , 2 , 6 )], Read_counts [, c ( 1 , 8 : 13 )], by = \"gene_ID\" ) %>% inner_join ( Functional_annotations , by = \"gene_ID\" ) #Write the table to file write.table ( DEG_Ti_counts_FA , file = \"DEG_Output_tables/Ti_Treatment_Control_DGE.txt\" , quote = FALSE , row.names = FALSE , sep = \"\\t\" ) #Display the first 10 rows of the table head ( DEG_Ti_counts_FA , n = 10 ) #Create the table with the DEGs, Raw counts(just for the Ti samples in this case, change to columns (2:7) for the S10 samples) and Functional annotations DEG_Ti_counts_FA < - inner_join(DEG_Ti_filtered[,c(1, 2, 6)], Read_counts[, c(1, 8:13)], by=\"gene_ID\") %>% inner_join(Functional_annotations, by=\"gene_ID\") #Write the table to file write.table(DEG_Ti_counts_FA, file = \"DEG_Output_tables/Ti_Treatment_Control_DGE.txt\", quote = FALSE, row.names = FALSE, sep = \"\\t\") #Display the first 10 rows of the table head(DEG_Ti_counts_FA, n=10) Example for filtering and counting the Up/Down genes. You can easily filter using the \"filter()\" function just by specifying the dataframe and a logical argument. In [ ]: Copied! DEG_Ti_up <- filter ( DEG_Ti_counts_FA , logFC > 0 ) print ( \"Nr of upregulated genes:\" ) print ( nrow ( DEG_Ti_up )) DEG_Ti_down <- filter ( DEG_Ti_counts_FA , logFC < 0 ) print ( \"Nr of downregulated genes:\" ) print ( nrow ( DEG_Ti_down )) DEG_Ti_up < - filter(DEG_Ti_counts_FA, logFC > 0) print(\"Nr of upregulated genes:\") print(nrow(DEG_Ti_up)) DEG_Ti_down < - filter(DEG_Ti_counts_FA, logFC < 0) print(\"Nr of downregulated genes:\") print(nrow(DEG_Ti_down)) You can use the \"inner_join\" and the \"anti_join\" functions from the dplyr package to select the common and unique genes for each genotype: example_file_joined <- inner_join(file1, file2, by=\"gene_ID\")","title":"Adding functional annotation to DEGs"},{"location":"nb/bulk.html#wrapping-up","text":"In this notebook, you have worked with RNA-seq results for two white clover genotypes exposed to one night of cold treatment, aiming to identify genes that change their expression in response to the cold treatment. You have learned to perform exploratory data analysis of raw and normalized RNA-seq read counts. You have also performed differential gene expression using edgeR .","title":"Wrapping up \ud83c\udf89 \ud83c\udf89 \ud83c\udf89"},{"location":"nb/vcf.html","text":"(function (global, factory) { typeof exports === 'object' && typeof module !== 'undefined' ? module.exports = factory() : typeof define === 'function' && define.amd ? define(factory) : (global = global || self, global.ClipboardCopyElement = factory()); }(this, function () { 'use strict'; function createNode(text) { const node = document.createElement('pre'); node.style.width = '1px'; node.style.height = '1px'; node.style.position = 'fixed'; node.style.top = '5px'; node.textContent = text; return node; } function copyNode(node) { if ('clipboard' in navigator) { // eslint-disable-next-line flowtype/no-flow-fix-me-comments // $FlowFixMe Clipboard is not defined in Flow yet. return navigator.clipboard.writeText(node.textContent); } const selection = getSelection(); if (selection == null) { return Promise.reject(new Error()); } selection.removeAllRanges(); 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We also subtract one pixel so that no scrollbar appears if we have just one single-line cell in the notebook. This padding is to enable a 'scroll past end' feature in a notebook. */ --jp-notebook-scroll-padding: calc( 100% - var(--jp-code-font-size) * var(--jp-code-line-height) - var(--jp-code-padding) - var(--jp-cell-padding) - 1px ); /* Rendermime styles */ --jp-rendermime-error-background: #fdd; --jp-rendermime-table-row-background: var(--md-grey-100); --jp-rendermime-table-row-hover-background: var(--md-light-blue-50); /* Dialog specific styles */ --jp-dialog-background: rgba(0, 0, 0, 0.25); /* Console specific styles */ --jp-console-padding: 10px; /* Toolbar specific styles */ --jp-toolbar-border-color: var(--jp-border-color1); --jp-toolbar-micro-height: 8px; --jp-toolbar-background: var(--jp-layout-color1); --jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, 0.24); --jp-toolbar-header-margin: 4px 4px 0px 4px; --jp-toolbar-active-background: var(--md-grey-300); /* Statusbar specific styles */ --jp-statusbar-height: 24px; /* Input field styles */ --jp-input-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-input-active-background: var(--jp-layout-color1); --jp-input-hover-background: var(--jp-layout-color1); --jp-input-background: var(--md-grey-100); --jp-input-border-color: var(--jp-border-color1); --jp-input-active-border-color: var(--jp-brand-color1); --jp-input-active-box-shadow-color: rgba(19, 124, 189, 0.3); /* General editor styles */ --jp-editor-selected-background: #d9d9d9; --jp-editor-selected-focused-background: #d7d4f0; --jp-editor-cursor-color: var(--jp-ui-font-color0); /* Code mirror specific styles */ --jp-mirror-editor-keyword-color: #008000; --jp-mirror-editor-atom-color: #88f; --jp-mirror-editor-number-color: #080; --jp-mirror-editor-def-color: #00f; --jp-mirror-editor-variable-color: var(--md-grey-900); --jp-mirror-editor-variable-2-color: #05a; --jp-mirror-editor-variable-3-color: #085; --jp-mirror-editor-punctuation-color: #05a; --jp-mirror-editor-property-color: #05a; --jp-mirror-editor-operator-color: #aa22ff; --jp-mirror-editor-comment-color: #408080; --jp-mirror-editor-string-color: #ba2121; --jp-mirror-editor-string-2-color: #708; --jp-mirror-editor-meta-color: #aa22ff; --jp-mirror-editor-qualifier-color: #555; --jp-mirror-editor-builtin-color: #008000; --jp-mirror-editor-bracket-color: #997; --jp-mirror-editor-tag-color: #170; --jp-mirror-editor-attribute-color: #00c; --jp-mirror-editor-header-color: blue; --jp-mirror-editor-quote-color: #090; --jp-mirror-editor-link-color: #00c; --jp-mirror-editor-error-color: #f00; --jp-mirror-editor-hr-color: #999; /* Vega extension styles */ --jp-vega-background: white; /* Sidebar-related styles */ --jp-sidebar-min-width: 250px; /* Search-related styles */ --jp-search-toggle-off-opacity: 0.5; --jp-search-toggle-hover-opacity: 0.8; --jp-search-toggle-on-opacity: 1; --jp-search-selected-match-background-color: rgb(245, 200, 0); --jp-search-selected-match-color: black; --jp-search-unselected-match-background-color: var( --jp-inverse-layout-color0 ); --jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0); /* Icon colors that work well with light or dark backgrounds */ --jp-icon-contrast-color0: var(--md-purple-600); --jp-icon-contrast-color1: var(--md-green-600); --jp-icon-contrast-color2: var(--md-pink-600); --jp-icon-contrast-color3: var(--md-blue-600); } [data-md-color-scheme=\"slate\"] .jupyter-wrapper { /* Elevation * * We style box-shadows using Material Design's idea of elevation. These particular numbers are taken from here: * * https://github.com/material-components/material-components-web * https://material-components-web.appspot.com/elevation.html */ /* The dark theme shadows need a bit of work, but this will probably also require work on the core layout * colors used in the theme as well. */ --jp-shadow-base-lightness: 32; --jp-shadow-umbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.2 ); --jp-shadow-penumbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.14 ); --jp-shadow-ambient-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.12 ); --jp-elevation-z0: none; --jp-elevation-z1: 0px 2px 1px -1px var(--jp-shadow-umbra-color), 0px 1px 1px 0px var(--jp-shadow-penumbra-color), 0px 1px 3px 0px var(--jp-shadow-ambient-color); --jp-elevation-z2: 0px 3px 1px -2px var(--jp-shadow-umbra-color), 0px 2px 2px 0px var(--jp-shadow-penumbra-color), 0px 1px 5px 0px var(--jp-shadow-ambient-color); --jp-elevation-z4: 0px 2px 4px -1px var(--jp-shadow-umbra-color), 0px 4px 5px 0px var(--jp-shadow-penumbra-color), 0px 1px 10px 0px var(--jp-shadow-ambient-color); --jp-elevation-z6: 0px 3px 5px -1px var(--jp-shadow-umbra-color), 0px 6px 10px 0px var(--jp-shadow-penumbra-color), 0px 1px 18px 0px var(--jp-shadow-ambient-color); --jp-elevation-z8: 0px 5px 5px -3px var(--jp-shadow-umbra-color), 0px 8px 10px 1px var(--jp-shadow-penumbra-color), 0px 3px 14px 2px var(--jp-shadow-ambient-color); --jp-elevation-z12: 0px 7px 8px -4px var(--jp-shadow-umbra-color), 0px 12px 17px 2px var(--jp-shadow-penumbra-color), 0px 5px 22px 4px var(--jp-shadow-ambient-color); --jp-elevation-z16: 0px 8px 10px -5px var(--jp-shadow-umbra-color), 0px 16px 24px 2px var(--jp-shadow-penumbra-color), 0px 6px 30px 5px var(--jp-shadow-ambient-color); --jp-elevation-z20: 0px 10px 13px -6px var(--jp-shadow-umbra-color), 0px 20px 31px 3px var(--jp-shadow-penumbra-color), 0px 8px 38px 7px var(--jp-shadow-ambient-color); --jp-elevation-z24: 0px 11px 15px -7px var(--jp-shadow-umbra-color), 0px 24px 38px 3px var(--jp-shadow-penumbra-color), 0px 9px 46px 8px var(--jp-shadow-ambient-color); /* Borders * * The following variables, specify the visual styling of borders in JupyterLab. */ --jp-border-width: 1px; --jp-border-color0: var(--md-grey-700); --jp-border-color1: var(--md-grey-700); --jp-border-color2: var(--md-grey-800); --jp-border-color3: var(--md-grey-900); --jp-border-radius: 2px; /* UI Fonts * * The UI font CSS variables are used for the typography all of the JupyterLab * user interface elements that are not directly user generated content. * * The font sizing here is done assuming that the body font size of --jp-ui-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-ui-font-scale-factor: 1.2; --jp-ui-font-size0: 0.83333em; --jp-ui-font-size1: 13px; /* Base font size */ --jp-ui-font-size2: 1.2em; --jp-ui-font-size3: 1.44em; --jp-ui-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Use these font colors against the corresponding main layout colors. * In a light theme, these go from dark to light. */ /* Defaults use Material Design specification */ --jp-ui-font-color0: rgba(255, 255, 255, 1); --jp-ui-font-color1: rgba(255, 255, 255, 0.87); --jp-ui-font-color2: rgba(255, 255, 255, 0.54); --jp-ui-font-color3: rgba(255, 255, 255, 0.38); /* * Use these against the brand/accent/warn/error colors. * These will typically go from light to darker, in both a dark and light theme. */ --jp-ui-inverse-font-color0: rgba(0, 0, 0, 1); --jp-ui-inverse-font-color1: rgba(0, 0, 0, 0.8); --jp-ui-inverse-font-color2: rgba(0, 0, 0, 0.5); --jp-ui-inverse-font-color3: rgba(0, 0, 0, 0.3); /* Content Fonts * * Content font variables are used for typography of user generated content. * * The font sizing here is done assuming that the body font size of --jp-content-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-content-line-height: 1.6; --jp-content-font-scale-factor: 1.2; --jp-content-font-size0: 0.83333em; --jp-content-font-size1: 14px; /* Base font size */ --jp-content-font-size2: 1.2em; --jp-content-font-size3: 1.44em; --jp-content-font-size4: 1.728em; --jp-content-font-size5: 2.0736em; /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-content-presentation-font-size1: 17px; --jp-content-heading-line-height: 1; --jp-content-heading-margin-top: 1.2em; --jp-content-heading-margin-bottom: 0.8em; --jp-content-heading-font-weight: 500; /* Defaults use Material Design specification */ --jp-content-font-color0: rgba(255, 255, 255, 1); --jp-content-font-color1: rgba(255, 255, 255, 1); --jp-content-font-color2: rgba(255, 255, 255, 0.7); --jp-content-font-color3: rgba(255, 255, 255, 0.5); --jp-content-link-color: var(--md-blue-300); --jp-content-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Code Fonts * * Code font variables are used for typography of code and other monospaces content. */ --jp-code-font-size: 13px; --jp-code-line-height: 1.3077; /* 17px for 13px base */ --jp-code-padding: 5px; /* 5px for 13px base, codemirror highlighting needs integer px value */ --jp-code-font-family-default: Menlo, Consolas, \"DejaVu Sans Mono\", monospace; --jp-code-font-family: var(--jp-code-font-family-default); /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-code-presentation-font-size: 16px; /* may need to tweak cursor width if you change font size */ --jp-code-cursor-width0: 1.4px; --jp-code-cursor-width1: 2px; --jp-code-cursor-width2: 4px; /* Layout * * The following are the main layout colors use in JupyterLab. In a light * theme these would go from light to dark. */ --jp-layout-color0: #111111; --jp-layout-color1: var(--md-grey-900); --jp-layout-color2: var(--md-grey-800); --jp-layout-color3: var(--md-grey-700); --jp-layout-color4: var(--md-grey-600); /* Inverse Layout * * The following are the inverse layout colors use in JupyterLab. In a light * theme these would go from dark to light. */ --jp-inverse-layout-color0: white; --jp-inverse-layout-color1: white; --jp-inverse-layout-color2: var(--md-grey-200); --jp-inverse-layout-color3: var(--md-grey-400); --jp-inverse-layout-color4: var(--md-grey-600); /* Brand/accent */ --jp-brand-color0: var(--md-blue-700); --jp-brand-color1: var(--md-blue-500); --jp-brand-color2: var(--md-blue-300); --jp-brand-color3: var(--md-blue-100); --jp-brand-color4: var(--md-blue-50); --jp-accent-color0: var(--md-green-700); --jp-accent-color1: var(--md-green-500); --jp-accent-color2: var(--md-green-300); --jp-accent-color3: var(--md-green-100); /* State colors (warn, error, success, info) */ --jp-warn-color0: var(--md-orange-700); --jp-warn-color1: var(--md-orange-500); --jp-warn-color2: var(--md-orange-300); --jp-warn-color3: var(--md-orange-100); --jp-error-color0: var(--md-red-700); --jp-error-color1: var(--md-red-500); --jp-error-color2: var(--md-red-300); --jp-error-color3: var(--md-red-100); --jp-success-color0: var(--md-green-700); --jp-success-color1: var(--md-green-500); --jp-success-color2: var(--md-green-300); --jp-success-color3: var(--md-green-100); --jp-info-color0: var(--md-cyan-700); --jp-info-color1: var(--md-cyan-500); --jp-info-color2: var(--md-cyan-300); --jp-info-color3: var(--md-cyan-100); /* Cell specific styles */ --jp-cell-padding: 5px; --jp-cell-collapser-width: 8px; --jp-cell-collapser-min-height: 20px; --jp-cell-collapser-not-active-hover-opacity: 0.6; --jp-cell-editor-background: var(--jp-layout-color1); --jp-cell-editor-border-color: var(--md-grey-700); --jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-cell-editor-active-background: var(--jp-layout-color0); --jp-cell-editor-active-border-color: var(--jp-brand-color1); --jp-cell-prompt-width: 64px; --jp-cell-prompt-font-family: var(--jp-code-font-family-default); --jp-cell-prompt-letter-spacing: 0px; --jp-cell-prompt-opacity: 1; --jp-cell-prompt-not-active-opacity: 1; --jp-cell-prompt-not-active-font-color: var(--md-grey-300); /* A custom blend of MD grey and blue 600 * See https://meyerweb.com/eric/tools/color-blend/#546E7A:1E88E5:5:hex */ --jp-cell-inprompt-font-color: #307fc1; /* A custom blend of MD grey and orange 600 * https://meyerweb.com/eric/tools/color-blend/#546E7A:F4511E:5:hex */ --jp-cell-outprompt-font-color: #bf5b3d; /* Notebook specific styles */ --jp-notebook-padding: 10px; --jp-notebook-select-background: var(--jp-layout-color1); --jp-notebook-multiselected-color: rgba(33, 150, 243, 0.24); /* The scroll padding is calculated to fill enough space at the bottom of the notebook to show one single-line cell (with appropriate padding) at the top when the notebook is scrolled all the way to the bottom. We also subtract one pixel so that no scrollbar appears if we have just one single-line cell in the notebook. This padding is to enable a 'scroll past end' feature in a notebook. */ --jp-notebook-scroll-padding: calc( 100% - var(--jp-code-font-size) * var(--jp-code-line-height) - var(--jp-code-padding) - var(--jp-cell-padding) - 1px ); /* Rendermime styles */ --jp-rendermime-error-background: rgba(244, 67, 54, 0.28); --jp-rendermime-table-row-background: var(--md-grey-900); --jp-rendermime-table-row-hover-background: rgba(3, 169, 244, 0.2); /* Dialog specific styles */ --jp-dialog-background: rgba(0, 0, 0, 0.6); /* Console specific styles */ --jp-console-padding: 10px; /* Toolbar specific styles */ --jp-toolbar-border-color: var(--jp-border-color2); --jp-toolbar-micro-height: 8px; --jp-toolbar-background: var(--jp-layout-color1); --jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, 0.8); --jp-toolbar-header-margin: 4px 4px 0px 4px; --jp-toolbar-active-background: var(--jp-layout-color0); /* Statusbar specific styles */ --jp-statusbar-height: 24px; /* Input field styles */ --jp-input-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-input-active-background: var(--jp-layout-color0); --jp-input-hover-background: var(--jp-layout-color2); --jp-input-background: var(--md-grey-800); --jp-input-border-color: var(--jp-border-color1); --jp-input-active-border-color: var(--jp-brand-color1); --jp-input-active-box-shadow-color: rgba(19, 124, 189, 0.3); /* General editor styles */ --jp-editor-selected-background: var(--jp-layout-color2); --jp-editor-selected-focused-background: rgba(33, 150, 243, 0.24); --jp-editor-cursor-color: var(--jp-ui-font-color0); /* Code mirror specific styles */ --jp-mirror-editor-keyword-color: var(--md-green-500); --jp-mirror-editor-atom-color: var(--md-blue-300); --jp-mirror-editor-number-color: var(--md-green-400); --jp-mirror-editor-def-color: var(--md-blue-600); --jp-mirror-editor-variable-color: var(--md-grey-300); --jp-mirror-editor-variable-2-color: var(--md-blue-400); --jp-mirror-editor-variable-3-color: var(--md-green-600); --jp-mirror-editor-punctuation-color: var(--md-blue-400); --jp-mirror-editor-property-color: var(--md-blue-400); --jp-mirror-editor-operator-color: #aa22ff; --jp-mirror-editor-comment-color: #408080; --jp-mirror-editor-string-color: #ff7070; --jp-mirror-editor-string-2-color: var(--md-purple-300); --jp-mirror-editor-meta-color: #aa22ff; --jp-mirror-editor-qualifier-color: #555; --jp-mirror-editor-builtin-color: var(--md-green-600); --jp-mirror-editor-bracket-color: #997; --jp-mirror-editor-tag-color: var(--md-green-700); --jp-mirror-editor-attribute-color: var(--md-blue-700); --jp-mirror-editor-header-color: var(--md-blue-500); --jp-mirror-editor-quote-color: var(--md-green-300); --jp-mirror-editor-link-color: var(--md-blue-700); --jp-mirror-editor-error-color: #f00; --jp-mirror-editor-hr-color: #999; /* Vega extension styles */ --jp-vega-background: var(--md-grey-400); /* Sidebar-related styles */ --jp-sidebar-min-width: 250px; /* Search-related styles */ --jp-search-toggle-off-opacity: 0.6; --jp-search-toggle-hover-opacity: 0.8; --jp-search-toggle-on-opacity: 1; --jp-search-selected-match-background-color: rgb(255, 225, 0); --jp-search-selected-match-color: black; --jp-search-unselected-match-background-color: var( --jp-inverse-layout-color0 ); --jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0); /* scrollbar related styles. Supports every browser except Edge. */ /* colors based on JetBrain's Darcula theme */ --jp-scrollbar-background-color: #3f4244; --jp-scrollbar-thumb-color: 88, 96, 97; /* need to specify thumb color as an RGB triplet */ --jp-scrollbar-endpad: 3px; /* the minimum gap between the thumb and the ends of a scrollbar */ /* hacks for setting the thumb shape. These do nothing in Firefox */ --jp-scrollbar-thumb-margin: 3.5px; /* the space in between the sides of the thumb and the track */ --jp-scrollbar-thumb-radius: 9px; /* set to a large-ish value for rounded endcaps on the thumb */ /* Icon colors that work well with light or dark backgrounds */ --jp-icon-contrast-color0: var(--md-purple-600); --jp-icon-contrast-color1: var(--md-green-600); --jp-icon-contrast-color2: var(--md-pink-600); --jp-icon-contrast-color3: var(--md-blue-600); } :root{--md-red-50: #ffebee;--md-red-100: #ffcdd2;--md-red-200: #ef9a9a;--md-red-300: #e57373;--md-red-400: #ef5350;--md-red-500: #f44336;--md-red-600: #e53935;--md-red-700: #d32f2f;--md-red-800: #c62828;--md-red-900: #b71c1c;--md-red-A100: #ff8a80;--md-red-A200: #ff5252;--md-red-A400: #ff1744;--md-red-A700: #d50000;--md-pink-50: #fce4ec;--md-pink-100: #f8bbd0;--md-pink-200: #f48fb1;--md-pink-300: #f06292;--md-pink-400: #ec407a;--md-pink-500: #e91e63;--md-pink-600: #d81b60;--md-pink-700: #c2185b;--md-pink-800: #ad1457;--md-pink-900: #880e4f;--md-pink-A100: #ff80ab;--md-pink-A200: #ff4081;--md-pink-A400: #f50057;--md-pink-A700: #c51162;--md-purple-50: #f3e5f5;--md-purple-100: #e1bee7;--md-purple-200: #ce93d8;--md-purple-300: #ba68c8;--md-purple-400: #ab47bc;--md-purple-500: #9c27b0;--md-purple-600: #8e24aa;--md-purple-700: #7b1fa2;--md-purple-800: #6a1b9a;--md-purple-900: #4a148c;--md-purple-A100: #ea80fc;--md-purple-A200: #e040fb;--md-purple-A400: #d500f9;--md-purple-A700: #aa00ff;--md-deep-purple-50: #ede7f6;--md-deep-purple-100: #d1c4e9;--md-deep-purple-200: #b39ddb;--md-deep-purple-300: #9575cd;--md-deep-purple-400: #7e57c2;--md-deep-purple-500: #673ab7;--md-deep-purple-600: #5e35b1;--md-deep-purple-700: #512da8;--md-deep-purple-800: #4527a0;--md-deep-purple-900: #311b92;--md-deep-purple-A100: #b388ff;--md-deep-purple-A200: #7c4dff;--md-deep-purple-A400: #651fff;--md-deep-purple-A700: #6200ea;--md-indigo-50: #e8eaf6;--md-indigo-100: #c5cae9;--md-indigo-200: #9fa8da;--md-indigo-300: #7986cb;--md-indigo-400: #5c6bc0;--md-indigo-500: #3f51b5;--md-indigo-600: #3949ab;--md-indigo-700: #303f9f;--md-indigo-800: #283593;--md-indigo-900: #1a237e;--md-indigo-A100: #8c9eff;--md-indigo-A200: #536dfe;--md-indigo-A400: #3d5afe;--md-indigo-A700: #304ffe;--md-blue-50: #e3f2fd;--md-blue-100: #bbdefb;--md-blue-200: #90caf9;--md-blue-300: #64b5f6;--md-blue-400: #42a5f5;--md-blue-500: #2196f3;--md-blue-600: #1e88e5;--md-blue-700: #1976d2;--md-blue-800: #1565c0;--md-blue-900: #0d47a1;--md-blue-A100: #82b1ff;--md-blue-A200: #448aff;--md-blue-A400: #2979ff;--md-blue-A700: #2962ff;--md-light-blue-50: #e1f5fe;--md-light-blue-100: #b3e5fc;--md-light-blue-200: #81d4fa;--md-light-blue-300: #4fc3f7;--md-light-blue-400: #29b6f6;--md-light-blue-500: #03a9f4;--md-light-blue-600: #039be5;--md-light-blue-700: #0288d1;--md-light-blue-800: #0277bd;--md-light-blue-900: #01579b;--md-light-blue-A100: #80d8ff;--md-light-blue-A200: #40c4ff;--md-light-blue-A400: #00b0ff;--md-light-blue-A700: #0091ea;--md-cyan-50: #e0f7fa;--md-cyan-100: #b2ebf2;--md-cyan-200: #80deea;--md-cyan-300: #4dd0e1;--md-cyan-400: #26c6da;--md-cyan-500: #00bcd4;--md-cyan-600: #00acc1;--md-cyan-700: #0097a7;--md-cyan-800: #00838f;--md-cyan-900: #006064;--md-cyan-A100: #84ffff;--md-cyan-A200: #18ffff;--md-cyan-A400: #00e5ff;--md-cyan-A700: #00b8d4;--md-teal-50: #e0f2f1;--md-teal-100: #b2dfdb;--md-teal-200: #80cbc4;--md-teal-300: #4db6ac;--md-teal-400: #26a69a;--md-teal-500: #009688;--md-teal-600: #00897b;--md-teal-700: #00796b;--md-teal-800: #00695c;--md-teal-900: #004d40;--md-teal-A100: #a7ffeb;--md-teal-A200: #64ffda;--md-teal-A400: #1de9b6;--md-teal-A700: #00bfa5;--md-green-50: #e8f5e9;--md-green-100: #c8e6c9;--md-green-200: #a5d6a7;--md-green-300: #81c784;--md-green-400: #66bb6a;--md-green-500: #4caf50;--md-green-600: #43a047;--md-green-700: #388e3c;--md-green-800: #2e7d32;--md-green-900: #1b5e20;--md-green-A100: #b9f6ca;--md-green-A200: #69f0ae;--md-green-A400: #00e676;--md-green-A700: 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sourceMappingURL=mkdocs-jupyter.css.map*/ init_mathjax = function() { if (window.MathJax) { // MathJax loaded MathJax.Hub.Config({ TeX: { equationNumbers: { autoNumber: \"AMS\", useLabelIds: true } }, tex2jax: { inlineMath: [ ['$','$'], [\"\\\\(\",\"\\\\)\"] ], displayMath: [ ['$$','$$'], [\"\\\\[\",\"\\\\]\"] ], processEscapes: true, processEnvironments: true }, displayAlign: 'center', CommonHTML: { linebreaks: { automatic: true } } }); MathJax.Hub.Queue([\"Typeset\", MathJax.Hub]); } } init_mathjax(); Variant calling and VCF processing This tutorial will cover the steps for performing Variant calling and working on the resulting VCF file format. We will be using the Hifi and RNA-seq mapping data obtained from Galaxy. At the end of this tutorial you will be able to: call variants using alignment files extract data from VCF files using Python filter and count variants The present tutorial, like the rest of the course material, is available at our open-source github repository . To use this notebook, use the NGS (python) kernel that contains the packages. Choose it by selecting Kernel -> Change Kernel in the menu on top of the window. A few introductory points to run this notebook (click to show): To use this notebook, use the NGS (python) kernel that contains the packages. Choose it by selecting Kernel -> Change Kernel in the menu on top of the window. In this notebook you will use both bash and python commands On some computers, you might see the result of the commands once they are done running. This means you will wait some time while the computer is crunching, and only afterwards you will see the result of the command you have executed You can run the code in each cell by clicking on the run cell button, or by pressing Shift + Enter . When the code is done running, a small green check sign will appear on the left side You need to run the cells in sequential order, please do not run a cell until the one above finished running and do not skip any cells Each cell contains a short description of the code and the output you should get. Please try not to focus on understanding the code for each command in too much detail, but rather try to focus on the output You can create new code cells by pressing + in the Menu bar above. Import the necessary Python libraries: In [1]: Copied! import pandas import allel import numpy as np import matplotlib as mpl import matplotlib.pyplot as plt import seaborn as sns import pandas import allel import numpy as np import matplotlib as mpl import matplotlib.pyplot as plt import seaborn as sns Some of the commands used in the course are functions we implement to simplify reading the code of this course. Mostly, those are commands requiring lines of code that would not add anything to your learning curve (management of plots, trivial calculations, few management of the notebook layout). However, you are free to look at the code into the file Scripts/pythonScripts.py and to reuse our code in your own work (citing our course). In [2]: Copied! % run ../ Scripts / pythonScripts . py %run ../Scripts/pythonScripts.py Create a folder for the data necessary to VCF data analysis In [3]: Copied! %% bash mkdir - p Data_for_VCF_analysis %%bash mkdir -p Data_for_VCF_analysis Use the bam files generated from the alignment of raw reads \u00b6 We will use the data created during the alignment session of the course. You had two possibilities: using the interactive Galaxy platform as explained in the course webpage . In this case you will need to upload the some of the created data into the folder Data_for_VCF_analysis , renaming the files as follows: Hifi reads mapped to Contig1: HIFI_contig_1.bam Hifi reads mapped to Contig2: HIFI_contig_2.bam Hifi reads mapped to Contig 1 and 2: HIFI_contig_1_2_asm20.bam (you can of course choose the one with the map-pb alignment option from the alignment notebook) S10 RNAseq reads mapped to Contig 1 and 2 (After merging the bam files): RNA_S10_merged.bam Ti RNAseq reads mapped to Contig 1 and 2 (After merging the bam files): RNA_TI_merged.bam using the notebook 01_align.ipynb . In that case, you just need to run the command below, linking the data in the results folder into the folder Data_for_VCF_analysis . In [4]: Copied! %% bash ln - fs ` realpath results / HIFI_alignment / PacBio_clover_alignment_1 . sort . bam ` Data_for_VCF_analysis / HIFI_contig_1 . bam ln - fs ` realpath results / HIFI_alignment / PacBio_clover_alignment_2 . sort . bam ` Data_for_VCF_analysis / HIFI_contig_2 . bam ln - fs ` realpath results / HIFI_alignment / PacBio_clover_alignment_1_2_asm20 . sort . bam ` Data_for_VCF_analysis / HIFI_contig_1_2 . bam ln - fs ` realpath results / STAR_output / S10_align_contigs_1_2_merge / S10 . sorted . bam ` Data_for_VCF_analysis / RNA_S10_merged . bam ln - fs ` realpath results / STAR_output / TI_align_contigs_1_2_merge / TI . sorted . bam ` Data_for_VCF_analysis / RNA_TI_merged . bam %%bash ln -fs `realpath results/HIFI_alignment/PacBio_clover_alignment_1.sort.bam` Data_for_VCF_analysis/HIFI_contig_1.bam ln -fs `realpath results/HIFI_alignment/PacBio_clover_alignment_2.sort.bam` Data_for_VCF_analysis/HIFI_contig_2.bam ln -fs `realpath results/HIFI_alignment/PacBio_clover_alignment_1_2_asm20.sort.bam` Data_for_VCF_analysis/HIFI_contig_1_2.bam ln -fs `realpath results/STAR_output/S10_align_contigs_1_2_merge/S10.sorted.bam` Data_for_VCF_analysis/RNA_S10_merged.bam ln -fs `realpath results/STAR_output/TI_align_contigs_1_2_merge/TI.sorted.bam` Data_for_VCF_analysis/RNA_TI_merged.bam Variant calling \u00b6 We will use a tool named bcftools to find variants in the alignment files ( .bam ) relative to the reference genome, in this case, the file DNA_Contig1_2.fasta . In this way we can identify positions in the genome where the sequence of the aligned reads differ from the reference genome they were mapped to. There are multiple types of genetic variation , e.g. SNPs (single nucleotide polymorphism), indels (insertions or deletions) and structural variations (such as chromosomal rearrangements). Here we will only be working with SNPs, thus we will use the terms variants and SNPs interchangeably. The called variants will be stored in a specific format file, the Variant Call Format ( VCF ). When looking at an alignment files in IGV you should easily recognize the SNP positions. You can see in the figure below that at the marked positions all the mapped reads have a mismatch compared to the reference. You will be calling two types of SNPs in this notebook. First, we will find subgenome SNPs using the Hifi reads mapped to white clover Contig1 and Contig2. These are positions in the contigs that are different between the two subgenomes of white clover. Secondly, we will find SNPs that differentiate between the sequenced white clover S10 (the reference genome) and Ti genotype, using RNA-seq read mapping. Create a folder that will store all the files generated in this notebook. In [5]: Copied! ! mkdir - p results / VCF_Files !mkdir -p results/VCF_Files Call subgenome SNPs using the Hifi alignments and the reference genome \u00b6 We will use the bcftools software to call SNPs from alignment files. bcftools is a toolkit for variant calling and manipulating VCF files. If you are interested, you can find all the functionalities here http://samtools.github.io/bcftools/bcftools.html#call . We mainly need two commands for this step, first bcftools mpileup which takes as input the alignment and the genome reference files, followed by bcftools call to produce VCF files. The cell below will generate 3 VCF files stored in the folder results/VCF_Files using the Hifi alignment files uploaded from Galaxy. In [6]: Copied! ! bcftools mpileup -- threads 4 - Ou -- skip - indels - f ../ Data / Clover_Data / DNA_Contig1_2 . fasta Data_for_VCF_analysis / HIFI_contig_1 . bam | bcftools call - mv - Ov - o results / VCF_Files / HIFI_Contig_1 . vcf ! bcftools mpileup -- threads 4 - Ou -- skip - indels - f ../ Data / Clover_Data / DNA_Contig1_2 . fasta Data_for_VCF_analysis / HIFI_contig_2 . bam | bcftools call - mv - Ov - o results / VCF_Files / HIFI_Contig_2 . vcf ! bcftools mpileup -- threads 4 - Ou -- skip - indels - f ../ Data / Clover_Data / DNA_Contig1_2 . fasta Data_for_VCF_analysis / HIFI_contig_1_2 . bam | bcftools call - mv - Ov - o results / VCF_Files / HIFI_Contig_1_2 . vcf !bcftools mpileup --threads 4 -Ou --skip-indels -f ../Data/Clover_Data/DNA_Contig1_2.fasta Data_for_VCF_analysis/HIFI_contig_1.bam | bcftools call -mv -Ov -o results/VCF_Files/HIFI_Contig_1.vcf !bcftools mpileup --threads 4 -Ou --skip-indels -f ../Data/Clover_Data/DNA_Contig1_2.fasta Data_for_VCF_analysis/HIFI_contig_2.bam | bcftools call -mv -Ov -o results/VCF_Files/HIFI_Contig_2.vcf !bcftools mpileup --threads 4 -Ou --skip-indels -f ../Data/Clover_Data/DNA_Contig1_2.fasta Data_for_VCF_analysis/HIFI_contig_1_2.bam | bcftools call -mv -Ov -o results/VCF_Files/HIFI_Contig_1_2.vcf Note: none of --samples-file, --ploidy or --ploidy-file given, assuming all sites are diploid [mpileup] 1 samples in 1 input files [mpileup] maximum number of reads per input file set to -d 250 Note: none of --samples-file, --ploidy or --ploidy-file given, assuming all sites are diploid [mpileup] 1 samples in 1 input files [mpileup] maximum number of reads per input file set to -d 250 Note: none of --samples-file, --ploidy or --ploidy-file given, assuming all sites are diploid [mpileup] 1 samples in 1 input files [mpileup] maximum number of reads per input file set to -d 250 TASK Inspect the VCF files using IGV, comparing them to the specific alignment BAM files. (You can download the VCF files to your computer from the results/VCF_Files folder) Are there any problematic positions that may not represent true SNPs? How can you use the HiFi reads aligned to Contigs1+2 identify potential problems? Hint: Try scrolling to the ends of the contigs. Call SNPs using the two RNA-seq genotypes, S10 and Ti \u00b6 We will repeat the same step as above this time using the RNA-seq alignment files for the two white clover genotypes (S10 and Ti). These commands will produce another two VCF files, stored in the same folder. In [7]: Copied! ! bcftools mpileup -- threads 4 - Ou -- skip - indels - f reference_data / DNA_Contig1_2 . fasta Data_for_VCF_analysis / RNA_S10_merged . bam | bcftools call - mv - Ov - o results / VCF_Files / RNA_S10_merged . vcf ! bcftools mpileup -- threads 4 - Ou -- skip - indels - f reference_data / DNA_Contig1_2 . fasta Data_for_VCF_analysis / RNA_TI_merged . bam | bcftools call - mv - Ov - o results / VCF_Files / RNA_TI_merged . vcf !bcftools mpileup --threads 4 -Ou --skip-indels -f reference_data/DNA_Contig1_2.fasta Data_for_VCF_analysis/RNA_S10_merged.bam | bcftools call -mv -Ov -o results/VCF_Files/RNA_S10_merged.vcf !bcftools mpileup --threads 4 -Ou --skip-indels -f reference_data/DNA_Contig1_2.fasta Data_for_VCF_analysis/RNA_TI_merged.bam | bcftools call -mv -Ov -o results/VCF_Files/RNA_TI_merged.vcf [mpileup] 1 samples in 1 input files Note: none of --samples-file, --ploidy or --ploidy-file given, assuming all sites are diploid [mpileup] maximum number of reads per input file set to -d 250 Note: none of --samples-file, --ploidy or --ploidy-file given, assuming all sites are diploid [mpileup] 1 samples in 1 input files [mpileup] maximum number of reads per input file set to -d 250 TASK Inspect one of the VCFs and the corresponding alignment files in IGV. Is it relevant to filter for false positives in this case, and what parameters would you look further into? VCF files processing and filtering \u00b6 Take a look at what one of the previously generated VCF files contains by running the cell below. A VCF file begins with the meta-information lines starting with ## followed by one header line, starting with # . Next, there are the data lines, each describing a genetic variant for a certain position in the genome. Each data line contains multiple tab-delimited columns with information about the variant. There are 9 fixed columns, labelled \u201cCHROM\u201d, \u201cPOS\u201d, \u201cID\u201d, \u201cREF\u201d, \u201cALT\u201d, \u201cQUAL\u201d, \u201cFILTER\u201d, \u201cINFO\u201d and \u201cFORMAT\u201d. Find more about the content of those columns in this wikipedia page . In the sample column you can find the most likely genotype of the sample. For diploid organisms, there will be a 0 value for reference allele and 1 for the alternate allele. Figure: possible genotypes in the data. For example, here we read the first 50 rows from the RNAseq_S10_merged.vcf file. You can see that the file format follows the same pattern as explained above. In [8]: Copied! ! cat results / VCF_Files / RNA_S10_merged . vcf | head - 50 !cat results/VCF_Files/RNA_S10_merged.vcf | head -50 ##fileformat=VCFv4.2 ##FILTER= ##bcftoolsVersion=1.16+htslib-1.17 ##bcftoolsCommand=mpileup --threads 4 -Ou --skip-indels -f reference_data/DNA_Contig1_2.fasta Data_for_VCF_analysis/RNA_S10_merged.bam ##reference=file://reference_data/DNA_Contig1_2.fasta ##contig= ##contig= ##ALT= ##INFO= ##INFO= ##INFO= ##INFO= ##INFO= ##INFO= ##INFO= ##INFO= ##INFO= ##INFO= ##INFO= ##INFO= ##INFO= ##INFO= ##FORMAT= ##FORMAT= ##INFO= ##INFO= ##INFO= ##INFO= ##bcftools_callVersion=1.16+htslib-1.17 ##bcftools_callCommand=call -mv -Ov -o results/VCF_Files/RNA_S10_merged.vcf; Date=Mon Jun 19 10:48:17 2023 #CHROM POS ID REF ALT QUAL FILTER INFO FORMAT Data_for_VCF_analysis/RNA_S10_merged.bam 1 33218 . G A 65.3262 . DP=88;VDB=0.0923504;SGB=-0.692914;RPBZ=3.99376;MQBZ=-1.86933;MQSBZ=-0.901828;BQBZ=-1.8368;SCBZ=2.6559;FS=0;MQ0F=0;AC=1;AN=2;DP4=15,13,16,9;MQ=19 GT:PL 0/1:98,0,138 1 33222 . C G 69.1967 . DP=56;VDB=4.89382e-06;SGB=-0.692352;RPBZ=3.47069;MQBZ=-1.73535;MQSBZ=-0.222384;BQBZ=-1.36705;SCBZ=2.16882;FS=0;MQ0F=0;AC=1;AN=2;DP4=18,13,12,9;MQ=19 GT:PL 0/1:102,0,138 1 33223 . T G 61.1405 . DP=56;VDB=2.99077e-05;SGB=-0.692067;RPBZ=3.40644;MQBZ=-1.80665;MQSBZ=-0.222384;BQBZ=0.830878;SCBZ=2.63256;FS=0;MQ0F=0;AC=1;AN=2;DP4=18,14,12,8;MQ=19 GT:PL 0/1:94,0,132 1 33224 . T G 65.1016 . DP=58;VDB=4.85907e-06;SGB=-0.692352;RPBZ=3.67489;MQBZ=-1.78978;MQSBZ=-0.159627;BQBZ=-1.43699;SCBZ=2.60016;FS=0;MQ0F=0;AC=1;AN=2;DP4=18,15,12,9;MQ=19 GT:PL 0/1:98,0,144 1 33225 . G T 64.0746 . DP=58;VDB=4.36523e-06;SGB=-0.692352;RPBZ=3.67489;MQBZ=-1.78978;MQSBZ=-0.159627;BQBZ=-2.60059;SCBZ=2.60016;FS=0;MQ0F=0;AC=1;AN=2;DP4=18,15,12,9;MQ=19 GT:PL 0/1:97,0,144 1 33227 . T A 64.067 . DP=59;VDB=4.09235e-06;SGB=-0.692352;RPBZ=4.13393;MQBZ=-1.81637;MQSBZ=-0.18317;BQBZ=-1.48941;SCBZ=2.64866;FS=0;MQ0F=0;AC=1;AN=2;DP4=19,15,12,9;MQ=19 GT:PL 0/1:97,0,144 1 49149 . C T 30.4183 . DP=4;VDB=0.0058656;SGB=-0.556411;FS=0;MQ0F=0;AC=2;AN=2;DP4=0,0,0,4;MQ=20 GT:PL 1/1:60,12,0 1 83968 . C G 78.0347 . DP=10;VDB=5.52906e-06;SGB=-0.662043;RPBZ=-3;MQBZ=0;MQSBZ=0;BQBZ=-0.333333;SCBZ=1.77187;FS=0;MQ0F=0;AC=2;AN=2;DP4=1,0,2,7;MQ=20 GT:PL 1/1:105,10,0 1 83970 . T C 78.0347 . DP=10;VDB=8.25997e-06;SGB=-0.662043;RPBZ=-1.79284;MQBZ=0;MQSBZ=0;BQBZ=0;SCBZ=1.77187;FS=0;MQ0F=0;AC=2;AN=2;DP4=1,0,2,7;MQ=20 GT:PL 1/1:105,10,0 1 83972 . G T 78.0347 . DP=10;VDB=8.25997e-06;SGB=-0.662043;RPBZ=-1.79284;MQBZ=0;MQSBZ=0;BQBZ=0;SCBZ=1.57628;FS=0;MQ0F=0;AC=2;AN=2;DP4=1,0,2,7;MQ=20 GT:PL 1/1:105,10,0 1 83975 . G A 78.0347 . DP=10;VDB=1.22485e-05;SGB=-0.662043;RPBZ=-1.79284;MQBZ=0;MQSBZ=0;BQBZ=0;SCBZ=1.57628;FS=0;MQ0F=0;AC=2;AN=2;DP4=1,0,2,7;MQ=20 GT:PL 1/1:105,10,0 1 174956 . C T 10.7923 . DP=2;VDB=0.14;SGB=-0.453602;MQSBZ=0;FS=0;MQ0F=0;AC=2;AN=2;DP4=0,0,1,1;MQ=20 GT:PL 1/1:40,6,0 1 175456 . A G 7.30814 . DP=2;VDB=0.02;SGB=-0.453602;FS=0;MQ0F=0;AC=2;AN=2;DP4=0,0,2,0;MQ=20 GT:PL 1/1:36,6,0 1 211292 . A T 16.7427 . DP=5;VDB=0.0058656;SGB=-0.556411;RPBZ=-2;MQBZ=0;MQSBZ=0;BQBZ=1.22474;SCBZ=2;FS=0;MQ0F=0;AC=1;AN=2;DP4=0,1,4,0;MQ=20 GT:PL 0/1:47,0,5 1 321659 . G A 13.5525 . DP=7;VDB=0.107235;SGB=-0.511536;RPBZ=1.34164;MQBZ=0;MQSBZ=0;BQBZ=1.73205;SCBZ=0.942809;FS=0;MQ0F=0;AC=1;AN=2;DP4=1,0,1,2;MQ=20 GT:PL 0/1:45,0,8 1 321661 . A C 13.5525 . DP=7;VDB=0.107235;SGB=-0.511536;RPBZ=1.34164;MQBZ=0;MQSBZ=0;BQBZ=1.73205;SCBZ=0.942809;FS=0;MQ0F=0;AC=1;AN=2;DP4=1,0,1,2;MQ=20 GT:PL 0/1:45,0,8 1 323166 . G A 7.30814 . DP=4;VDB=0.1;SGB=-0.453602;FS=0;MQ0F=0;AC=2;AN=2;DP4=0,0,0,2;MQ=20 GT:PL 1/1:36,6,0 1 331785 . A T 7.30814 . DP=2;VDB=0.02;SGB=-0.453602;FS=0;MQ0F=0;AC=2;AN=2;DP4=0,0,2,0;MQ=20 GT:PL 1/1:36,6,0 1 331890 . C T 39.4149 . DP=6;VDB=0.100421;SGB=-0.556411;MQSBZ=0;FS=0;MQ0F=0;AC=2;AN=2;DP4=0,0,3,1;MQ=20 GT:PL 1/1:69,12,0 Next, in order to generate plots of the metrics stored in the VCF files, we will first import the VCF files in Python using the scikit-allel package. This package contains a series of function helpful for processing variant calling files. For now, we will import only the VCF files generated using the Hifi data alignments. You can ignore the warning. In [9]: Copied! #Import the VCF files Hifi_Contig_1_vcf = allel . read_vcf ( 'results/VCF_Files/HIFI_Contig_1.vcf' , fields = '*' ) Hifi_Contig_2_vcf = allel . read_vcf ( 'results/VCF_Files/HIFI_Contig_2.vcf' , fields = '*' ) Hifi_Contig_1_2_vcf = allel . read_vcf ( 'results/VCF_Files/HIFI_Contig_1_2.vcf' , fields = '*' ) #Import the VCF files Hifi_Contig_1_vcf = allel.read_vcf('results/VCF_Files/HIFI_Contig_1.vcf', fields='*') Hifi_Contig_2_vcf = allel.read_vcf('results/VCF_Files/HIFI_Contig_2.vcf', fields='*') Hifi_Contig_1_2_vcf = allel.read_vcf('results/VCF_Files/HIFI_Contig_1_2.vcf', fields='*') /work/Course_Env/lib/python3.10/site-packages/allel/io/vcf_read.py:1732: UserWarning: invalid INFO header: '##INFO=\\n' warnings.warn('invalid INFO header: %r' % header) Let's check the number of SNPs in each of the three VCF files. The first two VCF files contain subgenome SNPs. You can see that we identified a have a high number of polymorphisms between the two subgenomes. On the other hand the Hifi_Contig1_2_vcf file contains what we would call false positives, as we called SNPs using the same Hifi reads that were used for assembling the reference genome. As expected the number of SNPs in this file is much smaller. In [10]: Copied! print ( \"Number of SNPs in Hifi Contig 1 vcf file:\" , Hifi_Contig_1_vcf [ 'variants/POS' ] . size ) print ( \"Number of SNPs in Hifi Contig 2 vcf file:\" , Hifi_Contig_2_vcf [ 'variants/POS' ] . size ) print ( \"Number of SNPs in Hifi Contig 1 and 2 vcf file:\" , Hifi_Contig_1_2_vcf [ 'variants/POS' ] . size ) print(\"Number of SNPs in Hifi Contig 1 vcf file:\", Hifi_Contig_1_vcf['variants/POS'].size) print(\"Number of SNPs in Hifi Contig 2 vcf file:\", Hifi_Contig_2_vcf['variants/POS'].size) print(\"Number of SNPs in Hifi Contig 1 and 2 vcf file:\", Hifi_Contig_1_2_vcf['variants/POS'].size) Number of SNPs in Hifi Contig 1 vcf file: 31981 Number of SNPs in Hifi Contig 2 vcf file: 30823 Number of SNPs in Hifi Contig 1 and 2 vcf file: 1309 As an overview of the distribution of SNPs, we can plot the density of SNPs across the contigs. We have defined a function called plot_variant_density in the file ../Scripts/pythonScripts.sh that does exactly that. We use that to plot the SNP density across the contigs in the three VCF files in bins of 5000 bp. We can approximate that a SNP density of 0.1 SNPs/bp repesents one polymorphic site at every ten nucleotides. You can see that for the two contigs there are regions more conserved between the two subgenomes, which show a low density of SNPs and more variable regions, which have a higher SNP density. In the third plot we can mainly observe SNPs at the beging of the contigs, as you have probably also oberved when inspecting the files in IGV. Taking a look at the scale of the y-axis you can oberve that the SNP density for Hifi_Contig1_2_vcf is much smaller compared to the other two files. Note: For easier plotting of the files that contain SNPs from both Contig 1 and 2, the SNP density is plotted as an overlay of both contigs. In [11]: Copied! #Plot SNP density plot_variant_density ( Hifi_Contig_1_vcf , window_size = 5000 , title = 'Contig 1 Raw SNP density' ) plot_variant_density ( Hifi_Contig_2_vcf , window_size = 5000 , title = 'Contig 2 Raw SNP density' ) plot_variant_density ( Hifi_Contig_1_2_vcf , window_size = 5000 , title = 'Contig 1 and 2 Raw SNP density' ) #Plot SNP density plot_variant_density(Hifi_Contig_1_vcf, window_size=5000, title='Contig 1 Raw SNP density') plot_variant_density(Hifi_Contig_2_vcf, window_size=5000, title='Contig 2 Raw SNP density') plot_variant_density(Hifi_Contig_1_2_vcf, window_size=5000, title='Contig 1 and 2 Raw SNP density') We can also plot some of the most relevant parameters from the VCF files, such as the read depth and the quality, to have an overview of the quality of the SNPs in these samples. We defined for that the function plot_variant_hist in the file ../Scripts/pythonScripts.sh . We can plot the metrics \"DP\" (Read depth), \"QUAL\" (Quality score), and \"MQ\" (Mapping quality for Hifi_Contig1_vcf . The x-axis represents the chosen metric, while the y-axis represents the number of SNPs. You can observe that the majority of the SNPs from this file have a high quality. In [12]: Copied! plot_variant_hist ( Hifi_Contig_1_vcf , 'DP' , \"mediumblue\" ) plot_variant_hist ( Hifi_Contig_1_vcf , 'QUAL' , \"forestgreen\" ) plot_variant_hist ( Hifi_Contig_1_vcf , 'MQ' , \"darkred\" ) plot_variant_hist(Hifi_Contig_1_vcf,'DP', \"mediumblue\") plot_variant_hist(Hifi_Contig_1_vcf, 'QUAL', \"forestgreen\") plot_variant_hist(Hifi_Contig_1_vcf, 'MQ', \"darkred\") TASK In the two cells below generate the same three plots as above using the VCF files for Contig 2 and Contig 1+2. You will just need to copy the three command lines from the cell above and replace the name of the VCF files. In [ ]: Copied! #Plot the metrics for Contig 2 #Plot the metrics for Contig 2 In [ ]: Copied! #Plot the metrics for Contig 1 + 2 #Plot the metrics for Contig 1 + 2 As you can observe in the plots above, there are some positions with very low read depth and low quality. Those might represent false-positive variants. Thus, we will filter the VCF files in order to eliminate those positions. For this purpose, we will use another bcftools function bcftools filter . We can pass thresholds for all the info metrics to this function using the \"-i\" flag. Below is just an example of the thresholds, you can change the values to suit your files. Note that this command will generate a new filtered VCF file in the \"VCF_Files\" Folder. In [ ]: Copied! ! bcftools filter - i '(QUAL>150) & (DP > 20) & (MQ > 40)' results / VCF_Files / HIFI_Contig_1 . vcf - O v > results / VCF_Files / HIFI_Contig_1_filtered . vcf !bcftools filter -i '(QUAL>150) & (DP > 20) & (MQ > 40)' results/VCF_Files/HIFI_Contig_1.vcf -O v > results/VCF_Files/HIFI_Contig_1_filtered.vcf In [ ]: Copied! ! bcftools filter - i '(QUAL>150) & (DP > 20) & (MQ > 40)' results / VCF_Files / HIFI_Contig_2 . vcf - O v > results / VCF_Files / HIFI_Contig_2_filtered . vcf !bcftools filter -i '(QUAL>150) & (DP > 20) & (MQ > 40)' results/VCF_Files/HIFI_Contig_2.vcf -O v > results/VCF_Files/HIFI_Contig_2_filtered.vcf In [ ]: Copied! ! bcftools filter - i '(QUAL>150) & (DP > 20) & (MQ > 40)' results / VCF_Files / HIFI_Contig_1_2 . vcf - O v > results / VCF_Files / HIFI_Contig_1_2_filtered . vcf !bcftools filter -i '(QUAL>150) & (DP > 20) & (MQ > 40)' results/VCF_Files/HIFI_Contig_1_2.vcf -O v > results/VCF_Files/HIFI_Contig_1_2_filtered.vcf We read the filtered VCF file for Contig1 in python and generate the same plots as before. We can read the VCF file and generate all the plots in one cell this time. We check how many positions have been eliminated by the filter and compare the plots with the ones for the unfiltered VCFs. If you consider that there are still positions with with low quality you can change the filter and repeat the process. In [ ]: Copied! Hifi_Contig1_filtered = allel . read_vcf ( 'results/VCF_Files/HIFI_Contig_1_filtered.vcf' , fields = '*' ) print ( \"Number of SNPs in Contig 1 VCF filtered file:\" , Hifi_Contig1_filtered [ 'variants/POS' ] . size ) #Plot SNP density plot_variant_density ( Hifi_Contig1_filtered , window_size = 5000 , title = 'Contig 1 Filtered variant density' ) #Plot metrics plot_variant_hist ( Hifi_Contig1_filtered , 'DP' , \"mediumblue\" ) plot_variant_hist ( Hifi_Contig1_filtered , 'QUAL' , \"forestgreen\" ) plot_variant_hist ( Hifi_Contig1_filtered , 'MQ' , \"darkred\" ) Hifi_Contig1_filtered = allel.read_vcf('results/VCF_Files/HIFI_Contig_1_filtered.vcf', fields='*') print(\"Number of SNPs in Contig 1 VCF filtered file:\", Hifi_Contig1_filtered['variants/POS'].size) #Plot SNP density plot_variant_density(Hifi_Contig1_filtered, window_size=5000, title='Contig 1 Filtered variant density') #Plot metrics plot_variant_hist(Hifi_Contig1_filtered,'DP', \"mediumblue\") plot_variant_hist(Hifi_Contig1_filtered, 'QUAL', \"forestgreen\") plot_variant_hist(Hifi_Contig1_filtered, 'MQ', \"darkred\") TASK This exercise covers the rest of the notebook and contains explanations as well . The code will work only if you complete correctly the empty cells. Some cells are filled in to help. Compare the VCF files before and after filtering in IGV. In the two cells below generate the same plots as above using the filtered VCF files for Contig 2 and Contig 1 + 2. You will just need to copy the three command lines from the cell above and replace the name of the VCF files. In [ ]: Copied! #Figures for Contig 2 Filtered VCF #Figures for Contig 2 Filtered VCF In [ ]: Copied! #Figures for Contig 1 + 2 Filtered VCF #Figures for Contig 1 + 2 Filtered VCF Repeat the quality check and filtering as described above for the two RNA-seq VCF files: Read the VCF files in Python: In [ ]: Copied! vcf_S10_RNA = allel . read_vcf ( 'results/VCF_Files/RNA_S10_merged.vcf' , fields = '*' ) vcf_Ti_RNA = allel . read_vcf ( 'results/VCF_Files/RNA_TI_merged.vcf' , fields = '*' ) vcf_S10_RNA = allel.read_vcf('results/VCF_Files/RNA_S10_merged.vcf', fields='*') vcf_Ti_RNA = allel.read_vcf('results/VCF_Files/RNA_TI_merged.vcf', fields='*') Print the number of SNPs in the two RNA-seq VCF files. Note that the number of SNPs is much smaller than for the Hifi VCF files. Also, there is a difference in the number of SNPs for the two genotypes. As S10 RNA-seq data originates from the same genotype as the referece genome, one would expect a small number of SNPs to be identified in this case. In [ ]: Copied! print ( \"Number of SNPs in S10 RNA-seq VCF file:\" , vcf_S10_RNA [ 'variants/POS' ] . size ) print ( \"Number of SNPs in Ti RNA-seq VCF file:\" , vcf_Ti_RNA [ 'variants/POS' ] . size ) print(\"Number of SNPs in S10 RNA-seq VCF file:\", vcf_S10_RNA['variants/POS'].size) print(\"Number of SNPs in Ti RNA-seq VCF file:\", vcf_Ti_RNA['variants/POS'].size) Generate SNP density plots using the plot_variant_density() function: In [ ]: Copied! In [ ]: Copied! Generate info histograms using the plot_variant_hist() function for the same fields. You should observe that the metrics for the RNA-seq vcf files are quite different from the ones using Hifi reads. In [ ]: Copied! In [ ]: Copied! Filter the VCF files using bcftools and your selected tresholds after inspecting the VCF files. Add the filter thresholds before running the command. In [ ]: Copied! ! bcftools filter - i 'add filter thresholds here' results / VCF_Files / RNA_S10_merged . vcf - O v > results / VCF_Files / RNA_S10_merged_filtered . vcf ! bcftools filter - i 'add filter thresholds here' results / VCF_Files / RNA_TI_merged . vcf - O v > results / VCF_Files / RNA_TI_merged_filtered . vcf !bcftools filter -i 'add filter thresholds here' results/VCF_Files/RNA_S10_merged.vcf -O v > results/VCF_Files/RNA_S10_merged_filtered.vcf !bcftools filter -i 'add filter thresholds here' results/VCF_Files/RNA_TI_merged.vcf -O v > results/VCF_Files/RNA_TI_merged_filtered.vcf Read the filtered VCFs in python: In [ ]: Copied! vcf_S10_RNA_filtered = allel . read_vcf ( 'results/VCF_Files/RNA_S10_merged_filtered.vcf' , fields = '*' ) vcf_Ti_RNA_filtered = allel . read_vcf ( 'results/VCF_Files/RNA_TI_merged_filtered.vcf' , fields = '*' ) vcf_S10_RNA_filtered = allel.read_vcf('results/VCF_Files/RNA_S10_merged_filtered.vcf', fields='*') vcf_Ti_RNA_filtered = allel.read_vcf('results/VCF_Files/RNA_TI_merged_filtered.vcf', fields='*') Check the number of SNPs in the filtered VCF files: In [ ]: Copied! print ( \"Number of SNPs in the filtered S10 RNA-seq VCF file:\" , vcf_S10_RNA_filtered [ 'variants/POS' ] . size ) print ( \"Number of SNPs in the filtered Ti RNA-seq VCF file:\" , vcf_Ti_RNA_filtered [ 'variants/POS' ] . size ) print(\"Number of SNPs in the filtered S10 RNA-seq VCF file:\", vcf_S10_RNA_filtered['variants/POS'].size) print(\"Number of SNPs in the filtered Ti RNA-seq VCF file:\", vcf_Ti_RNA_filtered['variants/POS'].size) Plot same histograms for the filtered VCF files. In [ ]: Copied! In [ ]: Copied! Question: Is it relevant to filter for false positives in this case, and what parameters would you look further into? Genotypes counting \u00b6 We want to count the different genotypes in each of the five filtered VCF files generated previously. To make this task easier we can take advantage of the counting functions in the scikit-allel package. We have used those to implement a little function called position_count in the file ../Scripts/pythonScripts.sh . You can then use the function with each of the 5 filtered VCF files to count the number of positions. You need to fill the names of the VCF files imported in Python. In [ ]: Copied! #Contig 1 filtered VCF position_count ( Hifi_Contig1_filtered ) #Contig 1 filtered VCF position_count(Hifi_Contig1_filtered) In [ ]: Copied! #Contig 2 filtered VCF position_count () #Contig 2 filtered VCF position_count() In [ ]: Copied! #Contig 1 and 2 filtered VCF position_count () #Contig 1 and 2 filtered VCF position_count() In [ ]: Copied! #RNAseq S10 filtered VCF position_count () #RNAseq S10 filtered VCF position_count() In [ ]: Copied! #RNAseq Ti filtered VCF position_count () #RNAseq Ti filtered VCF position_count() Comparison of VCF files \u00b6 Compare the VCF files with subgenome variants and the VCF files with S10/Tienshan genotype specific variants obtained from RNA-seq mapping We want now to find overlapping position between the Contig 1 and Contig 2 Hifi VCF files and the Ti RNAseq VCF file. To ease this task we will first create two merged VCF files containing: Hifi Contig 1 VCF + Ti RNAseq VCF Hifi Contig 2 VCF + Ti RNAseq VCF Once again, we will use bcftools for this task, using the merge function. However, the bcftools merge function works only with compressed and indexed VCF files, so first we will do this. In [ ]: Copied! ! bcftools view results / VCF_Files / HIFI_Contig_1_filtered . vcf - Oz - o results / VCF_Files / HIFI_Contig_1_filtered . vcf . gz ! bcftools index results / VCF_Files / HIFI_Contig_1_filtered . vcf . gz - f - o results / VCF_Files / HIFI_Contig_1_filtered . vcf . gz . csi ! bcftools view results / VCF_Files / HIFI_Contig_2_filtered . vcf - Oz - o results / VCF_Files / HIFI_Contig_2_filtered . vcf . gz ! bcftools index results / VCF_Files / HIFI_Contig_2_filtered . vcf . gz - f - o results / VCF_Files / HIFI_Contig_2_filtered . vcf . gz . csi ! bcftools view results / VCF_Files / RNA_TI_merged_filtered . vcf - Oz - o results / VCF_Files / RNA_TI_merged_filtered . vcf . gz ! bcftools index results / VCF_Files / RNA_TI_merged_filtered . vcf . gz - f - o results / VCF_Files / RNA_TI_merged_filtered . vcf . gz . csi !bcftools view results/VCF_Files/HIFI_Contig_1_filtered.vcf -Oz -o results/VCF_Files/HIFI_Contig_1_filtered.vcf.gz !bcftools index results/VCF_Files/HIFI_Contig_1_filtered.vcf.gz -f -o results/VCF_Files/HIFI_Contig_1_filtered.vcf.gz.csi !bcftools view results/VCF_Files/HIFI_Contig_2_filtered.vcf -Oz -o results/VCF_Files/HIFI_Contig_2_filtered.vcf.gz !bcftools index results/VCF_Files/HIFI_Contig_2_filtered.vcf.gz -f -o results/VCF_Files/HIFI_Contig_2_filtered.vcf.gz.csi !bcftools view results/VCF_Files/RNA_TI_merged_filtered.vcf -Oz -o results/VCF_Files/RNA_TI_merged_filtered.vcf.gz !bcftools index results/VCF_Files/RNA_TI_merged_filtered.vcf.gz -f -o results/VCF_Files/RNA_TI_merged_filtered.vcf.gz.csi Now we can merge Contig 1 VCF with the Ti RNA VCF and keep only positions that match between the two files by filtering out any positions with missing genotypes. In [ ]: Copied! ! bcftools merge results / VCF_Files / HIFI_Contig_1_filtered . vcf . gz results / VCF_Files / RNA_TI_merged_filtered . vcf . gz | bcftools filter - e 'GT=\"./.\"' > results / VCF_Files / TI_Contig_1_merged . vcf !bcftools merge results/VCF_Files/HIFI_Contig_1_filtered.vcf.gz results/VCF_Files/RNA_TI_merged_filtered.vcf.gz | bcftools filter -e 'GT=\"./.\"' > results/VCF_Files/TI_Contig_1_merged.vcf Merge Contig 2 VCF with the Ti RNA VCF and keep only positions that match between the two files. In [ ]: Copied! ! bcftools merge results / VCF_Files / HIFI_Contig_2_filtered . vcf . gz results / VCF_Files / RNA_TI_merged_filtered . vcf . gz | bcftools filter - e 'GT=\"./.\"' > results / VCF_Files / TI_Contig_2_merged . vcf !bcftools merge results/VCF_Files/HIFI_Contig_2_filtered.vcf.gz results/VCF_Files/RNA_TI_merged_filtered.vcf.gz | bcftools filter -e 'GT=\"./.\"' > results/VCF_Files/TI_Contig_2_merged.vcf Take a look at the merged VCF file. You can see that compared with the VCF files for just one sample, there is now a new column in the file, with the corresponding genotype for the second sample that we merged. In [ ]: Copied! ! cat results / VCF_Files / TI_Contig_1_merged . vcf | head - 50 !cat results/VCF_Files/TI_Contig_1_merged.vcf | head -50 In order to count the genotypes for the matching positions we can use once again bcftools filter command. Only that this time we will filter based on the genotype columns for the two samples (GT[0] and GT[1]). GT[0] repesents the genotype for the first sample in the merge command and GT[1] is the genotype for the second sample in the merge command. AR - heterozygous AA - heterozygous alternate For example, the filter GT[0]=\"AR\" && GT[1]=\"AR\" will keep only position in which both samples have a heterozygous genotype. A quick way to count how many positions are in the VCF file is by suing the simple bash command grep . This will count all the lines in the VCF files that do not start with a # , thus all the data lines. In [ ]: Copied! ! echo \"AR + AR:\" ! bcftools filter - i 'GT[0]=\"AR\" && GT[1]=\"AR\"' results / VCF_Files / TI_Contig_1_merged . vcf | grep - c - v \"^#\" ! echo \"AR + AA:\" ! bcftools filter - i 'GT[0]=\"AR\" && GT[1]=\"AA\"' results / VCF_Files / TI_Contig_1_merged . vcf | grep - c - v \"^#\" ! echo \"AA + AR:\" ! bcftools filter - i 'GT[0]=\"AA\" && GT[1]=\"AR\"' results / VCF_Files / TI_Contig_1_merged . vcf | grep - c - v \"^#\" ! echo \"AA + AA:\" ! bcftools filter - i 'GT[0]=\"AA\" && GT[1]=\"AA\"' results / VCF_Files / TI_Contig_1_merged . vcf | grep - c - v \"^#\" !echo \"AR + AR:\" !bcftools filter -i 'GT[0]=\"AR\" && GT[1]=\"AR\"' results/VCF_Files/TI_Contig_1_merged.vcf | grep -c -v \"^#\" !echo \"AR + AA:\" !bcftools filter -i 'GT[0]=\"AR\" && GT[1]=\"AA\"' results/VCF_Files/TI_Contig_1_merged.vcf | grep -c -v \"^#\" !echo \"AA + AR:\" !bcftools filter -i 'GT[0]=\"AA\" && GT[1]=\"AR\"' results/VCF_Files/TI_Contig_1_merged.vcf | grep -c -v \"^#\" !echo \"AA + AA:\" !bcftools filter -i 'GT[0]=\"AA\" && GT[1]=\"AA\"' results/VCF_Files/TI_Contig_1_merged.vcf | grep -c -v \"^#\" Use the same commands as above to count the positions marching the filters in the Contig2_Ti_merged.vcf. In [ ]: Copied! Wrapping up \ud83c\udf89 \ud83c\udf89 \ud83c\udf89 \u00b6 In this exercise, you identified polymorphisms between the two white clover subgenomes using Hifi reads alignment files and SNPs specific for the white clover Tienshan genotype using RNA-seq alignment files. You learned how to import VCF files in Python to extract data and created plots. You also filtered low-quality variants from the VCF files and counted genotypes.","title":"3.2.Variant calling analysis"},{"location":"nb/vcf.html#use-the-bam-files-generated-from-the-alignment-of-raw-reads","text":"We will use the data created during the alignment session of the course. You had two possibilities: using the interactive Galaxy platform as explained in the course webpage . In this case you will need to upload the some of the created data into the folder Data_for_VCF_analysis , renaming the files as follows: Hifi reads mapped to Contig1: HIFI_contig_1.bam Hifi reads mapped to Contig2: HIFI_contig_2.bam Hifi reads mapped to Contig 1 and 2: HIFI_contig_1_2_asm20.bam (you can of course choose the one with the map-pb alignment option from the alignment notebook) S10 RNAseq reads mapped to Contig 1 and 2 (After merging the bam files): RNA_S10_merged.bam Ti RNAseq reads mapped to Contig 1 and 2 (After merging the bam files): RNA_TI_merged.bam using the notebook 01_align.ipynb . In that case, you just need to run the command below, linking the data in the results folder into the folder Data_for_VCF_analysis . In [4]: Copied! %% bash ln - fs ` realpath results / HIFI_alignment / PacBio_clover_alignment_1 . sort . bam ` Data_for_VCF_analysis / HIFI_contig_1 . bam ln - fs ` realpath results / HIFI_alignment / PacBio_clover_alignment_2 . sort . bam ` Data_for_VCF_analysis / HIFI_contig_2 . bam ln - fs ` realpath results / HIFI_alignment / PacBio_clover_alignment_1_2_asm20 . sort . bam ` Data_for_VCF_analysis / HIFI_contig_1_2 . bam ln - fs ` realpath results / STAR_output / S10_align_contigs_1_2_merge / S10 . sorted . bam ` Data_for_VCF_analysis / RNA_S10_merged . bam ln - fs ` realpath results / STAR_output / TI_align_contigs_1_2_merge / TI . sorted . bam ` Data_for_VCF_analysis / RNA_TI_merged . bam %%bash ln -fs `realpath results/HIFI_alignment/PacBio_clover_alignment_1.sort.bam` Data_for_VCF_analysis/HIFI_contig_1.bam ln -fs `realpath results/HIFI_alignment/PacBio_clover_alignment_2.sort.bam` Data_for_VCF_analysis/HIFI_contig_2.bam ln -fs `realpath results/HIFI_alignment/PacBio_clover_alignment_1_2_asm20.sort.bam` Data_for_VCF_analysis/HIFI_contig_1_2.bam ln -fs `realpath results/STAR_output/S10_align_contigs_1_2_merge/S10.sorted.bam` Data_for_VCF_analysis/RNA_S10_merged.bam ln -fs `realpath results/STAR_output/TI_align_contigs_1_2_merge/TI.sorted.bam` Data_for_VCF_analysis/RNA_TI_merged.bam","title":"Use the bam files generated from the alignment of raw reads"},{"location":"nb/vcf.html#variant-calling","text":"We will use a tool named bcftools to find variants in the alignment files ( .bam ) relative to the reference genome, in this case, the file DNA_Contig1_2.fasta . In this way we can identify positions in the genome where the sequence of the aligned reads differ from the reference genome they were mapped to. There are multiple types of genetic variation , e.g. SNPs (single nucleotide polymorphism), indels (insertions or deletions) and structural variations (such as chromosomal rearrangements). Here we will only be working with SNPs, thus we will use the terms variants and SNPs interchangeably. The called variants will be stored in a specific format file, the Variant Call Format ( VCF ). When looking at an alignment files in IGV you should easily recognize the SNP positions. You can see in the figure below that at the marked positions all the mapped reads have a mismatch compared to the reference. You will be calling two types of SNPs in this notebook. First, we will find subgenome SNPs using the Hifi reads mapped to white clover Contig1 and Contig2. These are positions in the contigs that are different between the two subgenomes of white clover. Secondly, we will find SNPs that differentiate between the sequenced white clover S10 (the reference genome) and Ti genotype, using RNA-seq read mapping. Create a folder that will store all the files generated in this notebook. In [5]: Copied! ! mkdir - p results / VCF_Files !mkdir -p results/VCF_Files","title":"Variant calling"},{"location":"nb/vcf.html#call-subgenome-snps-using-the-hifi-alignments-and-the-reference-genome","text":"We will use the bcftools software to call SNPs from alignment files. bcftools is a toolkit for variant calling and manipulating VCF files. If you are interested, you can find all the functionalities here http://samtools.github.io/bcftools/bcftools.html#call . We mainly need two commands for this step, first bcftools mpileup which takes as input the alignment and the genome reference files, followed by bcftools call to produce VCF files. The cell below will generate 3 VCF files stored in the folder results/VCF_Files using the Hifi alignment files uploaded from Galaxy. In [6]: Copied! ! bcftools mpileup -- threads 4 - Ou -- skip - indels - f ../ Data / Clover_Data / DNA_Contig1_2 . fasta Data_for_VCF_analysis / HIFI_contig_1 . bam | bcftools call - mv - Ov - o results / VCF_Files / HIFI_Contig_1 . vcf ! bcftools mpileup -- threads 4 - Ou -- skip - indels - f ../ Data / Clover_Data / DNA_Contig1_2 . fasta Data_for_VCF_analysis / HIFI_contig_2 . bam | bcftools call - mv - Ov - o results / VCF_Files / HIFI_Contig_2 . vcf ! bcftools mpileup -- threads 4 - Ou -- skip - indels - f ../ Data / Clover_Data / DNA_Contig1_2 . fasta Data_for_VCF_analysis / HIFI_contig_1_2 . bam | bcftools call - mv - Ov - o results / VCF_Files / HIFI_Contig_1_2 . vcf !bcftools mpileup --threads 4 -Ou --skip-indels -f ../Data/Clover_Data/DNA_Contig1_2.fasta Data_for_VCF_analysis/HIFI_contig_1.bam | bcftools call -mv -Ov -o results/VCF_Files/HIFI_Contig_1.vcf !bcftools mpileup --threads 4 -Ou --skip-indels -f ../Data/Clover_Data/DNA_Contig1_2.fasta Data_for_VCF_analysis/HIFI_contig_2.bam | bcftools call -mv -Ov -o results/VCF_Files/HIFI_Contig_2.vcf !bcftools mpileup --threads 4 -Ou --skip-indels -f ../Data/Clover_Data/DNA_Contig1_2.fasta Data_for_VCF_analysis/HIFI_contig_1_2.bam | bcftools call -mv -Ov -o results/VCF_Files/HIFI_Contig_1_2.vcf Note: none of --samples-file, --ploidy or --ploidy-file given, assuming all sites are diploid [mpileup] 1 samples in 1 input files [mpileup] maximum number of reads per input file set to -d 250 Note: none of --samples-file, --ploidy or --ploidy-file given, assuming all sites are diploid [mpileup] 1 samples in 1 input files [mpileup] maximum number of reads per input file set to -d 250 Note: none of --samples-file, --ploidy or --ploidy-file given, assuming all sites are diploid [mpileup] 1 samples in 1 input files [mpileup] maximum number of reads per input file set to -d 250 TASK Inspect the VCF files using IGV, comparing them to the specific alignment BAM files. (You can download the VCF files to your computer from the results/VCF_Files folder) Are there any problematic positions that may not represent true SNPs? How can you use the HiFi reads aligned to Contigs1+2 identify potential problems? Hint: Try scrolling to the ends of the contigs.","title":"Call subgenome SNPs using the Hifi alignments and the reference genome"},{"location":"nb/vcf.html#call-snps-using-the-two-rna-seq-genotypes-s10-and-ti","text":"We will repeat the same step as above this time using the RNA-seq alignment files for the two white clover genotypes (S10 and Ti). These commands will produce another two VCF files, stored in the same folder. In [7]: Copied! ! bcftools mpileup -- threads 4 - Ou -- skip - indels - f reference_data / DNA_Contig1_2 . fasta Data_for_VCF_analysis / RNA_S10_merged . bam | bcftools call - mv - Ov - o results / VCF_Files / RNA_S10_merged . vcf ! bcftools mpileup -- threads 4 - Ou -- skip - indels - f reference_data / DNA_Contig1_2 . fasta Data_for_VCF_analysis / RNA_TI_merged . bam | bcftools call - mv - Ov - o results / VCF_Files / RNA_TI_merged . vcf !bcftools mpileup --threads 4 -Ou --skip-indels -f reference_data/DNA_Contig1_2.fasta Data_for_VCF_analysis/RNA_S10_merged.bam | bcftools call -mv -Ov -o results/VCF_Files/RNA_S10_merged.vcf !bcftools mpileup --threads 4 -Ou --skip-indels -f reference_data/DNA_Contig1_2.fasta Data_for_VCF_analysis/RNA_TI_merged.bam | bcftools call -mv -Ov -o results/VCF_Files/RNA_TI_merged.vcf [mpileup] 1 samples in 1 input files Note: none of --samples-file, --ploidy or --ploidy-file given, assuming all sites are diploid [mpileup] maximum number of reads per input file set to -d 250 Note: none of --samples-file, --ploidy or --ploidy-file given, assuming all sites are diploid [mpileup] 1 samples in 1 input files [mpileup] maximum number of reads per input file set to -d 250 TASK Inspect one of the VCFs and the corresponding alignment files in IGV. Is it relevant to filter for false positives in this case, and what parameters would you look further into?","title":"Call SNPs using the two RNA-seq genotypes, S10 and Ti"},{"location":"nb/vcf.html#vcf-files-processing-and-filtering","text":"Take a look at what one of the previously generated VCF files contains by running the cell below. A VCF file begins with the meta-information lines starting with ## followed by one header line, starting with # . Next, there are the data lines, each describing a genetic variant for a certain position in the genome. Each data line contains multiple tab-delimited columns with information about the variant. There are 9 fixed columns, labelled \u201cCHROM\u201d, \u201cPOS\u201d, \u201cID\u201d, \u201cREF\u201d, \u201cALT\u201d, \u201cQUAL\u201d, \u201cFILTER\u201d, \u201cINFO\u201d and \u201cFORMAT\u201d. Find more about the content of those columns in this wikipedia page . In the sample column you can find the most likely genotype of the sample. For diploid organisms, there will be a 0 value for reference allele and 1 for the alternate allele. Figure: possible genotypes in the data. For example, here we read the first 50 rows from the RNAseq_S10_merged.vcf file. You can see that the file format follows the same pattern as explained above. In [8]: Copied! ! cat results / VCF_Files / RNA_S10_merged . vcf | head - 50 !cat results/VCF_Files/RNA_S10_merged.vcf | head -50 ##fileformat=VCFv4.2 ##FILTER= ##bcftoolsVersion=1.16+htslib-1.17 ##bcftoolsCommand=mpileup --threads 4 -Ou --skip-indels -f reference_data/DNA_Contig1_2.fasta Data_for_VCF_analysis/RNA_S10_merged.bam ##reference=file://reference_data/DNA_Contig1_2.fasta ##contig= ##contig= ##ALT= ##INFO= ##INFO= ##INFO= ##INFO= ##INFO= ##INFO= ##INFO= ##INFO= ##INFO= ##INFO= ##INFO= ##INFO= ##INFO= ##INFO= ##FORMAT= ##FORMAT= ##INFO= ##INFO= ##INFO= ##INFO= ##bcftools_callVersion=1.16+htslib-1.17 ##bcftools_callCommand=call -mv -Ov -o results/VCF_Files/RNA_S10_merged.vcf; Date=Mon Jun 19 10:48:17 2023 #CHROM POS ID REF ALT QUAL FILTER INFO FORMAT Data_for_VCF_analysis/RNA_S10_merged.bam 1 33218 . G A 65.3262 . DP=88;VDB=0.0923504;SGB=-0.692914;RPBZ=3.99376;MQBZ=-1.86933;MQSBZ=-0.901828;BQBZ=-1.8368;SCBZ=2.6559;FS=0;MQ0F=0;AC=1;AN=2;DP4=15,13,16,9;MQ=19 GT:PL 0/1:98,0,138 1 33222 . C G 69.1967 . DP=56;VDB=4.89382e-06;SGB=-0.692352;RPBZ=3.47069;MQBZ=-1.73535;MQSBZ=-0.222384;BQBZ=-1.36705;SCBZ=2.16882;FS=0;MQ0F=0;AC=1;AN=2;DP4=18,13,12,9;MQ=19 GT:PL 0/1:102,0,138 1 33223 . T G 61.1405 . DP=56;VDB=2.99077e-05;SGB=-0.692067;RPBZ=3.40644;MQBZ=-1.80665;MQSBZ=-0.222384;BQBZ=0.830878;SCBZ=2.63256;FS=0;MQ0F=0;AC=1;AN=2;DP4=18,14,12,8;MQ=19 GT:PL 0/1:94,0,132 1 33224 . T G 65.1016 . DP=58;VDB=4.85907e-06;SGB=-0.692352;RPBZ=3.67489;MQBZ=-1.78978;MQSBZ=-0.159627;BQBZ=-1.43699;SCBZ=2.60016;FS=0;MQ0F=0;AC=1;AN=2;DP4=18,15,12,9;MQ=19 GT:PL 0/1:98,0,144 1 33225 . G T 64.0746 . DP=58;VDB=4.36523e-06;SGB=-0.692352;RPBZ=3.67489;MQBZ=-1.78978;MQSBZ=-0.159627;BQBZ=-2.60059;SCBZ=2.60016;FS=0;MQ0F=0;AC=1;AN=2;DP4=18,15,12,9;MQ=19 GT:PL 0/1:97,0,144 1 33227 . T A 64.067 . DP=59;VDB=4.09235e-06;SGB=-0.692352;RPBZ=4.13393;MQBZ=-1.81637;MQSBZ=-0.18317;BQBZ=-1.48941;SCBZ=2.64866;FS=0;MQ0F=0;AC=1;AN=2;DP4=19,15,12,9;MQ=19 GT:PL 0/1:97,0,144 1 49149 . C T 30.4183 . DP=4;VDB=0.0058656;SGB=-0.556411;FS=0;MQ0F=0;AC=2;AN=2;DP4=0,0,0,4;MQ=20 GT:PL 1/1:60,12,0 1 83968 . C G 78.0347 . DP=10;VDB=5.52906e-06;SGB=-0.662043;RPBZ=-3;MQBZ=0;MQSBZ=0;BQBZ=-0.333333;SCBZ=1.77187;FS=0;MQ0F=0;AC=2;AN=2;DP4=1,0,2,7;MQ=20 GT:PL 1/1:105,10,0 1 83970 . T C 78.0347 . DP=10;VDB=8.25997e-06;SGB=-0.662043;RPBZ=-1.79284;MQBZ=0;MQSBZ=0;BQBZ=0;SCBZ=1.77187;FS=0;MQ0F=0;AC=2;AN=2;DP4=1,0,2,7;MQ=20 GT:PL 1/1:105,10,0 1 83972 . G T 78.0347 . DP=10;VDB=8.25997e-06;SGB=-0.662043;RPBZ=-1.79284;MQBZ=0;MQSBZ=0;BQBZ=0;SCBZ=1.57628;FS=0;MQ0F=0;AC=2;AN=2;DP4=1,0,2,7;MQ=20 GT:PL 1/1:105,10,0 1 83975 . G A 78.0347 . DP=10;VDB=1.22485e-05;SGB=-0.662043;RPBZ=-1.79284;MQBZ=0;MQSBZ=0;BQBZ=0;SCBZ=1.57628;FS=0;MQ0F=0;AC=2;AN=2;DP4=1,0,2,7;MQ=20 GT:PL 1/1:105,10,0 1 174956 . C T 10.7923 . DP=2;VDB=0.14;SGB=-0.453602;MQSBZ=0;FS=0;MQ0F=0;AC=2;AN=2;DP4=0,0,1,1;MQ=20 GT:PL 1/1:40,6,0 1 175456 . A G 7.30814 . DP=2;VDB=0.02;SGB=-0.453602;FS=0;MQ0F=0;AC=2;AN=2;DP4=0,0,2,0;MQ=20 GT:PL 1/1:36,6,0 1 211292 . A T 16.7427 . DP=5;VDB=0.0058656;SGB=-0.556411;RPBZ=-2;MQBZ=0;MQSBZ=0;BQBZ=1.22474;SCBZ=2;FS=0;MQ0F=0;AC=1;AN=2;DP4=0,1,4,0;MQ=20 GT:PL 0/1:47,0,5 1 321659 . G A 13.5525 . DP=7;VDB=0.107235;SGB=-0.511536;RPBZ=1.34164;MQBZ=0;MQSBZ=0;BQBZ=1.73205;SCBZ=0.942809;FS=0;MQ0F=0;AC=1;AN=2;DP4=1,0,1,2;MQ=20 GT:PL 0/1:45,0,8 1 321661 . A C 13.5525 . DP=7;VDB=0.107235;SGB=-0.511536;RPBZ=1.34164;MQBZ=0;MQSBZ=0;BQBZ=1.73205;SCBZ=0.942809;FS=0;MQ0F=0;AC=1;AN=2;DP4=1,0,1,2;MQ=20 GT:PL 0/1:45,0,8 1 323166 . G A 7.30814 . DP=4;VDB=0.1;SGB=-0.453602;FS=0;MQ0F=0;AC=2;AN=2;DP4=0,0,0,2;MQ=20 GT:PL 1/1:36,6,0 1 331785 . A T 7.30814 . DP=2;VDB=0.02;SGB=-0.453602;FS=0;MQ0F=0;AC=2;AN=2;DP4=0,0,2,0;MQ=20 GT:PL 1/1:36,6,0 1 331890 . C T 39.4149 . DP=6;VDB=0.100421;SGB=-0.556411;MQSBZ=0;FS=0;MQ0F=0;AC=2;AN=2;DP4=0,0,3,1;MQ=20 GT:PL 1/1:69,12,0 Next, in order to generate plots of the metrics stored in the VCF files, we will first import the VCF files in Python using the scikit-allel package. This package contains a series of function helpful for processing variant calling files. For now, we will import only the VCF files generated using the Hifi data alignments. You can ignore the warning. In [9]: Copied! #Import the VCF files Hifi_Contig_1_vcf = allel . read_vcf ( 'results/VCF_Files/HIFI_Contig_1.vcf' , fields = '*' ) Hifi_Contig_2_vcf = allel . read_vcf ( 'results/VCF_Files/HIFI_Contig_2.vcf' , fields = '*' ) Hifi_Contig_1_2_vcf = allel . read_vcf ( 'results/VCF_Files/HIFI_Contig_1_2.vcf' , fields = '*' ) #Import the VCF files Hifi_Contig_1_vcf = allel.read_vcf('results/VCF_Files/HIFI_Contig_1.vcf', fields='*') Hifi_Contig_2_vcf = allel.read_vcf('results/VCF_Files/HIFI_Contig_2.vcf', fields='*') Hifi_Contig_1_2_vcf = allel.read_vcf('results/VCF_Files/HIFI_Contig_1_2.vcf', fields='*') /work/Course_Env/lib/python3.10/site-packages/allel/io/vcf_read.py:1732: UserWarning: invalid INFO header: '##INFO=\\n' warnings.warn('invalid INFO header: %r' % header) Let's check the number of SNPs in each of the three VCF files. The first two VCF files contain subgenome SNPs. You can see that we identified a have a high number of polymorphisms between the two subgenomes. On the other hand the Hifi_Contig1_2_vcf file contains what we would call false positives, as we called SNPs using the same Hifi reads that were used for assembling the reference genome. As expected the number of SNPs in this file is much smaller. In [10]: Copied! print ( \"Number of SNPs in Hifi Contig 1 vcf file:\" , Hifi_Contig_1_vcf [ 'variants/POS' ] . size ) print ( \"Number of SNPs in Hifi Contig 2 vcf file:\" , Hifi_Contig_2_vcf [ 'variants/POS' ] . size ) print ( \"Number of SNPs in Hifi Contig 1 and 2 vcf file:\" , Hifi_Contig_1_2_vcf [ 'variants/POS' ] . size ) print(\"Number of SNPs in Hifi Contig 1 vcf file:\", Hifi_Contig_1_vcf['variants/POS'].size) print(\"Number of SNPs in Hifi Contig 2 vcf file:\", Hifi_Contig_2_vcf['variants/POS'].size) print(\"Number of SNPs in Hifi Contig 1 and 2 vcf file:\", Hifi_Contig_1_2_vcf['variants/POS'].size) Number of SNPs in Hifi Contig 1 vcf file: 31981 Number of SNPs in Hifi Contig 2 vcf file: 30823 Number of SNPs in Hifi Contig 1 and 2 vcf file: 1309 As an overview of the distribution of SNPs, we can plot the density of SNPs across the contigs. We have defined a function called plot_variant_density in the file ../Scripts/pythonScripts.sh that does exactly that. We use that to plot the SNP density across the contigs in the three VCF files in bins of 5000 bp. We can approximate that a SNP density of 0.1 SNPs/bp repesents one polymorphic site at every ten nucleotides. You can see that for the two contigs there are regions more conserved between the two subgenomes, which show a low density of SNPs and more variable regions, which have a higher SNP density. In the third plot we can mainly observe SNPs at the beging of the contigs, as you have probably also oberved when inspecting the files in IGV. Taking a look at the scale of the y-axis you can oberve that the SNP density for Hifi_Contig1_2_vcf is much smaller compared to the other two files. Note: For easier plotting of the files that contain SNPs from both Contig 1 and 2, the SNP density is plotted as an overlay of both contigs. In [11]: Copied! #Plot SNP density plot_variant_density ( Hifi_Contig_1_vcf , window_size = 5000 , title = 'Contig 1 Raw SNP density' ) plot_variant_density ( Hifi_Contig_2_vcf , window_size = 5000 , title = 'Contig 2 Raw SNP density' ) plot_variant_density ( Hifi_Contig_1_2_vcf , window_size = 5000 , title = 'Contig 1 and 2 Raw SNP density' ) #Plot SNP density plot_variant_density(Hifi_Contig_1_vcf, window_size=5000, title='Contig 1 Raw SNP density') plot_variant_density(Hifi_Contig_2_vcf, window_size=5000, title='Contig 2 Raw SNP density') plot_variant_density(Hifi_Contig_1_2_vcf, window_size=5000, title='Contig 1 and 2 Raw SNP density') We can also plot some of the most relevant parameters from the VCF files, such as the read depth and the quality, to have an overview of the quality of the SNPs in these samples. We defined for that the function plot_variant_hist in the file ../Scripts/pythonScripts.sh . We can plot the metrics \"DP\" (Read depth), \"QUAL\" (Quality score), and \"MQ\" (Mapping quality for Hifi_Contig1_vcf . The x-axis represents the chosen metric, while the y-axis represents the number of SNPs. You can observe that the majority of the SNPs from this file have a high quality. In [12]: Copied! plot_variant_hist ( Hifi_Contig_1_vcf , 'DP' , \"mediumblue\" ) plot_variant_hist ( Hifi_Contig_1_vcf , 'QUAL' , \"forestgreen\" ) plot_variant_hist ( Hifi_Contig_1_vcf , 'MQ' , \"darkred\" ) plot_variant_hist(Hifi_Contig_1_vcf,'DP', \"mediumblue\") plot_variant_hist(Hifi_Contig_1_vcf, 'QUAL', \"forestgreen\") plot_variant_hist(Hifi_Contig_1_vcf, 'MQ', \"darkred\") TASK In the two cells below generate the same three plots as above using the VCF files for Contig 2 and Contig 1+2. You will just need to copy the three command lines from the cell above and replace the name of the VCF files. In [ ]: Copied! #Plot the metrics for Contig 2 #Plot the metrics for Contig 2 In [ ]: Copied! #Plot the metrics for Contig 1 + 2 #Plot the metrics for Contig 1 + 2 As you can observe in the plots above, there are some positions with very low read depth and low quality. Those might represent false-positive variants. Thus, we will filter the VCF files in order to eliminate those positions. For this purpose, we will use another bcftools function bcftools filter . We can pass thresholds for all the info metrics to this function using the \"-i\" flag. Below is just an example of the thresholds, you can change the values to suit your files. Note that this command will generate a new filtered VCF file in the \"VCF_Files\" Folder. In [ ]: Copied! ! bcftools filter - i '(QUAL>150) & (DP > 20) & (MQ > 40)' results / VCF_Files / HIFI_Contig_1 . vcf - O v > results / VCF_Files / HIFI_Contig_1_filtered . vcf !bcftools filter -i '(QUAL>150) & (DP > 20) & (MQ > 40)' results/VCF_Files/HIFI_Contig_1.vcf -O v > results/VCF_Files/HIFI_Contig_1_filtered.vcf In [ ]: Copied! ! bcftools filter - i '(QUAL>150) & (DP > 20) & (MQ > 40)' results / VCF_Files / HIFI_Contig_2 . vcf - O v > results / VCF_Files / HIFI_Contig_2_filtered . vcf !bcftools filter -i '(QUAL>150) & (DP > 20) & (MQ > 40)' results/VCF_Files/HIFI_Contig_2.vcf -O v > results/VCF_Files/HIFI_Contig_2_filtered.vcf In [ ]: Copied! ! bcftools filter - i '(QUAL>150) & (DP > 20) & (MQ > 40)' results / VCF_Files / HIFI_Contig_1_2 . vcf - O v > results / VCF_Files / HIFI_Contig_1_2_filtered . vcf !bcftools filter -i '(QUAL>150) & (DP > 20) & (MQ > 40)' results/VCF_Files/HIFI_Contig_1_2.vcf -O v > results/VCF_Files/HIFI_Contig_1_2_filtered.vcf We read the filtered VCF file for Contig1 in python and generate the same plots as before. We can read the VCF file and generate all the plots in one cell this time. We check how many positions have been eliminated by the filter and compare the plots with the ones for the unfiltered VCFs. If you consider that there are still positions with with low quality you can change the filter and repeat the process. In [ ]: Copied! Hifi_Contig1_filtered = allel . read_vcf ( 'results/VCF_Files/HIFI_Contig_1_filtered.vcf' , fields = '*' ) print ( \"Number of SNPs in Contig 1 VCF filtered file:\" , Hifi_Contig1_filtered [ 'variants/POS' ] . size ) #Plot SNP density plot_variant_density ( Hifi_Contig1_filtered , window_size = 5000 , title = 'Contig 1 Filtered variant density' ) #Plot metrics plot_variant_hist ( Hifi_Contig1_filtered , 'DP' , \"mediumblue\" ) plot_variant_hist ( Hifi_Contig1_filtered , 'QUAL' , \"forestgreen\" ) plot_variant_hist ( Hifi_Contig1_filtered , 'MQ' , \"darkred\" ) Hifi_Contig1_filtered = allel.read_vcf('results/VCF_Files/HIFI_Contig_1_filtered.vcf', fields='*') print(\"Number of SNPs in Contig 1 VCF filtered file:\", Hifi_Contig1_filtered['variants/POS'].size) #Plot SNP density plot_variant_density(Hifi_Contig1_filtered, window_size=5000, title='Contig 1 Filtered variant density') #Plot metrics plot_variant_hist(Hifi_Contig1_filtered,'DP', \"mediumblue\") plot_variant_hist(Hifi_Contig1_filtered, 'QUAL', \"forestgreen\") plot_variant_hist(Hifi_Contig1_filtered, 'MQ', \"darkred\") TASK This exercise covers the rest of the notebook and contains explanations as well . The code will work only if you complete correctly the empty cells. Some cells are filled in to help. Compare the VCF files before and after filtering in IGV. In the two cells below generate the same plots as above using the filtered VCF files for Contig 2 and Contig 1 + 2. You will just need to copy the three command lines from the cell above and replace the name of the VCF files. In [ ]: Copied! #Figures for Contig 2 Filtered VCF #Figures for Contig 2 Filtered VCF In [ ]: Copied! #Figures for Contig 1 + 2 Filtered VCF #Figures for Contig 1 + 2 Filtered VCF Repeat the quality check and filtering as described above for the two RNA-seq VCF files: Read the VCF files in Python: In [ ]: Copied! vcf_S10_RNA = allel . read_vcf ( 'results/VCF_Files/RNA_S10_merged.vcf' , fields = '*' ) vcf_Ti_RNA = allel . read_vcf ( 'results/VCF_Files/RNA_TI_merged.vcf' , fields = '*' ) vcf_S10_RNA = allel.read_vcf('results/VCF_Files/RNA_S10_merged.vcf', fields='*') vcf_Ti_RNA = allel.read_vcf('results/VCF_Files/RNA_TI_merged.vcf', fields='*') Print the number of SNPs in the two RNA-seq VCF files. Note that the number of SNPs is much smaller than for the Hifi VCF files. Also, there is a difference in the number of SNPs for the two genotypes. As S10 RNA-seq data originates from the same genotype as the referece genome, one would expect a small number of SNPs to be identified in this case. In [ ]: Copied! print ( \"Number of SNPs in S10 RNA-seq VCF file:\" , vcf_S10_RNA [ 'variants/POS' ] . size ) print ( \"Number of SNPs in Ti RNA-seq VCF file:\" , vcf_Ti_RNA [ 'variants/POS' ] . size ) print(\"Number of SNPs in S10 RNA-seq VCF file:\", vcf_S10_RNA['variants/POS'].size) print(\"Number of SNPs in Ti RNA-seq VCF file:\", vcf_Ti_RNA['variants/POS'].size) Generate SNP density plots using the plot_variant_density() function: In [ ]: Copied! In [ ]: Copied! Generate info histograms using the plot_variant_hist() function for the same fields. You should observe that the metrics for the RNA-seq vcf files are quite different from the ones using Hifi reads. In [ ]: Copied! In [ ]: Copied! Filter the VCF files using bcftools and your selected tresholds after inspecting the VCF files. Add the filter thresholds before running the command. In [ ]: Copied! ! bcftools filter - i 'add filter thresholds here' results / VCF_Files / RNA_S10_merged . vcf - O v > results / VCF_Files / RNA_S10_merged_filtered . vcf ! bcftools filter - i 'add filter thresholds here' results / VCF_Files / RNA_TI_merged . vcf - O v > results / VCF_Files / RNA_TI_merged_filtered . vcf !bcftools filter -i 'add filter thresholds here' results/VCF_Files/RNA_S10_merged.vcf -O v > results/VCF_Files/RNA_S10_merged_filtered.vcf !bcftools filter -i 'add filter thresholds here' results/VCF_Files/RNA_TI_merged.vcf -O v > results/VCF_Files/RNA_TI_merged_filtered.vcf Read the filtered VCFs in python: In [ ]: Copied! vcf_S10_RNA_filtered = allel . read_vcf ( 'results/VCF_Files/RNA_S10_merged_filtered.vcf' , fields = '*' ) vcf_Ti_RNA_filtered = allel . read_vcf ( 'results/VCF_Files/RNA_TI_merged_filtered.vcf' , fields = '*' ) vcf_S10_RNA_filtered = allel.read_vcf('results/VCF_Files/RNA_S10_merged_filtered.vcf', fields='*') vcf_Ti_RNA_filtered = allel.read_vcf('results/VCF_Files/RNA_TI_merged_filtered.vcf', fields='*') Check the number of SNPs in the filtered VCF files: In [ ]: Copied! print ( \"Number of SNPs in the filtered S10 RNA-seq VCF file:\" , vcf_S10_RNA_filtered [ 'variants/POS' ] . size ) print ( \"Number of SNPs in the filtered Ti RNA-seq VCF file:\" , vcf_Ti_RNA_filtered [ 'variants/POS' ] . size ) print(\"Number of SNPs in the filtered S10 RNA-seq VCF file:\", vcf_S10_RNA_filtered['variants/POS'].size) print(\"Number of SNPs in the filtered Ti RNA-seq VCF file:\", vcf_Ti_RNA_filtered['variants/POS'].size) Plot same histograms for the filtered VCF files. In [ ]: Copied! In [ ]: Copied! Question: Is it relevant to filter for false positives in this case, and what parameters would you look further into?","title":"VCF files processing and filtering"},{"location":"nb/vcf.html#genotypes-counting","text":"We want to count the different genotypes in each of the five filtered VCF files generated previously. To make this task easier we can take advantage of the counting functions in the scikit-allel package. We have used those to implement a little function called position_count in the file ../Scripts/pythonScripts.sh . You can then use the function with each of the 5 filtered VCF files to count the number of positions. You need to fill the names of the VCF files imported in Python. In [ ]: Copied! #Contig 1 filtered VCF position_count ( Hifi_Contig1_filtered ) #Contig 1 filtered VCF position_count(Hifi_Contig1_filtered) In [ ]: Copied! #Contig 2 filtered VCF position_count () #Contig 2 filtered VCF position_count() In [ ]: Copied! #Contig 1 and 2 filtered VCF position_count () #Contig 1 and 2 filtered VCF position_count() In [ ]: Copied! #RNAseq S10 filtered VCF position_count () #RNAseq S10 filtered VCF position_count() In [ ]: Copied! #RNAseq Ti filtered VCF position_count () #RNAseq Ti filtered VCF position_count()","title":"Genotypes counting"},{"location":"nb/vcf.html#comparison-of-vcf-files","text":"Compare the VCF files with subgenome variants and the VCF files with S10/Tienshan genotype specific variants obtained from RNA-seq mapping We want now to find overlapping position between the Contig 1 and Contig 2 Hifi VCF files and the Ti RNAseq VCF file. To ease this task we will first create two merged VCF files containing: Hifi Contig 1 VCF + Ti RNAseq VCF Hifi Contig 2 VCF + Ti RNAseq VCF Once again, we will use bcftools for this task, using the merge function. However, the bcftools merge function works only with compressed and indexed VCF files, so first we will do this. In [ ]: Copied! ! bcftools view results / VCF_Files / HIFI_Contig_1_filtered . vcf - Oz - o results / VCF_Files / HIFI_Contig_1_filtered . vcf . gz ! bcftools index results / VCF_Files / HIFI_Contig_1_filtered . vcf . gz - f - o results / VCF_Files / HIFI_Contig_1_filtered . vcf . gz . csi ! bcftools view results / VCF_Files / HIFI_Contig_2_filtered . vcf - Oz - o results / VCF_Files / HIFI_Contig_2_filtered . vcf . gz ! bcftools index results / VCF_Files / HIFI_Contig_2_filtered . vcf . gz - f - o results / VCF_Files / HIFI_Contig_2_filtered . vcf . gz . csi ! bcftools view results / VCF_Files / RNA_TI_merged_filtered . vcf - Oz - o results / VCF_Files / RNA_TI_merged_filtered . vcf . gz ! bcftools index results / VCF_Files / RNA_TI_merged_filtered . vcf . gz - f - o results / VCF_Files / RNA_TI_merged_filtered . vcf . gz . csi !bcftools view results/VCF_Files/HIFI_Contig_1_filtered.vcf -Oz -o results/VCF_Files/HIFI_Contig_1_filtered.vcf.gz !bcftools index results/VCF_Files/HIFI_Contig_1_filtered.vcf.gz -f -o results/VCF_Files/HIFI_Contig_1_filtered.vcf.gz.csi !bcftools view results/VCF_Files/HIFI_Contig_2_filtered.vcf -Oz -o results/VCF_Files/HIFI_Contig_2_filtered.vcf.gz !bcftools index results/VCF_Files/HIFI_Contig_2_filtered.vcf.gz -f -o results/VCF_Files/HIFI_Contig_2_filtered.vcf.gz.csi !bcftools view results/VCF_Files/RNA_TI_merged_filtered.vcf -Oz -o results/VCF_Files/RNA_TI_merged_filtered.vcf.gz !bcftools index results/VCF_Files/RNA_TI_merged_filtered.vcf.gz -f -o results/VCF_Files/RNA_TI_merged_filtered.vcf.gz.csi Now we can merge Contig 1 VCF with the Ti RNA VCF and keep only positions that match between the two files by filtering out any positions with missing genotypes. In [ ]: Copied! ! bcftools merge results / VCF_Files / HIFI_Contig_1_filtered . vcf . gz results / VCF_Files / RNA_TI_merged_filtered . vcf . gz | bcftools filter - e 'GT=\"./.\"' > results / VCF_Files / TI_Contig_1_merged . vcf !bcftools merge results/VCF_Files/HIFI_Contig_1_filtered.vcf.gz results/VCF_Files/RNA_TI_merged_filtered.vcf.gz | bcftools filter -e 'GT=\"./.\"' > results/VCF_Files/TI_Contig_1_merged.vcf Merge Contig 2 VCF with the Ti RNA VCF and keep only positions that match between the two files. In [ ]: Copied! ! bcftools merge results / VCF_Files / HIFI_Contig_2_filtered . vcf . gz results / VCF_Files / RNA_TI_merged_filtered . vcf . gz | bcftools filter - e 'GT=\"./.\"' > results / VCF_Files / TI_Contig_2_merged . vcf !bcftools merge results/VCF_Files/HIFI_Contig_2_filtered.vcf.gz results/VCF_Files/RNA_TI_merged_filtered.vcf.gz | bcftools filter -e 'GT=\"./.\"' > results/VCF_Files/TI_Contig_2_merged.vcf Take a look at the merged VCF file. You can see that compared with the VCF files for just one sample, there is now a new column in the file, with the corresponding genotype for the second sample that we merged. In [ ]: Copied! ! cat results / VCF_Files / TI_Contig_1_merged . vcf | head - 50 !cat results/VCF_Files/TI_Contig_1_merged.vcf | head -50 In order to count the genotypes for the matching positions we can use once again bcftools filter command. Only that this time we will filter based on the genotype columns for the two samples (GT[0] and GT[1]). GT[0] repesents the genotype for the first sample in the merge command and GT[1] is the genotype for the second sample in the merge command. AR - heterozygous AA - heterozygous alternate For example, the filter GT[0]=\"AR\" && GT[1]=\"AR\" will keep only position in which both samples have a heterozygous genotype. A quick way to count how many positions are in the VCF file is by suing the simple bash command grep . This will count all the lines in the VCF files that do not start with a # , thus all the data lines. In [ ]: Copied! ! echo \"AR + AR:\" ! bcftools filter - i 'GT[0]=\"AR\" && GT[1]=\"AR\"' results / VCF_Files / TI_Contig_1_merged . vcf | grep - c - v \"^#\" ! echo \"AR + AA:\" ! bcftools filter - i 'GT[0]=\"AR\" && GT[1]=\"AA\"' results / VCF_Files / TI_Contig_1_merged . vcf | grep - c - v \"^#\" ! echo \"AA + AR:\" ! bcftools filter - i 'GT[0]=\"AA\" && GT[1]=\"AR\"' results / VCF_Files / TI_Contig_1_merged . vcf | grep - c - v \"^#\" ! echo \"AA + AA:\" ! bcftools filter - i 'GT[0]=\"AA\" && GT[1]=\"AA\"' results / VCF_Files / TI_Contig_1_merged . vcf | grep - c - v \"^#\" !echo \"AR + AR:\" !bcftools filter -i 'GT[0]=\"AR\" && GT[1]=\"AR\"' results/VCF_Files/TI_Contig_1_merged.vcf | grep -c -v \"^#\" !echo \"AR + AA:\" !bcftools filter -i 'GT[0]=\"AR\" && GT[1]=\"AA\"' results/VCF_Files/TI_Contig_1_merged.vcf | grep -c -v \"^#\" !echo \"AA + AR:\" !bcftools filter -i 'GT[0]=\"AA\" && GT[1]=\"AR\"' results/VCF_Files/TI_Contig_1_merged.vcf | grep -c -v \"^#\" !echo \"AA + AA:\" !bcftools filter -i 'GT[0]=\"AA\" && GT[1]=\"AA\"' results/VCF_Files/TI_Contig_1_merged.vcf | grep -c -v \"^#\" Use the same commands as above to count the positions marching the filters in the Contig2_Ti_merged.vcf. In [ ]: Copied!","title":"Comparison of VCF files"},{"location":"nb/vcf.html#wrapping-up","text":"In this exercise, you identified polymorphisms between the two white clover subgenomes using Hifi reads alignment files and SNPs specific for the white clover Tienshan genotype using RNA-seq alignment files. You learned how to import VCF files in Python to extract data and created plots. You also filtered low-quality variants from the VCF files and counted genotypes.","title":"Wrapping up \ud83c\udf89 \ud83c\udf89 \ud83c\udf89"}]} \ No newline at end of file +{"config":{"indexing":"full","lang":["en"],"min_search_length":3,"prebuild_index":false,"separator":"[\\s\\-]+"},"docs":[{"location":"index.html","text":"Introduction to Next Generation Sequencing data \u00b6 A course of the Summer School of Aarhus University \u00b6 Computing and didactical support from the Danish Health Data Science Sandbox The material for this course is organized in four separated jupyter notebooks in both bash , python and R where you will benefit of an interactive coding setup on jupyterlab . Overview After the course, you will have knowledge of bioinformatics methods for analyzing genomes using NGS data, including knowledge of the existing types of genome data, how the different types of data can be displayed and analyzed, the current methods for genome assembly and analysis, their accuracy and how they can be used. The course will enable you to devise and run a project that makes use of NGS data. \ud83d\udcda Prerequisites: This is an introductory course that needs a basic understanding of the biology behind sequencing, and not necessarily programming experience (though this would help!). \ud83d\udcac Syllabus: 1. Describe key challenges in the analysis of NGS data 2. Explain the theoretical foundation for methods that use NGS for assembly and analysis of genomes 3. Discuss the bioinformatic methods for genome analysis and hypothesize what drives the outcome of the methods 4. Review original literature within the subjects and relate the discussed topics to analysis scenarios 5. Apply bioinformatics tools within the selected application areas and reflect on the results, formulating your own conclusion in the proposed tasks \ud83d\udd70 Total Time Estimation: 20 hours \ud83d\udcc1 Supporting Materials: jupyter notebooks for interactive coding lecture slides from the instructor You can find the links to the material in the table at the bottom of this page. \ud83d\udd8d Course authors and instructors: Mikkel H Schierup Stig U Andersen Samuele Soraggi Peter S Porsborg Adri\u00e1n G Repoll\u00e9s \ud83d\udccb License: Tutorial Content is licensed under Creative Commons Attribution 4.0 International License \ud83d\udcdd Citation: If you use any of this material for your research, please cite this course with the DOI below, and acknowledge the Health Data Science Sandbox project of the Novo Nordisk Foundation (grant number NNF20OC0063268). It is of great help to support the project. \ud83d\udce7 Contact: Samuele Soraggi (samuele at birc.au.dk). Course structure and Instructions The course exercises are organized in four exercise modules. The first one is executed with the web interface usegalaxy.org . Click on 1.Galaxy Exercise in the menu for instructions) Afterwards, we will work on a computing environment to use jupyterlab . Use 2.Instructions for instructions. The menu 3.Course exercises contains all the compiled exercises as a reference. Course material 2022 \u00b6 Here you find a table with the instructor's slides from 2022. Topic Slide Notebook Sequencing technologies link -- Mapping to reference link Notebook Data visualization link -- SNPs and structural variants link Notebook RNA sequencing link Notebook De-novo assembly link -- Microbiomes and metagenomics link -- Single cell RNA sequencing link Notebook Course material 2023 (on its way after the course's end) \u00b6 Here you find a table with the instructor's slides and a link to the compiled notebooks, that you can also run on your own following the instructions in this webpage. Data alignment can also be performed on the Galaxy interactive webpage (see the galaxy exercise in this webpage).","title":"Course Introduction"},{"location":"index.html#introduction-to-next-generation-sequencing-data","text":"","title":"Introduction to Next Generation Sequencing data"},{"location":"index.html#a-course-of-the-summer-school-of-aarhus-university","text":"Computing and didactical support from the Danish Health Data Science Sandbox The material for this course is organized in four separated jupyter notebooks in both bash , python and R where you will benefit of an interactive coding setup on jupyterlab . Overview After the course, you will have knowledge of bioinformatics methods for analyzing genomes using NGS data, including knowledge of the existing types of genome data, how the different types of data can be displayed and analyzed, the current methods for genome assembly and analysis, their accuracy and how they can be used. The course will enable you to devise and run a project that makes use of NGS data. \ud83d\udcda Prerequisites: This is an introductory course that needs a basic understanding of the biology behind sequencing, and not necessarily programming experience (though this would help!). \ud83d\udcac Syllabus: 1. Describe key challenges in the analysis of NGS data 2. Explain the theoretical foundation for methods that use NGS for assembly and analysis of genomes 3. Discuss the bioinformatic methods for genome analysis and hypothesize what drives the outcome of the methods 4. Review original literature within the subjects and relate the discussed topics to analysis scenarios 5. Apply bioinformatics tools within the selected application areas and reflect on the results, formulating your own conclusion in the proposed tasks \ud83d\udd70 Total Time Estimation: 20 hours \ud83d\udcc1 Supporting Materials: jupyter notebooks for interactive coding lecture slides from the instructor You can find the links to the material in the table at the bottom of this page. \ud83d\udd8d Course authors and instructors: Mikkel H Schierup Stig U Andersen Samuele Soraggi Peter S Porsborg Adri\u00e1n G Repoll\u00e9s \ud83d\udccb License: Tutorial Content is licensed under Creative Commons Attribution 4.0 International License \ud83d\udcdd Citation: If you use any of this material for your research, please cite this course with the DOI below, and acknowledge the Health Data Science Sandbox project of the Novo Nordisk Foundation (grant number NNF20OC0063268). It is of great help to support the project. \ud83d\udce7 Contact: Samuele Soraggi (samuele at birc.au.dk). Course structure and Instructions The course exercises are organized in four exercise modules. The first one is executed with the web interface usegalaxy.org . Click on 1.Galaxy Exercise in the menu for instructions) Afterwards, we will work on a computing environment to use jupyterlab . Use 2.Instructions for instructions. The menu 3.Course exercises contains all the compiled exercises as a reference.","title":"A course of the Summer School of Aarhus University"},{"location":"index.html#course-material-2022","text":"Here you find a table with the instructor's slides from 2022. Topic Slide Notebook Sequencing technologies link -- Mapping to reference link Notebook Data visualization link -- SNPs and structural variants link Notebook RNA sequencing link Notebook De-novo assembly link -- Microbiomes and metagenomics link -- Single cell RNA sequencing link Notebook","title":"Course material 2022"},{"location":"index.html#course-material-2023-on-its-way-after-the-courses-end","text":"Here you find a table with the instructor's slides and a link to the compiled notebooks, that you can also run on your own following the instructions in this webpage. Data alignment can also be performed on the Galaxy interactive webpage (see the galaxy exercise in this webpage).","title":"Course material 2023 (on its way after the course's end)"},{"location":"empty.html","text":"General Page \u00b6 Section Overview \ud83d\udd70 Time Estimation: X minutes \ud83d\udcac Learning Objectives: 1. First item 2. Second item Write your introduction to the page here. Useful functions \u00b6 You should continue to write your markdown document as normal. But here are some useful functions. You can find more in the Reference guide listed on the tab above. Be sure to delete the guide and the tips below when finished with them. Code \u00b6 Text will be highlighted appropriately when you include language abbreviation: import tensorflow as tf Admonitions \u00b6 The admonitions used for course/section overview and requirements should be consistent, though you can use any other admonitions freely. Examples (both drop-down and not): Quote Here is a quote What is the smallest country in the world? A: Vatican City Footnotes \u00b6 We can include footnotes like this one 1 . LaTeX \u00b6 You write an equation as normal: \\[ \\operatorname{ker} f=\\{g\\in G:f(g)=e_{H}\\}{\\mbox{.}} \\] Images \u00b6 Format is similar to links, but include an exclamation mark before: You can link to a URL or to somewhere locally. Remember to eat your vegetables. \u21a9","title":"General page"},{"location":"empty.html#general-page","text":"Section Overview \ud83d\udd70 Time Estimation: X minutes \ud83d\udcac Learning Objectives: 1. First item 2. Second item Write your introduction to the page here.","title":"General Page"},{"location":"empty.html#useful-functions","text":"You should continue to write your markdown document as normal. But here are some useful functions. You can find more in the Reference guide listed on the tab above. Be sure to delete the guide and the tips below when finished with them.","title":"Useful functions"},{"location":"empty.html#code","text":"Text will be highlighted appropriately when you include language abbreviation: import tensorflow as tf","title":"Code"},{"location":"empty.html#admonitions","text":"The admonitions used for course/section overview and requirements should be consistent, though you can use any other admonitions freely. Examples (both drop-down and not): Quote Here is a quote What is the smallest country in the world? A: Vatican City","title":"Admonitions"},{"location":"empty.html#footnotes","text":"We can include footnotes like this one 1 .","title":"Footnotes"},{"location":"empty.html#latex","text":"You write an equation as normal: \\[ \\operatorname{ker} f=\\{g\\in G:f(g)=e_{H}\\}{\\mbox{.}} \\]","title":"LaTeX"},{"location":"empty.html#images","text":"Format is similar to links, but include an exclamation mark before: You can link to a URL or to somewhere locally. Remember to eat your vegetables. \u21a9","title":"Images"},{"location":"filtering.html","text":"Data filtering \u00b6 Each person works on its own personal folder, which contains a dataset. As a reminder, these are the personal folders Name Sample folder Andersen, Albert Lund Gifu_ctr_1 Bagger, Andreas Gifu_ctr_2 Hansen, Mads W\u00fcrgler Gifu_ctr_ 3 Milo, Lasse Gifu_ctr_4 Reimick, Sebastian Haunstrup Gifu_R7A_1 Hemmingsen, Jonas Klejs Gifu_R7A_2 Skovm\u00f8ller, Emma Hvitfeldt Gifu_R7A_3 S\u00f8rensen, Emma Frasez Gifu_R7A_4 Agersnap, Simon N\u00f8rregaard HAR1_ctr_1 Schmidt, Alina HAR1_ctr_2 Henriksen, Frederik Oskar HAR1_ctr_3 Lundby, Josephine Marie HAR1_ctr_4 N\u00f8rholm, Anne HAR1_R7A_1 Odgaard, Louise Nyrup HAR1_R7A_2 S\u00f8rensen, Sara Sejer HAR1_R7A_3 L\u00f8nskov, Jonas HAR1_R7A_4 Niklassen, Jacob Hansen Gifu_ctr_1_bis \u00d8llgaard, Ann Mai Br\u00f8ndum Holm Gifu_ctr_2_bis Overgaard, Morten \u00d8gelund Gifu_R7A_1_bis S\u00f8rensen, Elisabeth Asta Gifu_R7A_2_bis Rey, Isabel HAR1_ctr_1_bis Instructions \u00b6 Open a new session of jupyterlab in the same way as in the tutorial session Find your way to your personal folder, which is inside 426401/Students_analysis Start filtering your data using the notebook filtering.ipynb . This is a copy of the first part of the tutorial, so be careful about small changes you have to made (e.g. condition name, thresholds). For the condition name created in the metadata at the beginning of the tutorial, here we use one of Gifu_Control , Gifu_Infected , HAR1_Control , HAR1_Infected (depending on the dataset you have assigned), so that it is easy to integrated all datasets at the end. When you are done and you saved your data at the end (keep the filename filtered.h5Seurat ), write when you are done at this google docs under the sheet Filtering , so we all know when we are ready to proceed to data integration.","title":"Data filtering"},{"location":"filtering.html#data-filtering","text":"Each person works on its own personal folder, which contains a dataset. As a reminder, these are the personal folders Name Sample folder Andersen, Albert Lund Gifu_ctr_1 Bagger, Andreas Gifu_ctr_2 Hansen, Mads W\u00fcrgler Gifu_ctr_ 3 Milo, Lasse Gifu_ctr_4 Reimick, Sebastian Haunstrup Gifu_R7A_1 Hemmingsen, Jonas Klejs Gifu_R7A_2 Skovm\u00f8ller, Emma Hvitfeldt Gifu_R7A_3 S\u00f8rensen, Emma Frasez Gifu_R7A_4 Agersnap, Simon N\u00f8rregaard HAR1_ctr_1 Schmidt, Alina HAR1_ctr_2 Henriksen, Frederik Oskar HAR1_ctr_3 Lundby, Josephine Marie HAR1_ctr_4 N\u00f8rholm, Anne HAR1_R7A_1 Odgaard, Louise Nyrup HAR1_R7A_2 S\u00f8rensen, Sara Sejer HAR1_R7A_3 L\u00f8nskov, Jonas HAR1_R7A_4 Niklassen, Jacob Hansen Gifu_ctr_1_bis \u00d8llgaard, Ann Mai Br\u00f8ndum Holm Gifu_ctr_2_bis Overgaard, Morten \u00d8gelund Gifu_R7A_1_bis S\u00f8rensen, Elisabeth Asta Gifu_R7A_2_bis Rey, Isabel HAR1_ctr_1_bis","title":"Data filtering"},{"location":"filtering.html#instructions","text":"Open a new session of jupyterlab in the same way as in the tutorial session Find your way to your personal folder, which is inside 426401/Students_analysis Start filtering your data using the notebook filtering.ipynb . This is a copy of the first part of the tutorial, so be careful about small changes you have to made (e.g. condition name, thresholds). For the condition name created in the metadata at the beginning of the tutorial, here we use one of Gifu_Control , Gifu_Infected , HAR1_Control , HAR1_Infected (depending on the dataset you have assigned), so that it is easy to integrated all datasets at the end. When you are done and you saved your data at the end (keep the filename filtered.h5Seurat ), write when you are done at this google docs under the sheet Filtering , so we all know when we are ready to proceed to data integration.","title":"Instructions"},{"location":"galaxy.html","text":"Quality Control and Alignment of raw reads \u00b6 This first exercise will be executed on Galaxy , an interactive platform to run bioinformatics workflows. We will replicate this lesson with computer code later in this course. Galaxy has the possibility of working with a free account. Biological introduction \u00b6 White clover ( Trifolium repens ) is an allotetraploid . This means that it contains genomes originating from two different species within the same nucleus. Normally, white clover is an outbreeding species, but a self-compatible line was used for sequencing the white clover genome . This line is designated S10 in your data, indicating that this is the 10 th self-fertilized generation. In addition, you have data from a wild clover accession (ecotype) called Tienshan ( Ti ), which is collected from Chinese mountains and is adapted to alpine conditions. Figure: Characterisation of the white clover population. T.Repens is a hybrid of T.Occidentale and T.Pallescens Exercise guide \u00b6 Prepare the tools and data \u00b6 Install IGV on your computer from here . This is a genome browser you will use to look at some files. Create an account at usegalaxy.org and log into galaxy. Find the course data by going to this web address and by clicking on Import this history (top left corner of the page). Note: If usegalaxy.org has availability problems, you can use the other server https://usegalaxy.eu/ and get the data at this link . You might need to create an account there as well. Data \u00b6 You will be working with two types of sequencing data. The first is PacBio Hifi reads , which are long and accurate. You can find them under Hifi_reads_white_clover.fastq. The second type is Illumina RNA-seq reads , which are short and accurate and should be aligned using a spliced aligner, such as STAR . There are 24 of these files, 12 for each of the two genotypes mentioned before. The files are named [genotype]_[treatment]_[replicate].fastq . Treatment 1 is before and treatment 2 is after exposure to frost, respectively. In addition to the sequencing data, there are also three reference files: one for homologous contig 1 (referencing T. occidentale-derived subgenome), one for contig 2 (T. pallescens-derived subgenome) and one for both Contigs 1 and 2. The reference files are in fasta format. The file white_clover_genes.gtf contains the gene annotations for the two contigs. Workflow illustration \u00b6 Through Galaxy , we build a workflow applying tools to the data. We will look at the quality of the raw reads for both PacBio HiFi and Illumina RNA-seq reads. Afterwards, we align to references, using two different tools for the two types of data. Finally, we will look at the alignments on a genome browser. We will work then on a computing cluster through uCloud to analyze the aligned data in some of the upcoming lessons of the course. Galaxy Workflow \u00b6 When you import the files, what you actually import is a History - a sequence of files and softwares applied on the data. You can see the history on the right side of your usegalaxy.org webpage with green panels. Here, we only have the starting data, and you will build the rest of your history through various tools. On the left side of the screen, you have a menu with various available tools organized by category. All those softwares are also available on a classical computing command line (we will try those as well). Quality control \u00b6 1 Run FastQC on the PacBio Hifi reads and on two of the Illumina RNA-seq libraries. FastQC does quality control of the raw sequence data, providing an overview of the data which can help identify if there are any problems that should be addressed before further analysis. In the tool menu, click on FASTQ quality control --> FASTQC read quality reports . You will see a window with tool parameters : for the first option (raw read data from history), choose multiple files and select Hifi_reads_white_clover.fastq plus other fastq files you want to see the quality of (example in figure below). Then click on the button Run Tool . You will notice that some new elements are added to your history. Part of them are FastQC producing a text file, while others are FastQC producing a webpage report. The reports are ready when coloured in green: click on the eye symbol of a history item to read a report. 2 FastQC provides a report for each sample. To have a better comparison between the Hifi and Illumina data, we would combine the three FastQC reports into one using MultiQC . Choose the MultiQC tool from FASTQ quality control --> MultiQC aggregate results from ... . In the options, select FastQC as the used tool for the logsselect FastQC as the tool used to generate the output, and then select the items of FastQC of your history producing RawData (Figure below). In this way, you build a pipeline from the previous reports to the new tool you are using. Now click on Run Tool . The tool will be now running in your history. When it is done, click on the eye symbol to see the report. select the three \u201cRawData\u201d outputs generated by FastQC. Visualize the Webpage generated by MultiQC. Hint: You can find a \u201cHelp\u201d button that offers additional information about the plots for each panel. Questions: Focus on the following panels: \u201cPer base sequence quality\u201d, \u201cPer sequence quality scores\u201d\u2026. (\u201cPer base sequence content\u201d always gives a FAIL for RNA-seq data). What do you notice with respect to the sequence quality scores? And are there any other quality issues worth noting? Hifi Data Alignment \u00b6 3 Map the PacBio Hifi reads ( Hifi_reads_white_clover.fastq ) to the white clover reference sequence (Contigs 1 and 2) using minimap2 (Map with minimap2). Find Genomics Analysis --> Mapping --> Map with minimap2 . In the options, do not leave Use a built-in genome index , but select the option for having a genome from history. Choose then DNA_Contig1_2.fasta as the reference sequence. Under the profile with preset options, choose PacBio/Oxford Nanopore read to reference mapping (map-pb) . Then click on Run Tool . 4 Run the same alignment, but choose as preset options Long assembly to reference mapping. Divergence is far below 20% (asm20) . Rename then the two alignments using the edit function ( pen symbol in the history). Use for example names Contig1_2_mappb and Contig1_2_asm20 , to distinguish alignment options and reference genome. 5 The aligned genomes are not sorted by coordinates. Sort the alignments using Samtools sort (Find the tool under Genomic file manipulation --> SAM/BAM --> Samtools sort ... ). In the options, choose the two aligned files with multiple selection. Then click on Run Tool . 6 Download the two alignments to your computer. To do so, click on the disk symbol of each file in your history, and for each download both the Dataset (alignments in bam format) and their index files (in bai format). Download as well the reference genome in fasta format ( DNA_Contig1_2.fasta from the history). 7 Open IGV on your computer. Load the reference first: go on Genome --> Load genome from file and select the fasta file you downloaded. Then load the two alignments: go on File --> Load from file and select the bam and bai files you downloaded, together. You can now visualize the alignments. Questions: Look at the alignments in IGV. What do you notice about the alignments? What is the difference between the two alignments? Do you think one of them is better than the other? Choose on of the two alignments for the next steps. 8 Repeat the alignment with Minimap2 (using the chosen alignment option from the question above) and the sorting, but using the reference genomes for Contig 1 and for Contig 2 searaately. Note: you can run all at once by choosing multiple reference genomes in the options! Questions: Download the two references for Contig 1 and 2, and the two sorted alignments. Load the references from the menu `Genomes` in IGV, and then open the two alignments using the menu `File` in IGV. - Why do you see fluctuations in coverage and large regions without any apparent subgenome SNPs? - What are the major differences between the stats for the reads mapped to Contigs1&2 versus contig1 and contig2? What is your interpretation of the differences? RNA Data Alignment \u00b6 9 First, group the 24 RNA-seq libraries into two dataset lists, one list of pairs for S10 libraries and another for Tienshan libraries. so we can work with multiple samples simultaneously. You can do this by selecting the libraries for each genotype and choosing Build Lists of Dataset Pairs . The grouping suggested by Galaxy is wrong, because the paired reads are paired according to _1 and _2 in their names. Change those two with R1 and R2 , click on Unpair all , and then click on the suggested corrected pairs with the buttons Pair these datasets . Your sequences will be substituted by two elements in your history. Here we chose for example to name them S10 and TI . 10 Do alignment of the RNA-seq lists of raw files to the reference DNA_Contig1_2.fasta using STAR . Go to Genomics analysis --> RNA-seq --> RNA STAR Gapped-read mapper for RNA-seq data . In the options use: as data, the parameter Paired-end (as collection) , and then choose one of the two collections (you cannot run them all at once) as reference, DNA_Contig1_2.fasta , with Length of SA pre-indexing string equal to 9 as index with gene-model, use white_clover_annotations.gtf as output, Per gene read counts (GeneCounts) . 11 Use MultiQC to see the quality of the output. The alignment of STAR produces log files which can be used for quality reports. Go on Genomic File Manipulation --> MultiQC . In the options select the tool STAR . Then Insert STAR output , as type of output the Log , and choose the two logs listing collections of STAR alignments. Then click on Run Tool . View the report to see the alignment statistics. Final note: `Galaxy can also be used to create an automatic workflow that will map the data. This workflow can be useful when running multiple samples. You can generate a workflow from the analysis already completed in a history, by going to Settings \u2192 Extract workflow. You can also create a workflow from scratch using the Workflow editor.","title":"1.Galaxy Exercise"},{"location":"galaxy.html#quality-control-and-alignment-of-raw-reads","text":"This first exercise will be executed on Galaxy , an interactive platform to run bioinformatics workflows. We will replicate this lesson with computer code later in this course. Galaxy has the possibility of working with a free account.","title":"Quality Control and Alignment of raw reads"},{"location":"galaxy.html#biological-introduction","text":"White clover ( Trifolium repens ) is an allotetraploid . This means that it contains genomes originating from two different species within the same nucleus. Normally, white clover is an outbreeding species, but a self-compatible line was used for sequencing the white clover genome . This line is designated S10 in your data, indicating that this is the 10 th self-fertilized generation. In addition, you have data from a wild clover accession (ecotype) called Tienshan ( Ti ), which is collected from Chinese mountains and is adapted to alpine conditions. Figure: Characterisation of the white clover population. T.Repens is a hybrid of T.Occidentale and T.Pallescens","title":"Biological introduction"},{"location":"galaxy.html#exercise-guide","text":"","title":"Exercise guide"},{"location":"galaxy.html#prepare-the-tools-and-data","text":"Install IGV on your computer from here . This is a genome browser you will use to look at some files. Create an account at usegalaxy.org and log into galaxy. Find the course data by going to this web address and by clicking on Import this history (top left corner of the page). Note: If usegalaxy.org has availability problems, you can use the other server https://usegalaxy.eu/ and get the data at this link . You might need to create an account there as well.","title":"Prepare the tools and data"},{"location":"galaxy.html#data","text":"You will be working with two types of sequencing data. The first is PacBio Hifi reads , which are long and accurate. You can find them under Hifi_reads_white_clover.fastq. The second type is Illumina RNA-seq reads , which are short and accurate and should be aligned using a spliced aligner, such as STAR . There are 24 of these files, 12 for each of the two genotypes mentioned before. The files are named [genotype]_[treatment]_[replicate].fastq . Treatment 1 is before and treatment 2 is after exposure to frost, respectively. In addition to the sequencing data, there are also three reference files: one for homologous contig 1 (referencing T. occidentale-derived subgenome), one for contig 2 (T. pallescens-derived subgenome) and one for both Contigs 1 and 2. The reference files are in fasta format. The file white_clover_genes.gtf contains the gene annotations for the two contigs.","title":"Data"},{"location":"galaxy.html#workflow-illustration","text":"Through Galaxy , we build a workflow applying tools to the data. We will look at the quality of the raw reads for both PacBio HiFi and Illumina RNA-seq reads. Afterwards, we align to references, using two different tools for the two types of data. Finally, we will look at the alignments on a genome browser. We will work then on a computing cluster through uCloud to analyze the aligned data in some of the upcoming lessons of the course.","title":"Workflow illustration"},{"location":"galaxy.html#galaxy-workflow","text":"When you import the files, what you actually import is a History - a sequence of files and softwares applied on the data. You can see the history on the right side of your usegalaxy.org webpage with green panels. Here, we only have the starting data, and you will build the rest of your history through various tools. On the left side of the screen, you have a menu with various available tools organized by category. All those softwares are also available on a classical computing command line (we will try those as well).","title":"Galaxy Workflow"},{"location":"galaxy.html#quality-control","text":"1 Run FastQC on the PacBio Hifi reads and on two of the Illumina RNA-seq libraries. FastQC does quality control of the raw sequence data, providing an overview of the data which can help identify if there are any problems that should be addressed before further analysis. In the tool menu, click on FASTQ quality control --> FASTQC read quality reports . You will see a window with tool parameters : for the first option (raw read data from history), choose multiple files and select Hifi_reads_white_clover.fastq plus other fastq files you want to see the quality of (example in figure below). Then click on the button Run Tool . You will notice that some new elements are added to your history. Part of them are FastQC producing a text file, while others are FastQC producing a webpage report. The reports are ready when coloured in green: click on the eye symbol of a history item to read a report. 2 FastQC provides a report for each sample. To have a better comparison between the Hifi and Illumina data, we would combine the three FastQC reports into one using MultiQC . Choose the MultiQC tool from FASTQ quality control --> MultiQC aggregate results from ... . In the options, select FastQC as the used tool for the logsselect FastQC as the tool used to generate the output, and then select the items of FastQC of your history producing RawData (Figure below). In this way, you build a pipeline from the previous reports to the new tool you are using. Now click on Run Tool . The tool will be now running in your history. When it is done, click on the eye symbol to see the report. select the three \u201cRawData\u201d outputs generated by FastQC. Visualize the Webpage generated by MultiQC. Hint: You can find a \u201cHelp\u201d button that offers additional information about the plots for each panel. Questions: Focus on the following panels: \u201cPer base sequence quality\u201d, \u201cPer sequence quality scores\u201d\u2026. (\u201cPer base sequence content\u201d always gives a FAIL for RNA-seq data). What do you notice with respect to the sequence quality scores? And are there any other quality issues worth noting?","title":"Quality control"},{"location":"galaxy.html#hifi-data-alignment","text":"3 Map the PacBio Hifi reads ( Hifi_reads_white_clover.fastq ) to the white clover reference sequence (Contigs 1 and 2) using minimap2 (Map with minimap2). Find Genomics Analysis --> Mapping --> Map with minimap2 . In the options, do not leave Use a built-in genome index , but select the option for having a genome from history. Choose then DNA_Contig1_2.fasta as the reference sequence. Under the profile with preset options, choose PacBio/Oxford Nanopore read to reference mapping (map-pb) . Then click on Run Tool . 4 Run the same alignment, but choose as preset options Long assembly to reference mapping. Divergence is far below 20% (asm20) . Rename then the two alignments using the edit function ( pen symbol in the history). Use for example names Contig1_2_mappb and Contig1_2_asm20 , to distinguish alignment options and reference genome. 5 The aligned genomes are not sorted by coordinates. Sort the alignments using Samtools sort (Find the tool under Genomic file manipulation --> SAM/BAM --> Samtools sort ... ). In the options, choose the two aligned files with multiple selection. Then click on Run Tool . 6 Download the two alignments to your computer. To do so, click on the disk symbol of each file in your history, and for each download both the Dataset (alignments in bam format) and their index files (in bai format). Download as well the reference genome in fasta format ( DNA_Contig1_2.fasta from the history). 7 Open IGV on your computer. Load the reference first: go on Genome --> Load genome from file and select the fasta file you downloaded. Then load the two alignments: go on File --> Load from file and select the bam and bai files you downloaded, together. You can now visualize the alignments. Questions: Look at the alignments in IGV. What do you notice about the alignments? What is the difference between the two alignments? Do you think one of them is better than the other? Choose on of the two alignments for the next steps. 8 Repeat the alignment with Minimap2 (using the chosen alignment option from the question above) and the sorting, but using the reference genomes for Contig 1 and for Contig 2 searaately. Note: you can run all at once by choosing multiple reference genomes in the options! Questions: Download the two references for Contig 1 and 2, and the two sorted alignments. Load the references from the menu `Genomes` in IGV, and then open the two alignments using the menu `File` in IGV. - Why do you see fluctuations in coverage and large regions without any apparent subgenome SNPs? - What are the major differences between the stats for the reads mapped to Contigs1&2 versus contig1 and contig2? What is your interpretation of the differences?","title":"Hifi Data Alignment"},{"location":"galaxy.html#rna-data-alignment","text":"9 First, group the 24 RNA-seq libraries into two dataset lists, one list of pairs for S10 libraries and another for Tienshan libraries. so we can work with multiple samples simultaneously. You can do this by selecting the libraries for each genotype and choosing Build Lists of Dataset Pairs . The grouping suggested by Galaxy is wrong, because the paired reads are paired according to _1 and _2 in their names. Change those two with R1 and R2 , click on Unpair all , and then click on the suggested corrected pairs with the buttons Pair these datasets . Your sequences will be substituted by two elements in your history. Here we chose for example to name them S10 and TI . 10 Do alignment of the RNA-seq lists of raw files to the reference DNA_Contig1_2.fasta using STAR . Go to Genomics analysis --> RNA-seq --> RNA STAR Gapped-read mapper for RNA-seq data . In the options use: as data, the parameter Paired-end (as collection) , and then choose one of the two collections (you cannot run them all at once) as reference, DNA_Contig1_2.fasta , with Length of SA pre-indexing string equal to 9 as index with gene-model, use white_clover_annotations.gtf as output, Per gene read counts (GeneCounts) . 11 Use MultiQC to see the quality of the output. The alignment of STAR produces log files which can be used for quality reports. Go on Genomic File Manipulation --> MultiQC . In the options select the tool STAR . Then Insert STAR output , as type of output the Log , and choose the two logs listing collections of STAR alignments. Then click on Run Tool . View the report to see the alignment statistics. Final note: `Galaxy can also be used to create an automatic workflow that will map the data. This workflow can be useful when running multiple samples. You can generate a workflow from the analysis already completed in a history, by going to Settings \u2192 Extract workflow. You can also create a workflow from scratch using the Workflow editor.","title":"RNA Data Alignment"},{"location":"instructions.html","text":"Instructions \u00b6 You have various options to run the course material. You can execute it on a supercomputing system or on your own computer. Access the material on uCloud \u00b6 Ucloud is an interactive online platform from the University of Southern Danmark eScience center that allows users to execute softwares and computer code directly from their browser. If you are enrolled in a danish university or hospital, you can access the platform for free and try the course. Please follow these instructions to access uCloud and get access to the course: To access uCloud for the first time, go to the ucloud webpage and log in by pressing on the green button, choosing your institution, and writing your institutional credentials. If you have a mail address from a danish institution or university, you should be able to login with your institutional credentials. If you are participating in the NGS Summer Course 2023 in Aarhus, click AFTER logging in on this link . This will add you to a project on uCloud, where we have data and extra computing credit for the course. You should see a message on your browser where you have to accept the invitation to the project. Click on the uCloud symbol on the top-left corner of the uCloud webpage. You should see a dashboard window similar to the one below. The dashboard is a home page you will always see when opening uCloud. You can use the menu on the left to navigate to different sections of uCloud (File explorer, applications, etc.) On top of the uCloud interface you have My Workspace . That is your private space where you have some free hundreds of CPU hours and GBs of memory to run different applications (that you can see by using the menu Apps on the left). Click on Apps and find the application called Genomics Courses . Here choose the course Intro to NGS data , and use the other options to choose, for example, how many resources you want to use (we suggest at least 8 vCPUs) Run the docker container \u00b6 With Docker , you can create containers that will work in the same way on all the machines. The results of the course are exactly reproducible if you use such a Docker container. We created a container for the course that already contains packages and course material installed. You need to download Docker Desktop at this link . We suggest you have at least 8GB of RAM and 20GB of hard disk space. When Docker Desktop is installed, you can use the command line to retrieve the container from the web: docker pull samuelesoraggi/ngs-summer-aarhus:2022.08.01 You can now execute the container. You will see a web address in the output of the command line: you need to copy that into your web browser to see jupyterlab . docker run --rm -it -p 8888:8888 samuelesoraggi/ngs-summer-aarhus:2022.08.01","title":"Instructions"},{"location":"instructions.html#instructions","text":"You have various options to run the course material. You can execute it on a supercomputing system or on your own computer.","title":"Instructions"},{"location":"instructions.html#access-the-material-on-ucloud","text":"Ucloud is an interactive online platform from the University of Southern Danmark eScience center that allows users to execute softwares and computer code directly from their browser. If you are enrolled in a danish university or hospital, you can access the platform for free and try the course. Please follow these instructions to access uCloud and get access to the course: To access uCloud for the first time, go to the ucloud webpage and log in by pressing on the green button, choosing your institution, and writing your institutional credentials. If you have a mail address from a danish institution or university, you should be able to login with your institutional credentials. If you are participating in the NGS Summer Course 2023 in Aarhus, click AFTER logging in on this link . This will add you to a project on uCloud, where we have data and extra computing credit for the course. You should see a message on your browser where you have to accept the invitation to the project. Click on the uCloud symbol on the top-left corner of the uCloud webpage. You should see a dashboard window similar to the one below. The dashboard is a home page you will always see when opening uCloud. You can use the menu on the left to navigate to different sections of uCloud (File explorer, applications, etc.) On top of the uCloud interface you have My Workspace . That is your private space where you have some free hundreds of CPU hours and GBs of memory to run different applications (that you can see by using the menu Apps on the left). Click on Apps and find the application called Genomics Courses . Here choose the course Intro to NGS data , and use the other options to choose, for example, how many resources you want to use (we suggest at least 8 vCPUs)","title":"Access the material on uCloud"},{"location":"instructions.html#run-the-docker-container","text":"With Docker , you can create containers that will work in the same way on all the machines. The results of the course are exactly reproducible if you use such a Docker container. We created a container for the course that already contains packages and course material installed. You need to download Docker Desktop at this link . We suggest you have at least 8GB of RAM and 20GB of hard disk space. When Docker Desktop is installed, you can use the command line to retrieve the container from the web: docker pull samuelesoraggi/ngs-summer-aarhus:2022.08.01 You can now execute the container. You will see a web address in the output of the command line: you need to copy that into your web browser to see jupyterlab . docker run --rm -it -p 8888:8888 samuelesoraggi/ngs-summer-aarhus:2022.08.01","title":"Run the docker container"},{"location":"ucloud.html","text":"Access through uCloud \u00b6 Ucloud is an interactive online platform from the University of Southern Danmark eScience center that allows users to execute softwares and computer code directly from their browser. If you have an account from a danish university, hospital, and other institutions, you can access the platform for free and try the course. Starting a session of the Introduction to NGS data analysis on uCloud \u00b6 Log onto ucloud at the address http://cloud.sdu.dk using the university/institution credentials you possess. If you are participating in the NGS Summer Course 2023 in Aarhus, click AFTER logging in on this link . You should see a message on your browser where you have to accept the invitation to the project. This will add you to a project on uCloud, where we have data and extra computing credit for the course. Be sure to be in the right project. If you are participating in the NGS Summer Course 2023 in Aarhus, choose NGS Summer Course from the project menu (red circle). Afterwards, click on the App menu (green circle) Note: The project My Workspace is instead your private space where you have some free hundreds of CPU hours and GBs of memory. Find the app Genomics Sandbox (red circle), which is under the title Featured . Click on it. You will get into the settings window. Choose any Job Name (Nr 1 in the figure below), how many hours you want to use for the job (Nr 2, choose at least 2 hours, you can increase this later), and how many CPUs (Nr 3, choose at least 4 CPUs for the first three exercises, but use at least 8 CPUs to run the single cell analysis). Select the Introduction to NGS Data Analysis as course (Nr 4). Then click on Submit (Nr 5). You will now enter a waiting queue. When the session starts, the timer begins to count down (red circle). In a couple of minutes you should be able to open the interface through the button (green circle). Note the buttons to add time to your session (blue circle) and the button to stop the session when you are done (pink circle) You will be able to see jupyterlab in your browser. On the left side of the screen you can see various folders. You can find the exercises inside the folder Notebooks . You can download any file by right-clicking on it and choosing Download . You can also upload files by clicking on the Upload button on the top left corner of the screen, if you want to experiment with your own data! Reuse your notebooks and data \u00b6 The first time you run the Genomics Sandbox, you download data and notebooks. You can reuse them! Open the Genomics Sandbox App and use the settings to run it. You can click on your previous job name to recycle the settings (red circle). Now click on Add Folder (red circle) and on the appearing browsing bar (green circle). You need to find a drive with your user name (red circle), written as Member Files: NameSurname#Number . Then, go into Jobs/Genomics Sandbox/JobID/Notebooks and choose the folder (green circle). JobID corresponds to the Job Name you used in the previous session, followed by some random number in parenthesis. Do the same procedure adding a folder again and finding Jobs/Genomics Sandbox/JobID/Data . At the end you should have two added folders like this: Click on Submit. Now you will be able to reuse data, results and exercises on which you worked before. Note: remember to recycle the settings of this session the next time, so you can actually reuse the data and notebooks without having to add folders.","title":"2.1.Ucloud Access"},{"location":"ucloud.html#access-through-ucloud","text":"Ucloud is an interactive online platform from the University of Southern Danmark eScience center that allows users to execute softwares and computer code directly from their browser. If you have an account from a danish university, hospital, and other institutions, you can access the platform for free and try the course.","title":"Access through uCloud"},{"location":"ucloud.html#starting-a-session-of-the-introduction-to-ngs-data-analysis-on-ucloud","text":"Log onto ucloud at the address http://cloud.sdu.dk using the university/institution credentials you possess. If you are participating in the NGS Summer Course 2023 in Aarhus, click AFTER logging in on this link . You should see a message on your browser where you have to accept the invitation to the project. This will add you to a project on uCloud, where we have data and extra computing credit for the course. Be sure to be in the right project. If you are participating in the NGS Summer Course 2023 in Aarhus, choose NGS Summer Course from the project menu (red circle). Afterwards, click on the App menu (green circle) Note: The project My Workspace is instead your private space where you have some free hundreds of CPU hours and GBs of memory. Find the app Genomics Sandbox (red circle), which is under the title Featured . Click on it. You will get into the settings window. Choose any Job Name (Nr 1 in the figure below), how many hours you want to use for the job (Nr 2, choose at least 2 hours, you can increase this later), and how many CPUs (Nr 3, choose at least 4 CPUs for the first three exercises, but use at least 8 CPUs to run the single cell analysis). Select the Introduction to NGS Data Analysis as course (Nr 4). Then click on Submit (Nr 5). You will now enter a waiting queue. When the session starts, the timer begins to count down (red circle). In a couple of minutes you should be able to open the interface through the button (green circle). Note the buttons to add time to your session (blue circle) and the button to stop the session when you are done (pink circle) You will be able to see jupyterlab in your browser. On the left side of the screen you can see various folders. You can find the exercises inside the folder Notebooks . You can download any file by right-clicking on it and choosing Download . You can also upload files by clicking on the Upload button on the top left corner of the screen, if you want to experiment with your own data!","title":"Starting a session of the Introduction to NGS data analysis on uCloud"},{"location":"ucloud.html#reuse-your-notebooks-and-data","text":"The first time you run the Genomics Sandbox, you download data and notebooks. You can reuse them! Open the Genomics Sandbox App and use the settings to run it. You can click on your previous job name to recycle the settings (red circle). Now click on Add Folder (red circle) and on the appearing browsing bar (green circle). You need to find a drive with your user name (red circle), written as Member Files: NameSurname#Number . Then, go into Jobs/Genomics Sandbox/JobID/Notebooks and choose the folder (green circle). JobID corresponds to the Job Name you used in the previous session, followed by some random number in parenthesis. Do the same procedure adding a folder again and finding Jobs/Genomics Sandbox/JobID/Data . At the end you should have two added folders like this: Click on Submit. Now you will be able to reuse data, results and exercises on which you worked before. Note: remember to recycle the settings of this session the next time, so you can actually reuse the data and notebooks without having to add folders.","title":"Reuse your notebooks and data"},{"location":"nb/align.html","text":"(function (global, factory) { typeof exports === 'object' && typeof module !== 'undefined' ? module.exports = factory() : typeof define === 'function' && define.amd ? define(factory) : (global = global || self, global.ClipboardCopyElement = factory()); }(this, function () { 'use strict'; function createNode(text) { const node = document.createElement('pre'); node.style.width = '1px'; node.style.height = '1px'; node.style.position = 'fixed'; node.style.top = '5px'; node.textContent = text; return node; } function copyNode(node) { if ('clipboard' in navigator) { // eslint-disable-next-line flowtype/no-flow-fix-me-comments // $FlowFixMe Clipboard is not defined in Flow yet. return navigator.clipboard.writeText(node.textContent); } const selection = getSelection(); 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These particular numbers are taken from here: * * https://github.com/material-components/material-components-web * https://material-components-web.appspot.com/elevation.html */ --jp-shadow-base-lightness: 0; --jp-shadow-umbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.2 ); --jp-shadow-penumbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.14 ); --jp-shadow-ambient-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.12 ); --jp-elevation-z0: none; --jp-elevation-z1: 0px 2px 1px -1px var(--jp-shadow-umbra-color), 0px 1px 1px 0px var(--jp-shadow-penumbra-color), 0px 1px 3px 0px var(--jp-shadow-ambient-color); --jp-elevation-z2: 0px 3px 1px -2px var(--jp-shadow-umbra-color), 0px 2px 2px 0px var(--jp-shadow-penumbra-color), 0px 1px 5px 0px var(--jp-shadow-ambient-color); --jp-elevation-z4: 0px 2px 4px -1px var(--jp-shadow-umbra-color), 0px 4px 5px 0px var(--jp-shadow-penumbra-color), 0px 1px 10px 0px var(--jp-shadow-ambient-color); --jp-elevation-z6: 0px 3px 5px -1px var(--jp-shadow-umbra-color), 0px 6px 10px 0px var(--jp-shadow-penumbra-color), 0px 1px 18px 0px var(--jp-shadow-ambient-color); --jp-elevation-z8: 0px 5px 5px -3px var(--jp-shadow-umbra-color), 0px 8px 10px 1px var(--jp-shadow-penumbra-color), 0px 3px 14px 2px var(--jp-shadow-ambient-color); --jp-elevation-z12: 0px 7px 8px -4px var(--jp-shadow-umbra-color), 0px 12px 17px 2px var(--jp-shadow-penumbra-color), 0px 5px 22px 4px var(--jp-shadow-ambient-color); --jp-elevation-z16: 0px 8px 10px -5px var(--jp-shadow-umbra-color), 0px 16px 24px 2px var(--jp-shadow-penumbra-color), 0px 6px 30px 5px var(--jp-shadow-ambient-color); --jp-elevation-z20: 0px 10px 13px -6px var(--jp-shadow-umbra-color), 0px 20px 31px 3px var(--jp-shadow-penumbra-color), 0px 8px 38px 7px var(--jp-shadow-ambient-color); --jp-elevation-z24: 0px 11px 15px -7px var(--jp-shadow-umbra-color), 0px 24px 38px 3px var(--jp-shadow-penumbra-color), 0px 9px 46px 8px var(--jp-shadow-ambient-color); /* Borders * * The following variables, specify the visual styling of borders in JupyterLab. */ --jp-border-width: 1px; --jp-border-color0: var(--md-grey-400); --jp-border-color1: var(--md-grey-400); --jp-border-color2: var(--md-grey-300); --jp-border-color3: var(--md-grey-200); --jp-border-radius: 2px; /* UI Fonts * * The UI font CSS variables are used for the typography all of the JupyterLab * user interface elements that are not directly user generated content. * * The font sizing here is done assuming that the body font size of --jp-ui-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-ui-font-scale-factor: 1.2; --jp-ui-font-size0: 0.83333em; --jp-ui-font-size1: 13px; /* Base font size */ --jp-ui-font-size2: 1.2em; --jp-ui-font-size3: 1.44em; --jp-ui-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Use these font colors against the corresponding main layout colors. * In a light theme, these go from dark to light. */ /* Defaults use Material Design specification */ --jp-ui-font-color0: rgba(0, 0, 0, 1); --jp-ui-font-color1: rgba(0, 0, 0, 0.87); --jp-ui-font-color2: rgba(0, 0, 0, 0.54); --jp-ui-font-color3: rgba(0, 0, 0, 0.38); /* * Use these against the brand/accent/warn/error colors. * These will typically go from light to darker, in both a dark and light theme. */ --jp-ui-inverse-font-color0: rgba(255, 255, 255, 1); --jp-ui-inverse-font-color1: rgba(255, 255, 255, 1); --jp-ui-inverse-font-color2: rgba(255, 255, 255, 0.7); --jp-ui-inverse-font-color3: rgba(255, 255, 255, 0.5); /* Content Fonts * * Content font variables are used for typography of user generated content. * * The font sizing here is done assuming that the body font size of --jp-content-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-content-line-height: 1.6; --jp-content-font-scale-factor: 1.2; --jp-content-font-size0: 0.83333em; --jp-content-font-size1: 14px; /* Base font size */ --jp-content-font-size2: 1.2em; --jp-content-font-size3: 1.44em; --jp-content-font-size4: 1.728em; --jp-content-font-size5: 2.0736em; /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-content-presentation-font-size1: 17px; --jp-content-heading-line-height: 1; --jp-content-heading-margin-top: 1.2em; --jp-content-heading-margin-bottom: 0.8em; --jp-content-heading-font-weight: 500; /* Defaults use Material Design specification */ --jp-content-font-color0: rgba(0, 0, 0, 1); --jp-content-font-color1: rgba(0, 0, 0, 0.87); --jp-content-font-color2: rgba(0, 0, 0, 0.54); --jp-content-font-color3: rgba(0, 0, 0, 0.38); --jp-content-link-color: var(--md-blue-700); --jp-content-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Code Fonts * * Code font variables are used for typography of code and other monospaces content. */ --jp-code-font-size: 13px; --jp-code-line-height: 1.3077; /* 17px for 13px base */ --jp-code-padding: 5px; /* 5px for 13px base, codemirror highlighting needs integer px value */ --jp-code-font-family-default: Menlo, Consolas, \"DejaVu Sans Mono\", monospace; --jp-code-font-family: var(--jp-code-font-family-default); /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-code-presentation-font-size: 16px; /* may need to tweak cursor width if you change font size */ --jp-code-cursor-width0: 1.4px; --jp-code-cursor-width1: 2px; --jp-code-cursor-width2: 4px; /* Layout * * The following are the main layout colors use in JupyterLab. In a light * theme these would go from light to dark. */ --jp-layout-color0: white; --jp-layout-color1: white; --jp-layout-color2: var(--md-grey-200); --jp-layout-color3: var(--md-grey-400); --jp-layout-color4: var(--md-grey-600); /* Inverse Layout * * The following are the inverse layout colors use in JupyterLab. In a light * theme these would go from dark to light. */ --jp-inverse-layout-color0: #111111; --jp-inverse-layout-color1: var(--md-grey-900); --jp-inverse-layout-color2: var(--md-grey-800); --jp-inverse-layout-color3: var(--md-grey-700); --jp-inverse-layout-color4: var(--md-grey-600); /* Brand/accent */ --jp-brand-color0: var(--md-blue-900); --jp-brand-color1: var(--md-blue-700); --jp-brand-color2: var(--md-blue-300); --jp-brand-color3: var(--md-blue-100); --jp-brand-color4: var(--md-blue-50); --jp-accent-color0: var(--md-green-900); --jp-accent-color1: var(--md-green-700); --jp-accent-color2: var(--md-green-300); --jp-accent-color3: var(--md-green-100); /* State colors (warn, error, success, info) */ --jp-warn-color0: var(--md-orange-900); --jp-warn-color1: var(--md-orange-700); --jp-warn-color2: var(--md-orange-300); --jp-warn-color3: var(--md-orange-100); --jp-error-color0: var(--md-red-900); --jp-error-color1: var(--md-red-700); --jp-error-color2: var(--md-red-300); --jp-error-color3: var(--md-red-100); --jp-success-color0: var(--md-green-900); --jp-success-color1: var(--md-green-700); --jp-success-color2: var(--md-green-300); --jp-success-color3: var(--md-green-100); --jp-info-color0: var(--md-cyan-900); --jp-info-color1: var(--md-cyan-700); --jp-info-color2: var(--md-cyan-300); --jp-info-color3: var(--md-cyan-100); /* Cell specific styles */ --jp-cell-padding: 5px; --jp-cell-collapser-width: 8px; --jp-cell-collapser-min-height: 20px; --jp-cell-collapser-not-active-hover-opacity: 0.6; --jp-cell-editor-background: var(--md-grey-100); --jp-cell-editor-border-color: var(--md-grey-300); --jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-cell-editor-active-background: var(--jp-layout-color0); --jp-cell-editor-active-border-color: var(--jp-brand-color1); --jp-cell-prompt-width: 64px; --jp-cell-prompt-font-family: var(--jp-code-font-family-default); --jp-cell-prompt-letter-spacing: 0px; --jp-cell-prompt-opacity: 1; --jp-cell-prompt-not-active-opacity: 0.5; --jp-cell-prompt-not-active-font-color: var(--md-grey-700); /* A custom blend of MD grey and blue 600 * See https://meyerweb.com/eric/tools/color-blend/#546E7A:1E88E5:5:hex */ --jp-cell-inprompt-font-color: #307fc1; /* A custom blend of MD grey and orange 600 * https://meyerweb.com/eric/tools/color-blend/#546E7A:F4511E:5:hex */ --jp-cell-outprompt-font-color: #bf5b3d; /* Notebook specific styles */ --jp-notebook-padding: 10px; --jp-notebook-select-background: var(--jp-layout-color1); --jp-notebook-multiselected-color: var(--md-blue-50); /* The scroll padding is calculated to fill enough space at the bottom of the notebook to show one single-line cell (with appropriate padding) at the top when the notebook is scrolled all the way to the bottom. We also subtract one pixel so that no scrollbar appears if we have just one single-line cell in the notebook. This padding is to enable a 'scroll past end' feature in a notebook. */ --jp-notebook-scroll-padding: calc( 100% - var(--jp-code-font-size) * var(--jp-code-line-height) - var(--jp-code-padding) - var(--jp-cell-padding) - 1px ); /* Rendermime styles */ --jp-rendermime-error-background: #fdd; --jp-rendermime-table-row-background: var(--md-grey-100); --jp-rendermime-table-row-hover-background: var(--md-light-blue-50); /* Dialog specific styles */ --jp-dialog-background: rgba(0, 0, 0, 0.25); /* Console specific styles */ --jp-console-padding: 10px; /* Toolbar specific styles */ --jp-toolbar-border-color: var(--jp-border-color1); --jp-toolbar-micro-height: 8px; --jp-toolbar-background: var(--jp-layout-color1); --jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, 0.24); --jp-toolbar-header-margin: 4px 4px 0px 4px; --jp-toolbar-active-background: var(--md-grey-300); /* Statusbar specific styles */ --jp-statusbar-height: 24px; /* Input field styles */ --jp-input-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-input-active-background: var(--jp-layout-color1); --jp-input-hover-background: var(--jp-layout-color1); --jp-input-background: var(--md-grey-100); --jp-input-border-color: var(--jp-border-color1); --jp-input-active-border-color: var(--jp-brand-color1); --jp-input-active-box-shadow-color: rgba(19, 124, 189, 0.3); /* General editor styles */ --jp-editor-selected-background: #d9d9d9; --jp-editor-selected-focused-background: #d7d4f0; --jp-editor-cursor-color: var(--jp-ui-font-color0); /* Code mirror specific styles */ --jp-mirror-editor-keyword-color: #008000; --jp-mirror-editor-atom-color: #88f; --jp-mirror-editor-number-color: #080; --jp-mirror-editor-def-color: #00f; --jp-mirror-editor-variable-color: var(--md-grey-900); --jp-mirror-editor-variable-2-color: #05a; --jp-mirror-editor-variable-3-color: #085; --jp-mirror-editor-punctuation-color: #05a; --jp-mirror-editor-property-color: #05a; --jp-mirror-editor-operator-color: #aa22ff; --jp-mirror-editor-comment-color: #408080; --jp-mirror-editor-string-color: #ba2121; --jp-mirror-editor-string-2-color: #708; --jp-mirror-editor-meta-color: #aa22ff; --jp-mirror-editor-qualifier-color: #555; --jp-mirror-editor-builtin-color: #008000; --jp-mirror-editor-bracket-color: #997; --jp-mirror-editor-tag-color: #170; --jp-mirror-editor-attribute-color: #00c; --jp-mirror-editor-header-color: blue; --jp-mirror-editor-quote-color: #090; --jp-mirror-editor-link-color: #00c; --jp-mirror-editor-error-color: #f00; --jp-mirror-editor-hr-color: #999; /* Vega extension styles */ --jp-vega-background: white; /* Sidebar-related styles */ --jp-sidebar-min-width: 250px; /* Search-related styles */ --jp-search-toggle-off-opacity: 0.5; --jp-search-toggle-hover-opacity: 0.8; --jp-search-toggle-on-opacity: 1; --jp-search-selected-match-background-color: rgb(245, 200, 0); --jp-search-selected-match-color: black; --jp-search-unselected-match-background-color: var( --jp-inverse-layout-color0 ); --jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0); /* Icon colors that work well with light or dark backgrounds */ --jp-icon-contrast-color0: var(--md-purple-600); --jp-icon-contrast-color1: var(--md-green-600); --jp-icon-contrast-color2: var(--md-pink-600); --jp-icon-contrast-color3: var(--md-blue-600); } [data-md-color-scheme=\"slate\"] .jupyter-wrapper { /* Elevation * * We style box-shadows using Material Design's idea of elevation. These particular numbers are taken from here: * * https://github.com/material-components/material-components-web * https://material-components-web.appspot.com/elevation.html */ /* The dark theme shadows need a bit of work, but this will probably also require work on the core layout * colors used in the theme as well. */ --jp-shadow-base-lightness: 32; --jp-shadow-umbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.2 ); --jp-shadow-penumbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.14 ); --jp-shadow-ambient-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.12 ); --jp-elevation-z0: none; --jp-elevation-z1: 0px 2px 1px -1px var(--jp-shadow-umbra-color), 0px 1px 1px 0px var(--jp-shadow-penumbra-color), 0px 1px 3px 0px var(--jp-shadow-ambient-color); --jp-elevation-z2: 0px 3px 1px -2px var(--jp-shadow-umbra-color), 0px 2px 2px 0px var(--jp-shadow-penumbra-color), 0px 1px 5px 0px var(--jp-shadow-ambient-color); --jp-elevation-z4: 0px 2px 4px -1px var(--jp-shadow-umbra-color), 0px 4px 5px 0px var(--jp-shadow-penumbra-color), 0px 1px 10px 0px var(--jp-shadow-ambient-color); --jp-elevation-z6: 0px 3px 5px -1px var(--jp-shadow-umbra-color), 0px 6px 10px 0px var(--jp-shadow-penumbra-color), 0px 1px 18px 0px var(--jp-shadow-ambient-color); --jp-elevation-z8: 0px 5px 5px -3px var(--jp-shadow-umbra-color), 0px 8px 10px 1px var(--jp-shadow-penumbra-color), 0px 3px 14px 2px var(--jp-shadow-ambient-color); --jp-elevation-z12: 0px 7px 8px -4px var(--jp-shadow-umbra-color), 0px 12px 17px 2px var(--jp-shadow-penumbra-color), 0px 5px 22px 4px var(--jp-shadow-ambient-color); --jp-elevation-z16: 0px 8px 10px -5px var(--jp-shadow-umbra-color), 0px 16px 24px 2px var(--jp-shadow-penumbra-color), 0px 6px 30px 5px var(--jp-shadow-ambient-color); --jp-elevation-z20: 0px 10px 13px -6px var(--jp-shadow-umbra-color), 0px 20px 31px 3px var(--jp-shadow-penumbra-color), 0px 8px 38px 7px var(--jp-shadow-ambient-color); --jp-elevation-z24: 0px 11px 15px -7px var(--jp-shadow-umbra-color), 0px 24px 38px 3px var(--jp-shadow-penumbra-color), 0px 9px 46px 8px var(--jp-shadow-ambient-color); /* Borders * * The following variables, specify the visual styling of borders in JupyterLab. */ --jp-border-width: 1px; --jp-border-color0: var(--md-grey-700); --jp-border-color1: var(--md-grey-700); --jp-border-color2: var(--md-grey-800); --jp-border-color3: var(--md-grey-900); --jp-border-radius: 2px; /* UI Fonts * * The UI font CSS variables are used for the typography all of the JupyterLab * user interface elements that are not directly user generated content. * * The font sizing here is done assuming that the body font size of --jp-ui-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-ui-font-scale-factor: 1.2; --jp-ui-font-size0: 0.83333em; --jp-ui-font-size1: 13px; /* Base font size */ --jp-ui-font-size2: 1.2em; --jp-ui-font-size3: 1.44em; --jp-ui-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Use these font colors against the corresponding main layout colors. * In a light theme, these go from dark to light. */ /* Defaults use Material Design specification */ --jp-ui-font-color0: rgba(255, 255, 255, 1); --jp-ui-font-color1: rgba(255, 255, 255, 0.87); --jp-ui-font-color2: rgba(255, 255, 255, 0.54); --jp-ui-font-color3: rgba(255, 255, 255, 0.38); /* * Use these against the brand/accent/warn/error colors. * These will typically go from light to darker, in both a dark and light theme. */ --jp-ui-inverse-font-color0: rgba(0, 0, 0, 1); --jp-ui-inverse-font-color1: rgba(0, 0, 0, 0.8); --jp-ui-inverse-font-color2: rgba(0, 0, 0, 0.5); --jp-ui-inverse-font-color3: rgba(0, 0, 0, 0.3); /* Content Fonts * * Content font variables are used for typography of user generated content. * * The font sizing here is done assuming that the body font size of --jp-content-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-content-line-height: 1.6; --jp-content-font-scale-factor: 1.2; --jp-content-font-size0: 0.83333em; --jp-content-font-size1: 14px; /* Base font size */ --jp-content-font-size2: 1.2em; --jp-content-font-size3: 1.44em; --jp-content-font-size4: 1.728em; --jp-content-font-size5: 2.0736em; /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-content-presentation-font-size1: 17px; --jp-content-heading-line-height: 1; --jp-content-heading-margin-top: 1.2em; --jp-content-heading-margin-bottom: 0.8em; --jp-content-heading-font-weight: 500; /* Defaults use Material Design specification */ --jp-content-font-color0: rgba(255, 255, 255, 1); --jp-content-font-color1: rgba(255, 255, 255, 1); --jp-content-font-color2: rgba(255, 255, 255, 0.7); --jp-content-font-color3: rgba(255, 255, 255, 0.5); --jp-content-link-color: var(--md-blue-300); --jp-content-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Code Fonts * * Code font variables are used for typography of code and other monospaces content. */ --jp-code-font-size: 13px; --jp-code-line-height: 1.3077; /* 17px for 13px base */ --jp-code-padding: 5px; /* 5px for 13px base, codemirror highlighting needs integer px value */ --jp-code-font-family-default: Menlo, Consolas, \"DejaVu Sans Mono\", monospace; --jp-code-font-family: var(--jp-code-font-family-default); /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-code-presentation-font-size: 16px; /* may need to tweak cursor width if you change font size */ --jp-code-cursor-width0: 1.4px; --jp-code-cursor-width1: 2px; --jp-code-cursor-width2: 4px; /* Layout * * The following are the main layout colors use in JupyterLab. In a light * theme these would go from light to dark. */ --jp-layout-color0: #111111; --jp-layout-color1: var(--md-grey-900); --jp-layout-color2: var(--md-grey-800); --jp-layout-color3: var(--md-grey-700); --jp-layout-color4: var(--md-grey-600); /* Inverse Layout * * The following are the inverse layout colors use in JupyterLab. In a light * theme these would go from dark to light. */ --jp-inverse-layout-color0: white; --jp-inverse-layout-color1: white; --jp-inverse-layout-color2: var(--md-grey-200); --jp-inverse-layout-color3: var(--md-grey-400); --jp-inverse-layout-color4: var(--md-grey-600); /* Brand/accent */ --jp-brand-color0: var(--md-blue-700); --jp-brand-color1: var(--md-blue-500); --jp-brand-color2: var(--md-blue-300); --jp-brand-color3: var(--md-blue-100); --jp-brand-color4: var(--md-blue-50); --jp-accent-color0: var(--md-green-700); --jp-accent-color1: var(--md-green-500); --jp-accent-color2: var(--md-green-300); --jp-accent-color3: var(--md-green-100); /* State colors (warn, error, success, info) */ --jp-warn-color0: var(--md-orange-700); --jp-warn-color1: var(--md-orange-500); --jp-warn-color2: var(--md-orange-300); --jp-warn-color3: var(--md-orange-100); --jp-error-color0: var(--md-red-700); --jp-error-color1: var(--md-red-500); --jp-error-color2: var(--md-red-300); --jp-error-color3: var(--md-red-100); --jp-success-color0: var(--md-green-700); --jp-success-color1: var(--md-green-500); --jp-success-color2: var(--md-green-300); --jp-success-color3: var(--md-green-100); --jp-info-color0: var(--md-cyan-700); --jp-info-color1: var(--md-cyan-500); --jp-info-color2: var(--md-cyan-300); --jp-info-color3: var(--md-cyan-100); /* Cell specific styles */ --jp-cell-padding: 5px; --jp-cell-collapser-width: 8px; --jp-cell-collapser-min-height: 20px; --jp-cell-collapser-not-active-hover-opacity: 0.6; --jp-cell-editor-background: var(--jp-layout-color1); --jp-cell-editor-border-color: var(--md-grey-700); --jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-cell-editor-active-background: var(--jp-layout-color0); --jp-cell-editor-active-border-color: var(--jp-brand-color1); --jp-cell-prompt-width: 64px; --jp-cell-prompt-font-family: var(--jp-code-font-family-default); --jp-cell-prompt-letter-spacing: 0px; --jp-cell-prompt-opacity: 1; --jp-cell-prompt-not-active-opacity: 1; --jp-cell-prompt-not-active-font-color: var(--md-grey-300); /* A custom blend of MD grey and blue 600 * See https://meyerweb.com/eric/tools/color-blend/#546E7A:1E88E5:5:hex */ --jp-cell-inprompt-font-color: #307fc1; /* A custom blend of MD grey and orange 600 * https://meyerweb.com/eric/tools/color-blend/#546E7A:F4511E:5:hex */ --jp-cell-outprompt-font-color: #bf5b3d; /* Notebook specific styles */ --jp-notebook-padding: 10px; --jp-notebook-select-background: var(--jp-layout-color1); --jp-notebook-multiselected-color: rgba(33, 150, 243, 0.24); /* The scroll padding is calculated to fill enough space at the bottom of the notebook to show one single-line cell (with appropriate padding) at the top when the notebook is scrolled all the way to the bottom. We also subtract one pixel so that no scrollbar appears if we have just one single-line cell in the notebook. This padding is to enable a 'scroll past end' feature in a notebook. */ --jp-notebook-scroll-padding: calc( 100% - var(--jp-code-font-size) * var(--jp-code-line-height) - var(--jp-code-padding) - var(--jp-cell-padding) - 1px ); /* Rendermime styles */ --jp-rendermime-error-background: rgba(244, 67, 54, 0.28); --jp-rendermime-table-row-background: var(--md-grey-900); --jp-rendermime-table-row-hover-background: rgba(3, 169, 244, 0.2); /* Dialog specific styles */ --jp-dialog-background: rgba(0, 0, 0, 0.6); /* Console specific styles */ --jp-console-padding: 10px; /* Toolbar specific styles */ --jp-toolbar-border-color: var(--jp-border-color2); --jp-toolbar-micro-height: 8px; --jp-toolbar-background: var(--jp-layout-color1); --jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, 0.8); --jp-toolbar-header-margin: 4px 4px 0px 4px; --jp-toolbar-active-background: var(--jp-layout-color0); /* Statusbar specific styles */ --jp-statusbar-height: 24px; /* Input field styles */ --jp-input-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-input-active-background: var(--jp-layout-color0); --jp-input-hover-background: var(--jp-layout-color2); --jp-input-background: var(--md-grey-800); --jp-input-border-color: var(--jp-border-color1); --jp-input-active-border-color: var(--jp-brand-color1); --jp-input-active-box-shadow-color: rgba(19, 124, 189, 0.3); /* General editor styles */ --jp-editor-selected-background: var(--jp-layout-color2); --jp-editor-selected-focused-background: rgba(33, 150, 243, 0.24); --jp-editor-cursor-color: var(--jp-ui-font-color0); /* Code mirror specific styles */ --jp-mirror-editor-keyword-color: var(--md-green-500); --jp-mirror-editor-atom-color: var(--md-blue-300); --jp-mirror-editor-number-color: var(--md-green-400); --jp-mirror-editor-def-color: var(--md-blue-600); --jp-mirror-editor-variable-color: var(--md-grey-300); --jp-mirror-editor-variable-2-color: var(--md-blue-400); --jp-mirror-editor-variable-3-color: var(--md-green-600); --jp-mirror-editor-punctuation-color: var(--md-blue-400); --jp-mirror-editor-property-color: var(--md-blue-400); --jp-mirror-editor-operator-color: #aa22ff; --jp-mirror-editor-comment-color: #408080; --jp-mirror-editor-string-color: #ff7070; --jp-mirror-editor-string-2-color: var(--md-purple-300); --jp-mirror-editor-meta-color: #aa22ff; --jp-mirror-editor-qualifier-color: #555; --jp-mirror-editor-builtin-color: var(--md-green-600); --jp-mirror-editor-bracket-color: #997; --jp-mirror-editor-tag-color: var(--md-green-700); --jp-mirror-editor-attribute-color: var(--md-blue-700); --jp-mirror-editor-header-color: var(--md-blue-500); --jp-mirror-editor-quote-color: var(--md-green-300); --jp-mirror-editor-link-color: var(--md-blue-700); --jp-mirror-editor-error-color: #f00; --jp-mirror-editor-hr-color: #999; /* Vega extension styles */ --jp-vega-background: var(--md-grey-400); /* Sidebar-related styles */ --jp-sidebar-min-width: 250px; /* Search-related styles */ --jp-search-toggle-off-opacity: 0.6; --jp-search-toggle-hover-opacity: 0.8; --jp-search-toggle-on-opacity: 1; --jp-search-selected-match-background-color: rgb(255, 225, 0); --jp-search-selected-match-color: black; --jp-search-unselected-match-background-color: var( --jp-inverse-layout-color0 ); --jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0); /* scrollbar related styles. Supports every browser except Edge. */ /* colors based on JetBrain's Darcula theme */ --jp-scrollbar-background-color: #3f4244; --jp-scrollbar-thumb-color: 88, 96, 97; /* need to specify thumb color as an RGB triplet */ --jp-scrollbar-endpad: 3px; /* the minimum gap between the thumb and the ends of a scrollbar */ /* hacks for setting the thumb shape. These do nothing in Firefox */ --jp-scrollbar-thumb-margin: 3.5px; /* the space in between the sides of the thumb and the track */ --jp-scrollbar-thumb-radius: 9px; /* set to a large-ish value for rounded endcaps on the thumb */ /* Icon colors that work well with light or dark backgrounds */ --jp-icon-contrast-color0: var(--md-purple-600); --jp-icon-contrast-color1: var(--md-green-600); --jp-icon-contrast-color2: var(--md-pink-600); --jp-icon-contrast-color3: var(--md-blue-600); } :root{--md-red-50: #ffebee;--md-red-100: #ffcdd2;--md-red-200: #ef9a9a;--md-red-300: #e57373;--md-red-400: #ef5350;--md-red-500: #f44336;--md-red-600: #e53935;--md-red-700: #d32f2f;--md-red-800: #c62828;--md-red-900: #b71c1c;--md-red-A100: #ff8a80;--md-red-A200: #ff5252;--md-red-A400: #ff1744;--md-red-A700: #d50000;--md-pink-50: #fce4ec;--md-pink-100: #f8bbd0;--md-pink-200: #f48fb1;--md-pink-300: #f06292;--md-pink-400: #ec407a;--md-pink-500: #e91e63;--md-pink-600: #d81b60;--md-pink-700: #c2185b;--md-pink-800: 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#7986cb;--md-indigo-400: #5c6bc0;--md-indigo-500: #3f51b5;--md-indigo-600: #3949ab;--md-indigo-700: #303f9f;--md-indigo-800: #283593;--md-indigo-900: #1a237e;--md-indigo-A100: #8c9eff;--md-indigo-A200: #536dfe;--md-indigo-A400: #3d5afe;--md-indigo-A700: #304ffe;--md-blue-50: #e3f2fd;--md-blue-100: #bbdefb;--md-blue-200: #90caf9;--md-blue-300: #64b5f6;--md-blue-400: #42a5f5;--md-blue-500: #2196f3;--md-blue-600: #1e88e5;--md-blue-700: #1976d2;--md-blue-800: #1565c0;--md-blue-900: #0d47a1;--md-blue-A100: #82b1ff;--md-blue-A200: #448aff;--md-blue-A400: #2979ff;--md-blue-A700: #2962ff;--md-light-blue-50: #e1f5fe;--md-light-blue-100: #b3e5fc;--md-light-blue-200: #81d4fa;--md-light-blue-300: #4fc3f7;--md-light-blue-400: #29b6f6;--md-light-blue-500: #03a9f4;--md-light-blue-600: #039be5;--md-light-blue-700: #0288d1;--md-light-blue-800: #0277bd;--md-light-blue-900: #01579b;--md-light-blue-A100: #80d8ff;--md-light-blue-A200: #40c4ff;--md-light-blue-A400: #00b0ff;--md-light-blue-A700: #0091ea;--md-cyan-50: #e0f7fa;--md-cyan-100: #b2ebf2;--md-cyan-200: #80deea;--md-cyan-300: #4dd0e1;--md-cyan-400: #26c6da;--md-cyan-500: #00bcd4;--md-cyan-600: #00acc1;--md-cyan-700: #0097a7;--md-cyan-800: #00838f;--md-cyan-900: #006064;--md-cyan-A100: #84ffff;--md-cyan-A200: #18ffff;--md-cyan-A400: #00e5ff;--md-cyan-A700: #00b8d4;--md-teal-50: #e0f2f1;--md-teal-100: #b2dfdb;--md-teal-200: #80cbc4;--md-teal-300: #4db6ac;--md-teal-400: #26a69a;--md-teal-500: #009688;--md-teal-600: #00897b;--md-teal-700: #00796b;--md-teal-800: #00695c;--md-teal-900: #004d40;--md-teal-A100: #a7ffeb;--md-teal-A200: #64ffda;--md-teal-A400: #1de9b6;--md-teal-A700: #00bfa5;--md-green-50: #e8f5e9;--md-green-100: #c8e6c9;--md-green-200: #a5d6a7;--md-green-300: #81c784;--md-green-400: #66bb6a;--md-green-500: #4caf50;--md-green-600: #43a047;--md-green-700: #388e3c;--md-green-800: #2e7d32;--md-green-900: #1b5e20;--md-green-A100: #b9f6ca;--md-green-A200: #69f0ae;--md-green-A400: #00e676;--md-green-A700: #00c853;--md-light-green-50: #f1f8e9;--md-light-green-100: #dcedc8;--md-light-green-200: #c5e1a5;--md-light-green-300: #aed581;--md-light-green-400: #9ccc65;--md-light-green-500: #8bc34a;--md-light-green-600: #7cb342;--md-light-green-700: #689f38;--md-light-green-800: #558b2f;--md-light-green-900: #33691e;--md-light-green-A100: #ccff90;--md-light-green-A200: #b2ff59;--md-light-green-A400: #76ff03;--md-light-green-A700: #64dd17;--md-lime-50: #f9fbe7;--md-lime-100: #f0f4c3;--md-lime-200: #e6ee9c;--md-lime-300: #dce775;--md-lime-400: #d4e157;--md-lime-500: #cddc39;--md-lime-600: #c0ca33;--md-lime-700: #afb42b;--md-lime-800: #9e9d24;--md-lime-900: #827717;--md-lime-A100: #f4ff81;--md-lime-A200: #eeff41;--md-lime-A400: #c6ff00;--md-lime-A700: #aeea00;--md-yellow-50: #fffde7;--md-yellow-100: #fff9c4;--md-yellow-200: #fff59d;--md-yellow-300: #fff176;--md-yellow-400: #ffee58;--md-yellow-500: #ffeb3b;--md-yellow-600: #fdd835;--md-yellow-700: #fbc02d;--md-yellow-800: #f9a825;--md-yellow-900: #f57f17;--md-yellow-A100: #ffff8d;--md-yellow-A200: #ffff00;--md-yellow-A400: #ffea00;--md-yellow-A700: #ffd600;--md-amber-50: #fff8e1;--md-amber-100: #ffecb3;--md-amber-200: #ffe082;--md-amber-300: #ffd54f;--md-amber-400: #ffca28;--md-amber-500: #ffc107;--md-amber-600: #ffb300;--md-amber-700: #ffa000;--md-amber-800: #ff8f00;--md-amber-900: #ff6f00;--md-amber-A100: #ffe57f;--md-amber-A200: #ffd740;--md-amber-A400: #ffc400;--md-amber-A700: #ffab00;--md-orange-50: #fff3e0;--md-orange-100: #ffe0b2;--md-orange-200: #ffcc80;--md-orange-300: #ffb74d;--md-orange-400: #ffa726;--md-orange-500: #ff9800;--md-orange-600: #fb8c00;--md-orange-700: #f57c00;--md-orange-800: #ef6c00;--md-orange-900: #e65100;--md-orange-A100: #ffd180;--md-orange-A200: #ffab40;--md-orange-A400: #ff9100;--md-orange-A700: #ff6d00;--md-deep-orange-50: #fbe9e7;--md-deep-orange-100: #ffccbc;--md-deep-orange-200: #ffab91;--md-deep-orange-300: #ff8a65;--md-deep-orange-400: #ff7043;--md-deep-orange-500: 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sourceMappingURL=mkdocs-jupyter.css.map*/ init_mathjax = function() { if (window.MathJax) { // MathJax loaded MathJax.Hub.Config({ TeX: { equationNumbers: { autoNumber: \"AMS\", useLabelIds: true } }, tex2jax: { inlineMath: [ ['$','$'], [\"\\\\(\",\"\\\\)\"] ], displayMath: [ ['$$','$$'], [\"\\\\[\",\"\\\\]\"] ], processEscapes: true, processEnvironments: true }, displayAlign: 'center', CommonHTML: { linebreaks: { automatic: true } } }); MathJax.Hub.Queue([\"Typeset\", MathJax.Hub]); } } init_mathjax(); Raw data alignment This tutorial will cover the steps for performing the alignment of raw RNA- and HiFi-sequencing data. You will need to use the software IGV on your computer to visualize some of the output files, which can be easily downloaded once they are produced. At the end of this tutorial you will be able to: perform and discuss quality control on raw data in fastq format using FastQC and MultiQC align HiFi and RNA sequencing data with dedicated tools such as MiniMap2 and STAR analyze the quality the alignment with qualimap The output of this notebook will be used for the Variant calling analysis and the bulk RNA-sequencing analysis. If you do not want to run this notebook, you can alternatively use the free interactive tool Galaxy to perform the alignment steps. We have uploaded the data on Galaxy, and the manual to perform the exercise is found at the course webpage . The present tutorial, like the rest of the course material, is available at our open-source github repository . A few introductory points to run this notebook (click to show): To use this notebook, use the NGS (python) kernel that contains the packages. Choose it by selecting Kernel -> Change Kernel in the menu on top of the window. In this notebook you will use only bash commands as you would do in the command line (this is why you read %%bash at the beginning of each piece of code). Those commands can be replicated in the command line, but we thought of integrating them in a notebook to make the tutorial understandable. The bash commands can also be marked with an ! sign at the beginning of the line On some computers, you might see the result of the commands once they are done running. This means you will wait some time while the computer is crunching, and only afterwards you will see the result of the command you have executed You can run the code in each cell by clicking on the run cell button, or by pressing Shift + Enter . When the code is done running, a small green check sign will appear on the left side You need to run the cells in sequential order, please do not run a cell until the one above finished running and do not skip any cells Each cell contains a short description of the code and the output you should get. Please try not to focus on understanding the code for each command in too much detail, but rather try to focus on the output You can create new code cells by pressing + in the Menu bar above. Biological background \u00b6 White clover (Trifolium repens) is an allotetraploid. It is a relatively young, outcrossing species, which originated during the most recent glaciation around 20,000 years ago by hybridisation of two diploid species, T. occidentale and T. pallescens (see figure below). This means that it contains genomes originating from two different species within the same nucleus. Normally, white clover is an outbreeding species, but a self-compatible line was used for sequencing the white clover genome (Griffiths et al, 2019) . This line will be designated as S10 in the data, indicating that this is the 10th self-fertilized generation. In addition, we have data from a wild clover accession (ecotype) called Tienshan ( Ti ), which is collected from the Chinese mountains and is adapted to alpine conditions. We will perform alignment of the data to the white clover's reference genome containing both T. occidentale and T. pallescens (called contig 1 and contig 2 in the data). We will also perform alignment to each subgenome, and see which are the differences with the quality control tools. Quality control and mapping \u00b6 Quality Control \u00b6 We run FastQC on the PacBio Hifi reads and on two of the Illumina RNA-seq libraries. FastQC does quality control of the raw sequence data, providing an overview of the data which can help identify if there are any problems that should be addressed before further analysis. You can find the report for each file into the folder results/fastqc_output/ . The output is in HTML format and can be opened in any browser or in jupyterlab . It is however not easy to compare the various libraries by opening separate reports. To aggregate all the results, we apply the MultiQC software to the reports' folder. The output of MultiQC is in the directory results/multiqc_output/fastqc_data . In [1]: Copied! %% bash #run fastqc mkdir - p results / fastqc_output fastqc - q - o results / fastqc_output ../ Data / Clover_Data /*. fastq > / dev / null 2 >& 1 %%bash #run fastqc mkdir -p results/fastqc_output fastqc -q -o results/fastqc_output ../Data/Clover_Data/*.fastq > /dev/null 2>&1 Note: fastqc prints a lot of output conisting of a simple confirmation of execution without error, even when using the option -q , which means quiet . Therefore we added > /dev/null 2>&1 to the command to mute the printing of that output. In [2]: Copied! %% bash #run multiqc multiqc -- outdir results / multiqc_output / fastqc_data results / fastqc_output %%bash #run multiqc multiqc --outdir results/multiqc_output/fastqc_data results/fastqc_output / / / \u001b]8;id=903972;https://multiqc.info\u001b\\ MultiQC \u001b]8;;\u001b\\ \ud83d\udd0d | v1.14 | multiqc | Search path : /work/SamueleSoraggi/Notebooks/results/fastqc_output | searching | \u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501 100% 50/50 .html | fastqc | Found 25 reports | multiqc | Compressing plot data | multiqc | Report : results/multiqc_output/fastqc_data/multiqc_report.html | multiqc | Data : results/multiqc_output/fastqc_data/multiqc_data | multiqc | MultiQC complete Questions Visualize the Webpage generated by MultiQC. Hint: You can find a Help button that offers additional information about the plots for each panel. Focus on the following panels: \u201cPer base sequence quality\u201d, \u201cPer sequence quality scores\u201d.... (\u201cPer base sequence content\u201d always gives a FAIL for RNA-seq data). What do you notice with respect to the sequence quality scores? Are there any other quality issues worth noting? Hifi data mapping \u00b6 We map the PacBio Hifi reads ( Hifi_reads_white_clover.fastq ) to the white clover reference sequence (Contig1&2) using minimap2 . We run two mapping rounds, using two different preset options ( -x in the command) for the technology: PacBio/Oxford Nanopore read to reference mapping: map-pb Long assembly to reference mapping. Divergence is below 20%\u201d settings asm20 . Next, we create reports of the mapping results by running QualiMap on the two obtained SAM files. We first need to index the reference fasta files using samtools faidx . This produces files in .fai format containing informations about length of the reference sequence, offset for the quality scores, name of the reference sequence. Click here for a detailed overview. In [3]: Copied! %% bash #copy the reference data in the folder reference_data, so that you can write the indexing files mkdir - p reference_data cp ../ Data / Clover_Data / DNA_Contig1_2 . fasta ../ Data / Clover_Data / DNA_Contig1 . fasta ../ Data / Clover_Data / DNA_Contig2 . fasta reference_data %%bash #copy the reference data in the folder reference_data, so that you can write the indexing files mkdir -p reference_data cp ../Data/Clover_Data/DNA_Contig1_2.fasta ../Data/Clover_Data/DNA_Contig1.fasta ../Data/Clover_Data/DNA_Contig2.fasta reference_data In [4]: Copied! %% bash samtools faidx reference_data / DNA_Contig1_2 . fasta samtools faidx reference_data / DNA_Contig1 . fasta samtools faidx reference_data / DNA_Contig2 . fasta %%bash samtools faidx reference_data/DNA_Contig1_2.fasta samtools faidx reference_data/DNA_Contig1.fasta samtools faidx reference_data/DNA_Contig2.fasta we create an output folder for the HIFI alignment, and run minimap2 with the settings explained before. In [5]: Copied! %% bash mkdir - p results / HIFI_alignment / minimap2 - a - x map - pb - o results / HIFI_alignment / PacBio_clover_alignment_1_2_mappb . sam \\ reference_data / DNA_Contig1_2 . fasta \\ ../ Data / Clover_Data / Hifi_reads_white_clover . fastq minimap2 - a - x asm20 - o results / HIFI_alignment / PacBio_clover_alignment_1_2_asm20 . sam \\ reference_data / DNA_Contig1_2 . fasta \\ ../ Data / Clover_Data / Hifi_reads_white_clover . fastq %%bash mkdir -p results/HIFI_alignment/ minimap2 -a -x map-pb -o results/HIFI_alignment/PacBio_clover_alignment_1_2_mappb.sam \\ reference_data/DNA_Contig1_2.fasta \\ ../Data/Clover_Data/Hifi_reads_white_clover.fastq minimap2 -a -x asm20 -o results/HIFI_alignment/PacBio_clover_alignment_1_2_asm20.sam \\ reference_data/DNA_Contig1_2.fasta \\ ../Data/Clover_Data/Hifi_reads_white_clover.fastq [M::mm_idx_gen::0.075*1.04] collected minimizers [M::mm_idx_gen::0.099*1.52] sorted minimizers [M::main::0.099*1.52] loaded/built the index for 2 target sequence(s) [M::mm_mapopt_update::0.114*1.45] mid_occ = 11 [M::mm_idx_stat] kmer size: 19; skip: 10; is_hpc: 1; #seq: 2 [M::mm_idx_stat::0.118*1.43] distinct minimizers: 203943 (79.05% are singletons); average occurrences: 1.273; average spacing: 8.047; total length: 2089554 [M::worker_pipeline::11.859*2.89] mapped 4395 sequences [M::main] Version: 2.24-r1122 [M::main] CMD: minimap2 -a -x map-pb -o results/HIFI_alignment/PacBio_clover_alignment_1_2_mappb.sam reference_data/DNA_Contig1_2.fasta ../Data/Clover_Data/Hifi_reads_white_clover.fastq [M::main] Real time: 11.873 sec; CPU: 34.246 sec; Peak RSS: 1.186 GB [M::mm_idx_gen::0.084*1.04] collected minimizers [M::mm_idx_gen::0.121*1.62] sorted minimizers [M::main::0.121*1.62] loaded/built the index for 2 target sequence(s) [M::mm_mapopt_update::0.138*1.54] mid_occ = 50 [M::mm_idx_stat] kmer size: 19; skip: 10; is_hpc: 0; #seq: 2 [M::mm_idx_stat::0.143*1.52] distinct minimizers: 298340 (78.21% are singletons); average occurrences: 1.277; average spacing: 5.484; total length: 2089554 [M::worker_pipeline::18.366*2.88] mapped 4395 sequences [M::main] Version: 2.24-r1122 [M::main] CMD: minimap2 -a -x asm20 -o results/HIFI_alignment/PacBio_clover_alignment_1_2_asm20.sam reference_data/DNA_Contig1_2.fasta ../Data/Clover_Data/Hifi_reads_white_clover.fastq [M::main] Real time: 18.394 sec; CPU: 52.932 sec; Peak RSS: 1.692 GB samtools sort is used to sort the alignment with left-to-right coordinates. The output is in .bam format, with .sam files in input (Note that you could have gotten .bam files from minimap2 with a specific option). In [6]: Copied! %% bash samtools sort results / HIFI_alignment / PacBio_clover_alignment_1_2_mappb . sam \\ - o results / HIFI_alignment / PacBio_clover_alignment_1_2_mappb . sort . bam samtools sort results / HIFI_alignment / PacBio_clover_alignment_1_2_asm20 . sam \\ - o results / HIFI_alignment / PacBio_clover_alignment_1_2_asm20 . sort . bam %%bash samtools sort results/HIFI_alignment/PacBio_clover_alignment_1_2_mappb.sam \\ -o results/HIFI_alignment/PacBio_clover_alignment_1_2_mappb.sort.bam samtools sort results/HIFI_alignment/PacBio_clover_alignment_1_2_asm20.sam \\ -o results/HIFI_alignment/PacBio_clover_alignment_1_2_asm20.sort.bam samtools index creates the index for the bam file, stored in .bai format. The index file lets programs access any position into the aligned data without reading the whole file, which would take too much time. In [7]: Copied! %% bash samtools index results / HIFI_alignment / PacBio_clover_alignment_1_2_mappb . sort . bam samtools index results / HIFI_alignment / PacBio_clover_alignment_1_2_asm20 . sort . bam %%bash samtools index results/HIFI_alignment/PacBio_clover_alignment_1_2_mappb.sort.bam samtools index results/HIFI_alignment/PacBio_clover_alignment_1_2_asm20.sort.bam Run quality control on both files In [8]: Copied! %% bash qualimap bamqc - bam results / HIFI_alignment / PacBio_clover_alignment_1_2_mappb . sort . bam \\ - outdir results / qualimap_output / PacBio_clover_alignment_1_2_mappb qualimap bamqc - bam results / HIFI_alignment / PacBio_clover_alignment_1_2_asm20 . sort . bam \\ - outdir results / qualimap_output / PacBio_clover_alignment_1_2_asm20 %%bash qualimap bamqc -bam results/HIFI_alignment/PacBio_clover_alignment_1_2_mappb.sort.bam \\ -outdir results/qualimap_output/PacBio_clover_alignment_1_2_mappb qualimap bamqc -bam results/HIFI_alignment/PacBio_clover_alignment_1_2_asm20.sort.bam \\ -outdir results/qualimap_output/PacBio_clover_alignment_1_2_asm20 Java memory size is set to 1200M Launching application... QualiMap v.2.2.2-dev Built on 2019-11-11 14:05 Selected tool: bamqc Available memory (Mb): 33 Max memory (Mb): 1258 Starting bam qc.... Loading sam header... Loading locator... Loading reference... Number of windows: 400, effective number of windows: 401 Chunk of reads size: 1000 Number of threads: 8 Processed 50 out of 401 windows... Processed 100 out of 401 windows... Processed 150 out of 401 windows... Processed 200 out of 401 windows... Processed 250 out of 401 windows... Processed 300 out of 401 windows... Processed 350 out of 401 windows... Processed 400 out of 401 windows... Total processed windows:401 Number of reads: 4395 Number of valid reads: 4696 Number of correct strand reads:0 Inside of regions... Num mapped reads: 4395 Num mapped first of pair: 0 Num mapped second of pair: 0 Num singletons: 0 Time taken to analyze reads: 12 Computing descriptors... numberOfMappedBases: 70274383 referenceSize: 2089554 numberOfSequencedBases: 70039397 numberOfAs: 23621784 Computing per chromosome statistics... Computing histograms... Overall analysis time: 12 end of bam qc Computing report... Writing HTML report... HTML report created successfully Finished Java memory size is set to 1200M Launching application... QualiMap v.2.2.2-dev Built on 2019-11-11 14:05 Selected tool: bamqc Available memory (Mb): 33 Max memory (Mb): 1258 Starting bam qc.... Loading sam header... Loading locator... Loading reference... Number of windows: 400, effective number of windows: 401 Chunk of reads size: 1000 Number of threads: 8 Processed 50 out of 401 windows... Processed 100 out of 401 windows... Processed 150 out of 401 windows... Processed 200 out of 401 windows... Processed 250 out of 401 windows... Processed 300 out of 401 windows... Processed 350 out of 401 windows... Processed 400 out of 401 windows... Total processed windows:401 Number of reads: 4395 Number of valid reads: 4747 Number of correct strand reads:0 Inside of regions... Num mapped reads: 4395 Num mapped first of pair: 0 Num mapped second of pair: 0 Num singletons: 0 Time taken to analyze reads: 12 Computing descriptors... numberOfMappedBases: 69947563 referenceSize: 2089554 numberOfSequencedBases: 69812688 numberOfAs: 23541478 Computing per chromosome statistics... Computing histograms... Overall analysis time: 12 end of bam qc Computing report... Writing HTML report... HTML report created successfully Finished For easier comparison, we can again collapse the two reports into a single one using MultiQC , in the same way we did for putting together the other reports from fastQC . In [9]: Copied! %% bash #run multiqc multiqc -- outdir results / qualimap_output results / qualimap_output %%bash #run multiqc multiqc --outdir results/qualimap_output results/qualimap_output / / / \u001b]8;id=941002;https://multiqc.info\u001b\\ MultiQC \u001b]8;;\u001b\\ \ud83d\udd0d | v1.14 | multiqc | Search path : /work/SamueleSoraggi/Notebooks/results/qualimap_output | searching | \u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501 100% 93/93 nt.t\u2026 | qualimap | Found 2 BamQC reports | multiqc | Compressing plot data | multiqc | Report : results/qualimap_output/multiqc_report.html | multiqc | Data : results/qualimap_output/multiqc_data | multiqc | MultiQC complete Now you can visualize the report generated, which is in results/qualimap_output/multiqc_report.html . Next, we map the white clover PacBio Hifi reads to contig1 and contig2 separately, using the setting you selected at the previous step (let's say map-pb was chosen, but you are free to change this setting in the commands). As the two contigs represent the two white clover subgenomes, this mapping will allow you to see the two subgenome haplotypes and call subgenome SNPs. In [10]: Copied! %% bash minimap2 - a - x map - pb - o results / HIFI_alignment / PacBio_clover_alignment_1 . sam \\ reference_data / DNA_Contig1 . fasta \\ ../ Data / Clover_Data / Hifi_reads_white_clover . fastq %%bash minimap2 -a -x map-pb -o results/HIFI_alignment/PacBio_clover_alignment_1.sam \\ reference_data/DNA_Contig1.fasta \\ ../Data/Clover_Data/Hifi_reads_white_clover.fastq [M::mm_idx_gen::0.050*0.96] collected minimizers [M::mm_idx_gen::0.071*1.55] sorted minimizers [M::main::0.071*1.55] loaded/built the index for 1 target sequence(s) [M::mm_mapopt_update::0.079*1.50] mid_occ = 10 [M::mm_idx_stat] kmer size: 19; skip: 10; is_hpc: 1; #seq: 1 [M::mm_idx_stat::0.082*1.48] distinct minimizers: 112547 (91.45% are singletons); average occurrences: 1.141; average spacing: 8.031; total length: 1031631 [M::worker_pipeline::27.024*2.90] mapped 4395 sequences [M::main] Version: 2.24-r1122 [M::main] CMD: minimap2 -a -x map-pb -o results/HIFI_alignment/PacBio_clover_alignment_1.sam reference_data/DNA_Contig1.fasta ../Data/Clover_Data/Hifi_reads_white_clover.fastq [M::main] Real time: 27.035 sec; CPU: 78.431 sec; Peak RSS: 2.258 GB In [11]: Copied! %% bash minimap2 - a - x map - pb - o results / HIFI_alignment / PacBio_clover_alignment_2 . sam \\ reference_data / DNA_Contig2 . fasta \\ ../ Data / Clover_Data / Hifi_reads_white_clover . fastq %%bash minimap2 -a -x map-pb -o results/HIFI_alignment/PacBio_clover_alignment_2.sam \\ reference_data/DNA_Contig2.fasta \\ ../Data/Clover_Data/Hifi_reads_white_clover.fastq [M::mm_idx_gen::0.048*1.04] collected minimizers [M::mm_idx_gen::0.069*1.61] sorted minimizers [M::main::0.069*1.61] loaded/built the index for 1 target sequence(s) [M::mm_mapopt_update::0.076*1.55] mid_occ = 10 [M::mm_idx_stat] kmer size: 19; skip: 10; is_hpc: 1; #seq: 1 [M::mm_idx_stat::0.080*1.53] distinct minimizers: 120722 (93.18% are singletons); average occurrences: 1.087; average spacing: 8.062; total length: 1057923 [M::worker_pipeline::27.150*2.94] mapped 4395 sequences [M::main] Version: 2.24-r1122 [M::main] CMD: minimap2 -a -x map-pb -o results/HIFI_alignment/PacBio_clover_alignment_2.sam reference_data/DNA_Contig2.fasta ../Data/Clover_Data/Hifi_reads_white_clover.fastq [M::main] Real time: 27.162 sec; CPU: 79.819 sec; Peak RSS: 2.058 GB Sort the bam files and create their index using samtools In [12]: Copied! %% bash samtools sort results / HIFI_alignment / PacBio_clover_alignment_1 . sam - o results / HIFI_alignment / PacBio_clover_alignment_1 . sort . bam samtools sort results / HIFI_alignment / PacBio_clover_alignment_2 . sam - o results / HIFI_alignment / PacBio_clover_alignment_2 . sort . bam %%bash samtools sort results/HIFI_alignment/PacBio_clover_alignment_1.sam -o results/HIFI_alignment/PacBio_clover_alignment_1.sort.bam samtools sort results/HIFI_alignment/PacBio_clover_alignment_2.sam -o results/HIFI_alignment/PacBio_clover_alignment_2.sort.bam In [13]: Copied! %% bash samtools index results / HIFI_alignment / PacBio_clover_alignment_1 . sort . bam samtools index results / HIFI_alignment / PacBio_clover_alignment_2 . sort . bam %%bash samtools index results/HIFI_alignment/PacBio_clover_alignment_1.sort.bam samtools index results/HIFI_alignment/PacBio_clover_alignment_2.sort.bam Perform quality control In [14]: Copied! %% bash mkdir - p results / qualimap_output qualimap bamqc - bam results / HIFI_alignment / PacBio_clover_alignment_1 . sort . bam - outdir results / qualimap_output / PacBio_clover_alignment_1 %%bash mkdir -p results/qualimap_output qualimap bamqc -bam results/HIFI_alignment/PacBio_clover_alignment_1.sort.bam -outdir results/qualimap_output/PacBio_clover_alignment_1 Java memory size is set to 1200M Launching application... QualiMap v.2.2.2-dev Built on 2019-11-11 14:05 Selected tool: bamqc Available memory (Mb): 33 Max memory (Mb): 1258 Starting bam qc.... Loading sam header... Loading locator... Loading reference... Number of windows: 400, effective number of windows: 400 Chunk of reads size: 1000 Number of threads: 8 Processed 50 out of 400 windows... Processed 100 out of 400 windows... Processed 150 out of 400 windows... Processed 200 out of 400 windows... Processed 250 out of 400 windows... Processed 300 out of 400 windows... Processed 350 out of 400 windows... Processed 400 out of 400 windows... Total processed windows:400 Number of reads: 4395 Number of valid reads: 9070 Number of correct strand reads:0 Inside of regions... Num mapped reads: 4356 Num mapped first of pair: 0 Num mapped second of pair: 0 Num singletons: 0 Time taken to analyze reads: 10 Computing descriptors... numberOfMappedBases: 59058058 referenceSize: 1031631 numberOfSequencedBases: 54917933 numberOfAs: 18508479 Computing per chromosome statistics... Computing histograms... Overall analysis time: 11 end of bam qc Computing report... Writing HTML report... HTML report created successfully Finished In [15]: Copied! %% bash qualimap bamqc - bam results / HIFI_alignment / PacBio_clover_alignment_2 . sort . bam - outdir results / qualimap_output / PacBio_clover_alignment_2 %%bash qualimap bamqc -bam results/HIFI_alignment/PacBio_clover_alignment_2.sort.bam -outdir results/qualimap_output/PacBio_clover_alignment_2 Java memory size is set to 1200M Launching application... QualiMap v.2.2.2-dev Built on 2019-11-11 14:05 Selected tool: bamqc Available memory (Mb): 33 Max memory (Mb): 1258 Starting bam qc.... Loading sam header... Loading locator... Loading reference... Number of windows: 400, effective number of windows: 400 Chunk of reads size: 1000 Number of threads: 8 Processed 50 out of 400 windows... Processed 100 out of 400 windows... Processed 150 out of 400 windows... Processed 200 out of 400 windows... Processed 250 out of 400 windows... Processed 300 out of 400 windows... Processed 350 out of 400 windows... Processed 400 out of 400 windows... Total processed windows:400 Number of reads: 4395 Number of valid reads: 9462 Number of correct strand reads:0 Inside of regions... Num mapped reads: 4394 Num mapped first of pair: 0 Num mapped second of pair: 0 Num singletons: 0 Time taken to analyze reads: 13 Computing descriptors... numberOfMappedBases: 59388453 referenceSize: 1057923 numberOfSequencedBases: 55867300 numberOfAs: 18822184 Computing per chromosome statistics... Computing histograms... Overall analysis time: 13 end of bam qc Computing report... Writing HTML report... HTML report created successfully Finished In [16]: Copied! %% bash #run multiqc multiqc -- outdir results / qualimap_output results / qualimap_output %%bash #run multiqc multiqc --outdir results/qualimap_output results/qualimap_output / / / \u001b]8;id=999624;https://multiqc.info\u001b\\ MultiQC \u001b]8;;\u001b\\ \ud83d\udd0d | v1.14 | multiqc | Search path : /work/SamueleSoraggi/Notebooks/results/qualimap_output | searching | \u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501 100% 187/187 .t\u2026 | qualimap | Found 4 BamQC reports | multiqc | Compressing plot data | multiqc | Previous MultiQC output found! Adjusting filenames.. | multiqc | Use -f or --force to overwrite existing reports instead | multiqc | Report : results/qualimap_output/multiqc_report_1.html | multiqc | Data : results/qualimap_output/multiqc_data_1 | multiqc | MultiQC complete Task: IGV visualization and Questions Now you can inspect the alignment files in IGV . First, you will need to download the reference fasta sequence in ../Data/Clover_Data/DNA_Contig1_2.fasta and import it into IGV. You can do the same for the files DNA_Contig1.fasta and DNA_Contig2.fasta that you might need later. In IGV, this is done with the menu Genomes --> Load Genome from file menu and by selecting the relevant fasta file. Then, choose the reference you need from the drop-down menu (see figure below). You will not yet see much, but you can choose one of the two subgenomes (contig 1 or 2) and double click on a chromosome position to inspect the reference sequence. The next step will visualize the mapped files on IGV. Each mapped genome can be seen in IGV against the reference file of choice. To load an aligned file, first download it together with the index file in .bai format. For example, you need to download both results/HIFI_alignment/PacBio_clover_alignment_1.sort.bam and results/HIFI_alignment/PacBio_clover_alignment_1.sort.bam.bai to see this alignment (you need to open only the .bam file with IGV). If you open more files, their alignments will be distributed in the IGV interface, and you can change the size of each visualization yourself (below shown with only one opened alignment). Now compare in IGV the two bam files PacBio_clover_alignment_1.sort.bam and PacBio_clover_alignment_2.sort.bam . What do you observe when comparing the two BAM files? Have a look at the polymorphic regions in IGV. Are they true polymorphisms? Add to the visualization the third alignment PacBio_clover_alignment_1_2_mappb.sort.bam in IGV. Why do you see fluctuations in coverage and large regions without any apparent subgenome SNPs? What are the major differences between the stats for the reads mapped to Contigs1&2 versus contig1 and contig2? What is your interpretation of the differences? RNA-seq mapping \u00b6 In the ../Data folder you will find 24 RNA-seq libraries, 12 S10 libraries and 12 Tienshan libraries. Each library is paired-end, which is denoted by R1 and R2 at the end of two files having the same name, such as S10_1_1.R1.fastq and S10_1_1.R2.fastq . We will align each library separately and then merge the alignments to create two final samples for S10 and Tienshan . First, we need to create a genome file for the reference fasta file of contigs 1 and 2 . This is done with STAR , using the option --runMode genomeGenerate . We also need to convert the gene annotation from gff to gtf format with gffread to allow counting gene transcripts. STAR is a very complex tool with many options, so it is always useful to have a reference manual . In [17]: Copied! %% bash gffread - T - o reference_data / white_clover_genes . gtf ../ Data / Clover_Data / white_clover_genes . gff %%bash gffread -T -o reference_data/white_clover_genes.gtf ../Data/Clover_Data/white_clover_genes.gff In [18]: Copied! %% bash STAR -- runThreadN 8 \\ -- runMode genomeGenerate \\ -- genomeDir results / STAR_output / indexing_contigs_1_2 \\ -- genomeFastaFiles reference_data / DNA_Contig1_2 . fasta \\ -- sjdbGTFfile reference_data / white_clover_genes . gtf %%bash STAR --runThreadN 8 \\ --runMode genomeGenerate \\ --genomeDir results/STAR_output/indexing_contigs_1_2 \\ --genomeFastaFiles reference_data/DNA_Contig1_2.fasta \\ --sjdbGTFfile reference_data/white_clover_genes.gtf STAR --runThreadN 8 --runMode genomeGenerate --genomeDir results/STAR_output/indexing_contigs_1_2 --genomeFastaFiles reference_data/DNA_Contig1_2.fasta --sjdbGTFfile reference_data/white_clover_genes.gtf STAR version: 2.7.10a compiled: 2022-01-14T18:50:00-05:00 :/home/dobin/data/STAR/STARcode/STAR.master/source Jun 19 10:23:03 ..... started STAR run Jun 19 10:23:03 ... starting to generate Genome files Jun 19 10:23:03 ..... processing annotations GTF !!!!! WARNING: --genomeSAindexNbases 14 is too large for the genome size=2089554, which may cause seg-fault at the mapping step. Re-run genome generation with recommended --genomeSAindexNbases 9 Jun 19 10:23:03 ... starting to sort Suffix Array. This may take a long time... Jun 19 10:23:03 ... sorting Suffix Array chunks and saving them to disk... Jun 19 10:23:04 ... loading chunks from disk, packing SA... Jun 19 10:23:04 ... finished generating suffix array Jun 19 10:23:04 ... generating Suffix Array index Jun 19 10:23:08 ... completed Suffix Array index Jun 19 10:23:08 ..... inserting junctions into the genome indices Jun 19 10:23:16 ... writing Genome to disk ... Jun 19 10:23:16 ... writing Suffix Array to disk ... Jun 19 10:23:17 ... writing SAindex to disk Jun 19 10:23:18 ..... finished successfully We got a warning saying !!!!! WARNING: --genomeSAindexNbases 14 is too large for the genome size=2089554, which may cause seg-fault at the mapping step. Re-run genome generation with recommended --genomeSAindexNbases 9 meaning we need shorter strings of bases (9 bases instead of 14) to be indexed, as our reference genome is very short, and too long strings would cause many alignment errors. So we rerun the command with the suggested option. In [19]: Copied! %% bash STAR -- runThreadN 8 \\ -- runMode genomeGenerate \\ -- genomeDir results / STAR_output / indexing_contigs_1_2 \\ -- genomeFastaFiles reference_data / DNA_Contig1_2 . fasta \\ -- sjdbGTFfile reference_data / white_clover_genes . gtf \\ -- genomeSAindexNbases 9 %%bash STAR --runThreadN 8 \\ --runMode genomeGenerate \\ --genomeDir results/STAR_output/indexing_contigs_1_2 \\ --genomeFastaFiles reference_data/DNA_Contig1_2.fasta \\ --sjdbGTFfile reference_data/white_clover_genes.gtf \\ --genomeSAindexNbases 9 STAR --runThreadN 8 --runMode genomeGenerate --genomeDir results/STAR_output/indexing_contigs_1_2 --genomeFastaFiles reference_data/DNA_Contig1_2.fasta --sjdbGTFfile reference_data/white_clover_genes.gtf --genomeSAindexNbases 9 STAR version: 2.7.10a compiled: 2022-01-14T18:50:00-05:00 :/home/dobin/data/STAR/STARcode/STAR.master/source Jun 19 10:23:18 ..... started STAR run Jun 19 10:23:18 ... starting to generate Genome files Jun 19 10:23:18 ..... processing annotations GTF Jun 19 10:23:20 ... starting to sort Suffix Array. This may take a long time... Jun 19 10:23:20 ... sorting Suffix Array chunks and saving them to disk... Jun 19 10:23:21 ... loading chunks from disk, packing SA... Jun 19 10:23:21 ... finished generating suffix array Jun 19 10:23:21 ... generating Suffix Array index Jun 19 10:23:21 ... completed Suffix Array index Jun 19 10:23:22 ..... inserting junctions into the genome indices Jun 19 10:23:23 ... writing Genome to disk ... Jun 19 10:23:23 ... writing Suffix Array to disk ... Jun 19 10:23:23 ... writing SAindex to disk Jun 19 10:23:24 ..... finished successfully We use again STAR to align every single library for S10 . We extract the library name of each file and run STAR through each pair of files. Note that plant introns are very rarely more than 5000 bp and that you are mapping to two homoeologous contigs that show high similarity, especially in genic regions. We set the maximum size to 5000 using --alignIntronMax 5000 . In [20]: Copied! %% bash for i in ` ls ../ Data / Clover_Data / S10 *. R1 . fastq ` do PREFIXNAME = ` basename $ i . R1 . fastq ` echo \"###############################################\" echo \"##### ALIGNING PAIRED-END READS \" $ PREFIXNAME echo \"###############################################\" STAR -- genomeDir results / STAR_output / indexing_contigs_1_2 / \\ -- runThreadN 8 \\ -- runMode alignReads \\ -- readFilesIn ../ Data / Clover_Data / $ PREFIXNAME . R1 . fastq ../ Data / Clover_Data / $ PREFIXNAME . R2 . fastq \\ -- outFileNamePrefix results / STAR_output / S10_align_contigs_1_2 / $ PREFIXNAME \\ -- outSAMtype BAM SortedByCoordinate \\ -- outSAMattributes Standard \\ -- quantMode GeneCounts \\ -- alignIntronMax 5000 done %%bash for i in `ls ../Data/Clover_Data/S10*.R1.fastq` do PREFIXNAME=`basename $i .R1.fastq` echo \"###############################################\" echo \"##### ALIGNING PAIRED-END READS \"$PREFIXNAME echo \"###############################################\" STAR --genomeDir results/STAR_output/indexing_contigs_1_2/ \\ --runThreadN 8 \\ --runMode alignReads \\ --readFilesIn ../Data/Clover_Data/$PREFIXNAME.R1.fastq ../Data/Clover_Data/$PREFIXNAME.R2.fastq \\ --outFileNamePrefix results/STAR_output/S10_align_contigs_1_2/$PREFIXNAME \\ --outSAMtype BAM SortedByCoordinate \\ --outSAMattributes Standard \\ --quantMode GeneCounts \\ --alignIntronMax 5000 done ############################################### ##### ALIGNING PAIRED-END READS S10_1_1 ############################################### STAR --genomeDir results/STAR_output/indexing_contigs_1_2/ --runThreadN 8 --runMode alignReads --readFilesIn ../Data/Clover_Data/S10_1_1.R1.fastq ../Data/Clover_Data/S10_1_1.R2.fastq --outFileNamePrefix results/STAR_output/S10_align_contigs_1_2/S10_1_1 --outSAMtype BAM SortedByCoordinate --outSAMattributes Standard --quantMode GeneCounts --alignIntronMax 5000 STAR version: 2.7.10a compiled: 2022-01-14T18:50:00-05:00 :/home/dobin/data/STAR/STARcode/STAR.master/source Jun 19 10:23:24 ..... started STAR run Jun 19 10:23:24 ..... loading genome Jun 19 10:23:24 ..... started mapping Jun 19 10:23:35 ..... finished mapping Jun 19 10:23:35 ..... started sorting BAM Jun 19 10:23:37 ..... finished successfully ############################################### ##### ALIGNING PAIRED-END READS S10_1_2 ############################################### STAR --genomeDir results/STAR_output/indexing_contigs_1_2/ --runThreadN 8 --runMode alignReads --readFilesIn ../Data/Clover_Data/S10_1_2.R1.fastq ../Data/Clover_Data/S10_1_2.R2.fastq --outFileNamePrefix results/STAR_output/S10_align_contigs_1_2/S10_1_2 --outSAMtype BAM SortedByCoordinate --outSAMattributes Standard --quantMode GeneCounts --alignIntronMax 5000 STAR version: 2.7.10a compiled: 2022-01-14T18:50:00-05:00 :/home/dobin/data/STAR/STARcode/STAR.master/source Jun 19 10:23:38 ..... started STAR run Jun 19 10:23:38 ..... loading genome Jun 19 10:23:38 ..... started mapping Jun 19 10:23:48 ..... finished mapping Jun 19 10:23:48 ..... started sorting BAM Jun 19 10:23:51 ..... finished successfully ############################################### ##### ALIGNING PAIRED-END READS S10_1_3 ############################################### STAR --genomeDir results/STAR_output/indexing_contigs_1_2/ --runThreadN 8 --runMode alignReads --readFilesIn ../Data/Clover_Data/S10_1_3.R1.fastq ../Data/Clover_Data/S10_1_3.R2.fastq --outFileNamePrefix results/STAR_output/S10_align_contigs_1_2/S10_1_3 --outSAMtype BAM SortedByCoordinate --outSAMattributes Standard --quantMode GeneCounts --alignIntronMax 5000 STAR version: 2.7.10a compiled: 2022-01-14T18:50:00-05:00 :/home/dobin/data/STAR/STARcode/STAR.master/source Jun 19 10:23:52 ..... started STAR run Jun 19 10:23:52 ..... loading genome Jun 19 10:23:52 ..... started mapping Jun 19 10:24:03 ..... finished mapping Jun 19 10:24:03 ..... started sorting BAM Jun 19 10:24:06 ..... finished successfully ############################################### ##### ALIGNING PAIRED-END READS S10_2_1 ############################################### STAR --genomeDir results/STAR_output/indexing_contigs_1_2/ --runThreadN 8 --runMode alignReads --readFilesIn ../Data/Clover_Data/S10_2_1.R1.fastq ../Data/Clover_Data/S10_2_1.R2.fastq --outFileNamePrefix results/STAR_output/S10_align_contigs_1_2/S10_2_1 --outSAMtype BAM SortedByCoordinate --outSAMattributes Standard --quantMode GeneCounts --alignIntronMax 5000 STAR version: 2.7.10a compiled: 2022-01-14T18:50:00-05:00 :/home/dobin/data/STAR/STARcode/STAR.master/source Jun 19 10:24:06 ..... started STAR run Jun 19 10:24:06 ..... loading genome Jun 19 10:24:06 ..... started mapping Jun 19 10:24:16 ..... finished mapping Jun 19 10:24:16 ..... started sorting BAM Jun 19 10:24:18 ..... finished successfully ############################################### ##### ALIGNING PAIRED-END READS S10_2_2 ############################################### STAR --genomeDir results/STAR_output/indexing_contigs_1_2/ --runThreadN 8 --runMode alignReads --readFilesIn ../Data/Clover_Data/S10_2_2.R1.fastq ../Data/Clover_Data/S10_2_2.R2.fastq --outFileNamePrefix results/STAR_output/S10_align_contigs_1_2/S10_2_2 --outSAMtype BAM SortedByCoordinate --outSAMattributes Standard --quantMode GeneCounts --alignIntronMax 5000 STAR version: 2.7.10a compiled: 2022-01-14T18:50:00-05:00 :/home/dobin/data/STAR/STARcode/STAR.master/source Jun 19 10:24:20 ..... started STAR run Jun 19 10:24:20 ..... loading genome Jun 19 10:24:20 ..... started mapping Jun 19 10:24:31 ..... finished mapping Jun 19 10:24:31 ..... started sorting BAM Jun 19 10:24:33 ..... finished successfully ############################################### ##### ALIGNING PAIRED-END READS S10_2_3 ############################################### STAR --genomeDir results/STAR_output/indexing_contigs_1_2/ --runThreadN 8 --runMode alignReads --readFilesIn ../Data/Clover_Data/S10_2_3.R1.fastq ../Data/Clover_Data/S10_2_3.R2.fastq --outFileNamePrefix results/STAR_output/S10_align_contigs_1_2/S10_2_3 --outSAMtype BAM SortedByCoordinate --outSAMattributes Standard --quantMode GeneCounts --alignIntronMax 5000 STAR version: 2.7.10a compiled: 2022-01-14T18:50:00-05:00 :/home/dobin/data/STAR/STARcode/STAR.master/source Jun 19 10:24:33 ..... started STAR run Jun 19 10:24:33 ..... loading genome Jun 19 10:24:33 ..... started mapping Jun 19 10:24:45 ..... finished mapping Jun 19 10:24:45 ..... started sorting BAM Jun 19 10:24:48 ..... finished successfully Do the same alignment for Tienshan libraries In [21]: Copied! %% bash for i in ` ls ../ Data / Clover_Data / TI *. R1 . fastq ` do PREFIXNAME = ` basename $ i . R1 . fastq ` echo \"###############################################\" echo \"##### ALIGNING PAIRED-END READS \" $ PREFIXNAME echo \"###############################################\" STAR -- genomeDir results / STAR_output / indexing_contigs_1_2 / \\ -- runThreadN 8 \\ -- readFilesIn ../ Data / Clover_Data / $ PREFIXNAME . R1 . fastq ../ Data / Clover_Data / $ PREFIXNAME . R2 . fastq \\ -- outFileNamePrefix results / STAR_output / TI_align_contigs_1_2 / $ PREFIXNAME \\ -- outSAMtype BAM SortedByCoordinate \\ -- outSAMattributes Standard \\ -- quantMode GeneCounts \\ -- alignIntronMax 5000 done %%bash for i in `ls ../Data/Clover_Data/TI*.R1.fastq` do PREFIXNAME=`basename $i .R1.fastq` echo \"###############################################\" echo \"##### ALIGNING PAIRED-END READS \"$PREFIXNAME echo \"###############################################\" STAR --genomeDir results/STAR_output/indexing_contigs_1_2/ \\ --runThreadN 8 \\ --readFilesIn ../Data/Clover_Data/$PREFIXNAME.R1.fastq ../Data/Clover_Data/$PREFIXNAME.R2.fastq \\ --outFileNamePrefix results/STAR_output/TI_align_contigs_1_2/$PREFIXNAME \\ --outSAMtype BAM SortedByCoordinate \\ --outSAMattributes Standard \\ --quantMode GeneCounts \\ --alignIntronMax 5000 done ############################################### ##### ALIGNING PAIRED-END READS TI_1_1 ############################################### STAR --genomeDir results/STAR_output/indexing_contigs_1_2/ --runThreadN 8 --readFilesIn ../Data/Clover_Data/TI_1_1.R1.fastq ../Data/Clover_Data/TI_1_1.R2.fastq --outFileNamePrefix results/STAR_output/TI_align_contigs_1_2/TI_1_1 --outSAMtype BAM SortedByCoordinate --outSAMattributes Standard --quantMode GeneCounts --alignIntronMax 5000 STAR version: 2.7.10a compiled: 2022-01-14T18:50:00-05:00 :/home/dobin/data/STAR/STARcode/STAR.master/source Jun 19 10:24:50 ..... started STAR run Jun 19 10:24:50 ..... loading genome Jun 19 10:24:50 ..... started mapping Jun 19 10:25:03 ..... finished mapping Jun 19 10:25:04 ..... started sorting BAM Jun 19 10:25:18 ..... finished successfully ############################################### ##### ALIGNING PAIRED-END READS TI_1_2 ############################################### STAR --genomeDir results/STAR_output/indexing_contigs_1_2/ --runThreadN 8 --readFilesIn ../Data/Clover_Data/TI_1_2.R1.fastq ../Data/Clover_Data/TI_1_2.R2.fastq --outFileNamePrefix results/STAR_output/TI_align_contigs_1_2/TI_1_2 --outSAMtype BAM SortedByCoordinate --outSAMattributes Standard --quantMode GeneCounts --alignIntronMax 5000 STAR version: 2.7.10a compiled: 2022-01-14T18:50:00-05:00 :/home/dobin/data/STAR/STARcode/STAR.master/source Jun 19 10:25:18 ..... started STAR run Jun 19 10:25:18 ..... loading genome Jun 19 10:25:18 ..... started mapping Jun 19 10:25:30 ..... finished mapping Jun 19 10:25:30 ..... started sorting BAM Jun 19 10:25:33 ..... finished successfully ############################################### ##### ALIGNING PAIRED-END READS TI_1_3 ############################################### STAR --genomeDir results/STAR_output/indexing_contigs_1_2/ --runThreadN 8 --readFilesIn ../Data/Clover_Data/TI_1_3.R1.fastq ../Data/Clover_Data/TI_1_3.R2.fastq --outFileNamePrefix results/STAR_output/TI_align_contigs_1_2/TI_1_3 --outSAMtype BAM SortedByCoordinate --outSAMattributes Standard --quantMode GeneCounts --alignIntronMax 5000 STAR version: 2.7.10a compiled: 2022-01-14T18:50:00-05:00 :/home/dobin/data/STAR/STARcode/STAR.master/source Jun 19 10:25:36 ..... started STAR run Jun 19 10:25:36 ..... loading genome Jun 19 10:25:36 ..... started mapping Jun 19 10:25:50 ..... finished mapping Jun 19 10:25:50 ..... started sorting BAM Jun 19 10:25:51 ..... finished successfully ############################################### ##### ALIGNING PAIRED-END READS TI_2_1 ############################################### STAR --genomeDir results/STAR_output/indexing_contigs_1_2/ --runThreadN 8 --readFilesIn ../Data/Clover_Data/TI_2_1.R1.fastq ../Data/Clover_Data/TI_2_1.R2.fastq --outFileNamePrefix results/STAR_output/TI_align_contigs_1_2/TI_2_1 --outSAMtype BAM SortedByCoordinate --outSAMattributes Standard --quantMode GeneCounts --alignIntronMax 5000 STAR version: 2.7.10a compiled: 2022-01-14T18:50:00-05:00 :/home/dobin/data/STAR/STARcode/STAR.master/source Jun 19 10:25:51 ..... started STAR run Jun 19 10:25:51 ..... loading genome Jun 19 10:25:52 ..... started mapping Jun 19 10:26:06 ..... finished mapping Jun 19 10:26:06 ..... started sorting BAM Jun 19 10:26:09 ..... finished successfully ############################################### ##### ALIGNING PAIRED-END READS TI_2_2 ############################################### STAR --genomeDir results/STAR_output/indexing_contigs_1_2/ --runThreadN 8 --readFilesIn ../Data/Clover_Data/TI_2_2.R1.fastq ../Data/Clover_Data/TI_2_2.R2.fastq --outFileNamePrefix results/STAR_output/TI_align_contigs_1_2/TI_2_2 --outSAMtype BAM SortedByCoordinate --outSAMattributes Standard --quantMode GeneCounts --alignIntronMax 5000 STAR version: 2.7.10a compiled: 2022-01-14T18:50:00-05:00 :/home/dobin/data/STAR/STARcode/STAR.master/source Jun 19 10:26:09 ..... started STAR run Jun 19 10:26:09 ..... loading genome Jun 19 10:26:09 ..... started mapping Jun 19 10:26:23 ..... finished mapping Jun 19 10:26:23 ..... started sorting BAM Jun 19 10:26:31 ..... finished successfully ############################################### ##### ALIGNING PAIRED-END READS TI_2_3 ############################################### STAR --genomeDir results/STAR_output/indexing_contigs_1_2/ --runThreadN 8 --readFilesIn ../Data/Clover_Data/TI_2_3.R1.fastq ../Data/Clover_Data/TI_2_3.R2.fastq --outFileNamePrefix results/STAR_output/TI_align_contigs_1_2/TI_2_3 --outSAMtype BAM SortedByCoordinate --outSAMattributes Standard --quantMode GeneCounts --alignIntronMax 5000 STAR version: 2.7.10a compiled: 2022-01-14T18:50:00-05:00 :/home/dobin/data/STAR/STARcode/STAR.master/source Jun 19 10:26:31 ..... started STAR run Jun 19 10:26:31 ..... loading genome Jun 19 10:26:31 ..... started mapping Jun 19 10:26:46 ..... finished mapping Jun 19 10:26:46 ..... started sorting BAM Jun 19 10:26:49 ..... finished successfully Run quality control on each aligned library with MultiQC . In this way there will be a whole report to compare S10 files and Tienshan files. In [22]: Copied! %% bash multiqc -- outdir results / multiqc_output / TI_STAR_align_1_2 \\ results / STAR_output / TI_align_contigs_1_2 / %%bash multiqc --outdir results/multiqc_output/TI_STAR_align_1_2 \\ results/STAR_output/TI_align_contigs_1_2/ / / / \u001b]8;id=121575;https://multiqc.info\u001b\\ MultiQC \u001b]8;;\u001b\\ \ud83d\udd0d | v1.14 | multiqc | Search path : /work/SamueleSoraggi/Notebooks/results/STAR_output/TI_align_contigs_1_2 | searching | \u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501 100% 36/36 og.o\u2026 | star | Found 6 reports and 6 gene count files | multiqc | Compressing plot data | multiqc | Report : results/multiqc_output/TI_STAR_align_1_2/multiqc_report.html | multiqc | Data : results/multiqc_output/TI_STAR_align_1_2/multiqc_data | multiqc | MultiQC complete In [23]: Copied! %% bash multiqc -- outdir results / multiqc_output / S10_STAR_align_1_2 \\ results / STAR_output / S10_align_contigs_1_2 / %%bash multiqc --outdir results/multiqc_output/S10_STAR_align_1_2 \\ results/STAR_output/S10_align_contigs_1_2/ / / / \u001b]8;id=596946;https://multiqc.info\u001b\\ MultiQC \u001b]8;;\u001b\\ \ud83d\udd0d | v1.14 | multiqc | Search path : /work/SamueleSoraggi/Notebooks/results/STAR_output/S10_align_contigs_1_2 | searching | \u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501 100% 36/36 ut.t\u2026 | star | Found 6 reports and 6 gene count files | multiqc | Compressing plot data | multiqc | Report : results/multiqc_output/S10_STAR_align_1_2/multiqc_report.html | multiqc | Data : results/multiqc_output/S10_STAR_align_1_2/multiqc_data | multiqc | MultiQC complete We merge the outputs of each group of aligned libraries. Here is how the files look like for the Tienshan . In [24]: Copied! ! ls - lh results / STAR_output / TI_align_contigs_1_2 / TI_ *. sortedByCoord . out . bam !ls -lh results/STAR_output/TI_align_contigs_1_2/TI_*.sortedByCoord.out.bam -rw-r--r--. 1 ucloud users 6.6M Jun 19 10:25 results/STAR_output/TI_align_contigs_1_2/TI_1_1Aligned.sortedByCoord.out.bam -rw-r--r--. 1 ucloud users 6.0M Jun 19 10:25 results/STAR_output/TI_align_contigs_1_2/TI_1_2Aligned.sortedByCoord.out.bam -rw-r--r--. 1 ucloud users 7.1M Jun 19 10:25 results/STAR_output/TI_align_contigs_1_2/TI_1_3Aligned.sortedByCoord.out.bam -rw-r--r--. 1 ucloud users 9.6M Jun 19 10:26 results/STAR_output/TI_align_contigs_1_2/TI_2_1Aligned.sortedByCoord.out.bam -rw-r--r--. 1 ucloud users 7.2M Jun 19 10:26 results/STAR_output/TI_align_contigs_1_2/TI_2_2Aligned.sortedByCoord.out.bam -rw-r--r--. 1 ucloud users 8.2M Jun 19 10:26 results/STAR_output/TI_align_contigs_1_2/TI_2_3Aligned.sortedByCoord.out.bam Apply samtools merge In [25]: Copied! %% bash mkdir - p results / STAR_output / TI_align_contigs_1_2_merge / samtools merge - f results / STAR_output / TI_align_contigs_1_2_merge / TI . sorted . bam results / STAR_output / TI_align_contigs_1_2 / TI_ *. sortedByCoord . out . bam %%bash mkdir -p results/STAR_output/TI_align_contigs_1_2_merge/ samtools merge -f results/STAR_output/TI_align_contigs_1_2_merge/TI.sorted.bam results/STAR_output/TI_align_contigs_1_2/TI_*.sortedByCoord.out.bam In [26]: Copied! %% bash mkdir - p results / STAR_output / S10_align_contigs_1_2_merge / samtools merge - f results / STAR_output / S10_align_contigs_1_2_merge / S10 . sorted . bam results / STAR_output / S10_align_contigs_1_2 / S10_ *. sortedByCoord . out . bam %%bash mkdir -p results/STAR_output/S10_align_contigs_1_2_merge/ samtools merge -f results/STAR_output/S10_align_contigs_1_2_merge/S10.sorted.bam results/STAR_output/S10_align_contigs_1_2/S10_*.sortedByCoord.out.bam Index both merging outputs. A file in format bam.bai will appear in their respective folders. In [27]: Copied! %% bash samtools index results / STAR_output / S10_align_contigs_1_2_merge / S10 . sorted . bam %%bash samtools index results/STAR_output/S10_align_contigs_1_2_merge/S10.sorted.bam In [28]: Copied! %% bash samtools index results / STAR_output / TI_align_contigs_1_2_merge / TI . sorted . bam %%bash samtools index results/STAR_output/TI_align_contigs_1_2_merge/TI.sorted.bam Wrapping up \ud83c\udf89 \ud83c\udf89 \ud83c\udf89 \u00b6 In this exercise, you learnt to align various types of data after performing quality control for raw data. We looked at some of the options for the aligners and at how to use some of the basic samtools manipulation programs. The outputs from the RNA alignments will be used for the VCF file analysis in the next notebook, and the RNA alignments will be use for the bulk RNA data analysis.","title":"3.1.Alignment of raw data"},{"location":"nb/align.html#biological-background","text":"White clover (Trifolium repens) is an allotetraploid. It is a relatively young, outcrossing species, which originated during the most recent glaciation around 20,000 years ago by hybridisation of two diploid species, T. occidentale and T. pallescens (see figure below). This means that it contains genomes originating from two different species within the same nucleus. Normally, white clover is an outbreeding species, but a self-compatible line was used for sequencing the white clover genome (Griffiths et al, 2019) . This line will be designated as S10 in the data, indicating that this is the 10th self-fertilized generation. In addition, we have data from a wild clover accession (ecotype) called Tienshan ( Ti ), which is collected from the Chinese mountains and is adapted to alpine conditions. We will perform alignment of the data to the white clover's reference genome containing both T. occidentale and T. pallescens (called contig 1 and contig 2 in the data). We will also perform alignment to each subgenome, and see which are the differences with the quality control tools.","title":"Biological background"},{"location":"nb/align.html#quality-control-and-mapping","text":"","title":"Quality control and mapping"},{"location":"nb/align.html#quality-control","text":"We run FastQC on the PacBio Hifi reads and on two of the Illumina RNA-seq libraries. FastQC does quality control of the raw sequence data, providing an overview of the data which can help identify if there are any problems that should be addressed before further analysis. You can find the report for each file into the folder results/fastqc_output/ . The output is in HTML format and can be opened in any browser or in jupyterlab . It is however not easy to compare the various libraries by opening separate reports. To aggregate all the results, we apply the MultiQC software to the reports' folder. The output of MultiQC is in the directory results/multiqc_output/fastqc_data . In [1]: Copied! %% bash #run fastqc mkdir - p results / fastqc_output fastqc - q - o results / fastqc_output ../ Data / Clover_Data /*. fastq > / dev / null 2 >& 1 %%bash #run fastqc mkdir -p results/fastqc_output fastqc -q -o results/fastqc_output ../Data/Clover_Data/*.fastq > /dev/null 2>&1 Note: fastqc prints a lot of output conisting of a simple confirmation of execution without error, even when using the option -q , which means quiet . Therefore we added > /dev/null 2>&1 to the command to mute the printing of that output. In [2]: Copied! %% bash #run multiqc multiqc -- outdir results / multiqc_output / fastqc_data results / fastqc_output %%bash #run multiqc multiqc --outdir results/multiqc_output/fastqc_data results/fastqc_output / / / \u001b]8;id=903972;https://multiqc.info\u001b\\ MultiQC \u001b]8;;\u001b\\ \ud83d\udd0d | v1.14 | multiqc | Search path : /work/SamueleSoraggi/Notebooks/results/fastqc_output | searching | \u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501 100% 50/50 .html | fastqc | Found 25 reports | multiqc | Compressing plot data | multiqc | Report : results/multiqc_output/fastqc_data/multiqc_report.html | multiqc | Data : results/multiqc_output/fastqc_data/multiqc_data | multiqc | MultiQC complete Questions Visualize the Webpage generated by MultiQC. Hint: You can find a Help button that offers additional information about the plots for each panel. Focus on the following panels: \u201cPer base sequence quality\u201d, \u201cPer sequence quality scores\u201d.... (\u201cPer base sequence content\u201d always gives a FAIL for RNA-seq data). What do you notice with respect to the sequence quality scores? Are there any other quality issues worth noting?","title":"Quality Control"},{"location":"nb/align.html#hifi-data-mapping","text":"We map the PacBio Hifi reads ( Hifi_reads_white_clover.fastq ) to the white clover reference sequence (Contig1&2) using minimap2 . We run two mapping rounds, using two different preset options ( -x in the command) for the technology: PacBio/Oxford Nanopore read to reference mapping: map-pb Long assembly to reference mapping. Divergence is below 20%\u201d settings asm20 . Next, we create reports of the mapping results by running QualiMap on the two obtained SAM files. We first need to index the reference fasta files using samtools faidx . This produces files in .fai format containing informations about length of the reference sequence, offset for the quality scores, name of the reference sequence. Click here for a detailed overview. In [3]: Copied! %% bash #copy the reference data in the folder reference_data, so that you can write the indexing files mkdir - p reference_data cp ../ Data / Clover_Data / DNA_Contig1_2 . fasta ../ Data / Clover_Data / DNA_Contig1 . fasta ../ Data / Clover_Data / DNA_Contig2 . fasta reference_data %%bash #copy the reference data in the folder reference_data, so that you can write the indexing files mkdir -p reference_data cp ../Data/Clover_Data/DNA_Contig1_2.fasta ../Data/Clover_Data/DNA_Contig1.fasta ../Data/Clover_Data/DNA_Contig2.fasta reference_data In [4]: Copied! %% bash samtools faidx reference_data / DNA_Contig1_2 . fasta samtools faidx reference_data / DNA_Contig1 . fasta samtools faidx reference_data / DNA_Contig2 . fasta %%bash samtools faidx reference_data/DNA_Contig1_2.fasta samtools faidx reference_data/DNA_Contig1.fasta samtools faidx reference_data/DNA_Contig2.fasta we create an output folder for the HIFI alignment, and run minimap2 with the settings explained before. In [5]: Copied! %% bash mkdir - p results / HIFI_alignment / minimap2 - a - x map - pb - o results / HIFI_alignment / PacBio_clover_alignment_1_2_mappb . sam \\ reference_data / DNA_Contig1_2 . fasta \\ ../ Data / Clover_Data / Hifi_reads_white_clover . fastq minimap2 - a - x asm20 - o results / HIFI_alignment / PacBio_clover_alignment_1_2_asm20 . sam \\ reference_data / DNA_Contig1_2 . fasta \\ ../ Data / Clover_Data / Hifi_reads_white_clover . fastq %%bash mkdir -p results/HIFI_alignment/ minimap2 -a -x map-pb -o results/HIFI_alignment/PacBio_clover_alignment_1_2_mappb.sam \\ reference_data/DNA_Contig1_2.fasta \\ ../Data/Clover_Data/Hifi_reads_white_clover.fastq minimap2 -a -x asm20 -o results/HIFI_alignment/PacBio_clover_alignment_1_2_asm20.sam \\ reference_data/DNA_Contig1_2.fasta \\ ../Data/Clover_Data/Hifi_reads_white_clover.fastq [M::mm_idx_gen::0.075*1.04] collected minimizers [M::mm_idx_gen::0.099*1.52] sorted minimizers [M::main::0.099*1.52] loaded/built the index for 2 target sequence(s) [M::mm_mapopt_update::0.114*1.45] mid_occ = 11 [M::mm_idx_stat] kmer size: 19; skip: 10; is_hpc: 1; #seq: 2 [M::mm_idx_stat::0.118*1.43] distinct minimizers: 203943 (79.05% are singletons); average occurrences: 1.273; average spacing: 8.047; total length: 2089554 [M::worker_pipeline::11.859*2.89] mapped 4395 sequences [M::main] Version: 2.24-r1122 [M::main] CMD: minimap2 -a -x map-pb -o results/HIFI_alignment/PacBio_clover_alignment_1_2_mappb.sam reference_data/DNA_Contig1_2.fasta ../Data/Clover_Data/Hifi_reads_white_clover.fastq [M::main] Real time: 11.873 sec; CPU: 34.246 sec; Peak RSS: 1.186 GB [M::mm_idx_gen::0.084*1.04] collected minimizers [M::mm_idx_gen::0.121*1.62] sorted minimizers [M::main::0.121*1.62] loaded/built the index for 2 target sequence(s) [M::mm_mapopt_update::0.138*1.54] mid_occ = 50 [M::mm_idx_stat] kmer size: 19; skip: 10; is_hpc: 0; #seq: 2 [M::mm_idx_stat::0.143*1.52] distinct minimizers: 298340 (78.21% are singletons); average occurrences: 1.277; average spacing: 5.484; total length: 2089554 [M::worker_pipeline::18.366*2.88] mapped 4395 sequences [M::main] Version: 2.24-r1122 [M::main] CMD: minimap2 -a -x asm20 -o results/HIFI_alignment/PacBio_clover_alignment_1_2_asm20.sam reference_data/DNA_Contig1_2.fasta ../Data/Clover_Data/Hifi_reads_white_clover.fastq [M::main] Real time: 18.394 sec; CPU: 52.932 sec; Peak RSS: 1.692 GB samtools sort is used to sort the alignment with left-to-right coordinates. The output is in .bam format, with .sam files in input (Note that you could have gotten .bam files from minimap2 with a specific option). In [6]: Copied! %% bash samtools sort results / HIFI_alignment / PacBio_clover_alignment_1_2_mappb . sam \\ - o results / HIFI_alignment / PacBio_clover_alignment_1_2_mappb . sort . bam samtools sort results / HIFI_alignment / PacBio_clover_alignment_1_2_asm20 . sam \\ - o results / HIFI_alignment / PacBio_clover_alignment_1_2_asm20 . sort . bam %%bash samtools sort results/HIFI_alignment/PacBio_clover_alignment_1_2_mappb.sam \\ -o results/HIFI_alignment/PacBio_clover_alignment_1_2_mappb.sort.bam samtools sort results/HIFI_alignment/PacBio_clover_alignment_1_2_asm20.sam \\ -o results/HIFI_alignment/PacBio_clover_alignment_1_2_asm20.sort.bam samtools index creates the index for the bam file, stored in .bai format. The index file lets programs access any position into the aligned data without reading the whole file, which would take too much time. In [7]: Copied! %% bash samtools index results / HIFI_alignment / PacBio_clover_alignment_1_2_mappb . sort . bam samtools index results / HIFI_alignment / PacBio_clover_alignment_1_2_asm20 . sort . bam %%bash samtools index results/HIFI_alignment/PacBio_clover_alignment_1_2_mappb.sort.bam samtools index results/HIFI_alignment/PacBio_clover_alignment_1_2_asm20.sort.bam Run quality control on both files In [8]: Copied! %% bash qualimap bamqc - bam results / HIFI_alignment / PacBio_clover_alignment_1_2_mappb . sort . bam \\ - outdir results / qualimap_output / PacBio_clover_alignment_1_2_mappb qualimap bamqc - bam results / HIFI_alignment / PacBio_clover_alignment_1_2_asm20 . sort . bam \\ - outdir results / qualimap_output / PacBio_clover_alignment_1_2_asm20 %%bash qualimap bamqc -bam results/HIFI_alignment/PacBio_clover_alignment_1_2_mappb.sort.bam \\ -outdir results/qualimap_output/PacBio_clover_alignment_1_2_mappb qualimap bamqc -bam results/HIFI_alignment/PacBio_clover_alignment_1_2_asm20.sort.bam \\ -outdir results/qualimap_output/PacBio_clover_alignment_1_2_asm20 Java memory size is set to 1200M Launching application... QualiMap v.2.2.2-dev Built on 2019-11-11 14:05 Selected tool: bamqc Available memory (Mb): 33 Max memory (Mb): 1258 Starting bam qc.... Loading sam header... Loading locator... Loading reference... Number of windows: 400, effective number of windows: 401 Chunk of reads size: 1000 Number of threads: 8 Processed 50 out of 401 windows... Processed 100 out of 401 windows... Processed 150 out of 401 windows... Processed 200 out of 401 windows... Processed 250 out of 401 windows... Processed 300 out of 401 windows... Processed 350 out of 401 windows... Processed 400 out of 401 windows... Total processed windows:401 Number of reads: 4395 Number of valid reads: 4696 Number of correct strand reads:0 Inside of regions... Num mapped reads: 4395 Num mapped first of pair: 0 Num mapped second of pair: 0 Num singletons: 0 Time taken to analyze reads: 12 Computing descriptors... numberOfMappedBases: 70274383 referenceSize: 2089554 numberOfSequencedBases: 70039397 numberOfAs: 23621784 Computing per chromosome statistics... Computing histograms... Overall analysis time: 12 end of bam qc Computing report... Writing HTML report... HTML report created successfully Finished Java memory size is set to 1200M Launching application... QualiMap v.2.2.2-dev Built on 2019-11-11 14:05 Selected tool: bamqc Available memory (Mb): 33 Max memory (Mb): 1258 Starting bam qc.... Loading sam header... Loading locator... Loading reference... Number of windows: 400, effective number of windows: 401 Chunk of reads size: 1000 Number of threads: 8 Processed 50 out of 401 windows... Processed 100 out of 401 windows... Processed 150 out of 401 windows... Processed 200 out of 401 windows... Processed 250 out of 401 windows... Processed 300 out of 401 windows... Processed 350 out of 401 windows... Processed 400 out of 401 windows... Total processed windows:401 Number of reads: 4395 Number of valid reads: 4747 Number of correct strand reads:0 Inside of regions... Num mapped reads: 4395 Num mapped first of pair: 0 Num mapped second of pair: 0 Num singletons: 0 Time taken to analyze reads: 12 Computing descriptors... numberOfMappedBases: 69947563 referenceSize: 2089554 numberOfSequencedBases: 69812688 numberOfAs: 23541478 Computing per chromosome statistics... Computing histograms... Overall analysis time: 12 end of bam qc Computing report... Writing HTML report... HTML report created successfully Finished For easier comparison, we can again collapse the two reports into a single one using MultiQC , in the same way we did for putting together the other reports from fastQC . In [9]: Copied! %% bash #run multiqc multiqc -- outdir results / qualimap_output results / qualimap_output %%bash #run multiqc multiqc --outdir results/qualimap_output results/qualimap_output / / / \u001b]8;id=941002;https://multiqc.info\u001b\\ MultiQC \u001b]8;;\u001b\\ \ud83d\udd0d | v1.14 | multiqc | Search path : /work/SamueleSoraggi/Notebooks/results/qualimap_output | searching | \u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501 100% 93/93 nt.t\u2026 | qualimap | Found 2 BamQC reports | multiqc | Compressing plot data | multiqc | Report : results/qualimap_output/multiqc_report.html | multiqc | Data : results/qualimap_output/multiqc_data | multiqc | MultiQC complete Now you can visualize the report generated, which is in results/qualimap_output/multiqc_report.html . Next, we map the white clover PacBio Hifi reads to contig1 and contig2 separately, using the setting you selected at the previous step (let's say map-pb was chosen, but you are free to change this setting in the commands). As the two contigs represent the two white clover subgenomes, this mapping will allow you to see the two subgenome haplotypes and call subgenome SNPs. In [10]: Copied! %% bash minimap2 - a - x map - pb - o results / HIFI_alignment / PacBio_clover_alignment_1 . sam \\ reference_data / DNA_Contig1 . fasta \\ ../ Data / Clover_Data / Hifi_reads_white_clover . fastq %%bash minimap2 -a -x map-pb -o results/HIFI_alignment/PacBio_clover_alignment_1.sam \\ reference_data/DNA_Contig1.fasta \\ ../Data/Clover_Data/Hifi_reads_white_clover.fastq [M::mm_idx_gen::0.050*0.96] collected minimizers [M::mm_idx_gen::0.071*1.55] sorted minimizers [M::main::0.071*1.55] loaded/built the index for 1 target sequence(s) [M::mm_mapopt_update::0.079*1.50] mid_occ = 10 [M::mm_idx_stat] kmer size: 19; skip: 10; is_hpc: 1; #seq: 1 [M::mm_idx_stat::0.082*1.48] distinct minimizers: 112547 (91.45% are singletons); average occurrences: 1.141; average spacing: 8.031; total length: 1031631 [M::worker_pipeline::27.024*2.90] mapped 4395 sequences [M::main] Version: 2.24-r1122 [M::main] CMD: minimap2 -a -x map-pb -o results/HIFI_alignment/PacBio_clover_alignment_1.sam reference_data/DNA_Contig1.fasta ../Data/Clover_Data/Hifi_reads_white_clover.fastq [M::main] Real time: 27.035 sec; CPU: 78.431 sec; Peak RSS: 2.258 GB In [11]: Copied! %% bash minimap2 - a - x map - pb - o results / HIFI_alignment / PacBio_clover_alignment_2 . sam \\ reference_data / DNA_Contig2 . fasta \\ ../ Data / Clover_Data / Hifi_reads_white_clover . fastq %%bash minimap2 -a -x map-pb -o results/HIFI_alignment/PacBio_clover_alignment_2.sam \\ reference_data/DNA_Contig2.fasta \\ ../Data/Clover_Data/Hifi_reads_white_clover.fastq [M::mm_idx_gen::0.048*1.04] collected minimizers [M::mm_idx_gen::0.069*1.61] sorted minimizers [M::main::0.069*1.61] loaded/built the index for 1 target sequence(s) [M::mm_mapopt_update::0.076*1.55] mid_occ = 10 [M::mm_idx_stat] kmer size: 19; skip: 10; is_hpc: 1; #seq: 1 [M::mm_idx_stat::0.080*1.53] distinct minimizers: 120722 (93.18% are singletons); average occurrences: 1.087; average spacing: 8.062; total length: 1057923 [M::worker_pipeline::27.150*2.94] mapped 4395 sequences [M::main] Version: 2.24-r1122 [M::main] CMD: minimap2 -a -x map-pb -o results/HIFI_alignment/PacBio_clover_alignment_2.sam reference_data/DNA_Contig2.fasta ../Data/Clover_Data/Hifi_reads_white_clover.fastq [M::main] Real time: 27.162 sec; CPU: 79.819 sec; Peak RSS: 2.058 GB Sort the bam files and create their index using samtools In [12]: Copied! %% bash samtools sort results / HIFI_alignment / PacBio_clover_alignment_1 . sam - o results / HIFI_alignment / PacBio_clover_alignment_1 . sort . bam samtools sort results / HIFI_alignment / PacBio_clover_alignment_2 . sam - o results / HIFI_alignment / PacBio_clover_alignment_2 . sort . bam %%bash samtools sort results/HIFI_alignment/PacBio_clover_alignment_1.sam -o results/HIFI_alignment/PacBio_clover_alignment_1.sort.bam samtools sort results/HIFI_alignment/PacBio_clover_alignment_2.sam -o results/HIFI_alignment/PacBio_clover_alignment_2.sort.bam In [13]: Copied! %% bash samtools index results / HIFI_alignment / PacBio_clover_alignment_1 . sort . bam samtools index results / HIFI_alignment / PacBio_clover_alignment_2 . sort . bam %%bash samtools index results/HIFI_alignment/PacBio_clover_alignment_1.sort.bam samtools index results/HIFI_alignment/PacBio_clover_alignment_2.sort.bam Perform quality control In [14]: Copied! %% bash mkdir - p results / qualimap_output qualimap bamqc - bam results / HIFI_alignment / PacBio_clover_alignment_1 . sort . bam - outdir results / qualimap_output / PacBio_clover_alignment_1 %%bash mkdir -p results/qualimap_output qualimap bamqc -bam results/HIFI_alignment/PacBio_clover_alignment_1.sort.bam -outdir results/qualimap_output/PacBio_clover_alignment_1 Java memory size is set to 1200M Launching application... QualiMap v.2.2.2-dev Built on 2019-11-11 14:05 Selected tool: bamqc Available memory (Mb): 33 Max memory (Mb): 1258 Starting bam qc.... Loading sam header... Loading locator... Loading reference... Number of windows: 400, effective number of windows: 400 Chunk of reads size: 1000 Number of threads: 8 Processed 50 out of 400 windows... Processed 100 out of 400 windows... Processed 150 out of 400 windows... Processed 200 out of 400 windows... Processed 250 out of 400 windows... Processed 300 out of 400 windows... Processed 350 out of 400 windows... Processed 400 out of 400 windows... Total processed windows:400 Number of reads: 4395 Number of valid reads: 9070 Number of correct strand reads:0 Inside of regions... Num mapped reads: 4356 Num mapped first of pair: 0 Num mapped second of pair: 0 Num singletons: 0 Time taken to analyze reads: 10 Computing descriptors... numberOfMappedBases: 59058058 referenceSize: 1031631 numberOfSequencedBases: 54917933 numberOfAs: 18508479 Computing per chromosome statistics... Computing histograms... Overall analysis time: 11 end of bam qc Computing report... Writing HTML report... HTML report created successfully Finished In [15]: Copied! %% bash qualimap bamqc - bam results / HIFI_alignment / PacBio_clover_alignment_2 . sort . bam - outdir results / qualimap_output / PacBio_clover_alignment_2 %%bash qualimap bamqc -bam results/HIFI_alignment/PacBio_clover_alignment_2.sort.bam -outdir results/qualimap_output/PacBio_clover_alignment_2 Java memory size is set to 1200M Launching application... QualiMap v.2.2.2-dev Built on 2019-11-11 14:05 Selected tool: bamqc Available memory (Mb): 33 Max memory (Mb): 1258 Starting bam qc.... Loading sam header... Loading locator... Loading reference... Number of windows: 400, effective number of windows: 400 Chunk of reads size: 1000 Number of threads: 8 Processed 50 out of 400 windows... Processed 100 out of 400 windows... Processed 150 out of 400 windows... Processed 200 out of 400 windows... Processed 250 out of 400 windows... Processed 300 out of 400 windows... Processed 350 out of 400 windows... Processed 400 out of 400 windows... Total processed windows:400 Number of reads: 4395 Number of valid reads: 9462 Number of correct strand reads:0 Inside of regions... Num mapped reads: 4394 Num mapped first of pair: 0 Num mapped second of pair: 0 Num singletons: 0 Time taken to analyze reads: 13 Computing descriptors... numberOfMappedBases: 59388453 referenceSize: 1057923 numberOfSequencedBases: 55867300 numberOfAs: 18822184 Computing per chromosome statistics... Computing histograms... Overall analysis time: 13 end of bam qc Computing report... Writing HTML report... HTML report created successfully Finished In [16]: Copied! %% bash #run multiqc multiqc -- outdir results / qualimap_output results / qualimap_output %%bash #run multiqc multiqc --outdir results/qualimap_output results/qualimap_output / / / \u001b]8;id=999624;https://multiqc.info\u001b\\ MultiQC \u001b]8;;\u001b\\ \ud83d\udd0d | v1.14 | multiqc | Search path : /work/SamueleSoraggi/Notebooks/results/qualimap_output | searching | \u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501 100% 187/187 .t\u2026 | qualimap | Found 4 BamQC reports | multiqc | Compressing plot data | multiqc | Previous MultiQC output found! Adjusting filenames.. | multiqc | Use -f or --force to overwrite existing reports instead | multiqc | Report : results/qualimap_output/multiqc_report_1.html | multiqc | Data : results/qualimap_output/multiqc_data_1 | multiqc | MultiQC complete Task: IGV visualization and Questions Now you can inspect the alignment files in IGV . First, you will need to download the reference fasta sequence in ../Data/Clover_Data/DNA_Contig1_2.fasta and import it into IGV. You can do the same for the files DNA_Contig1.fasta and DNA_Contig2.fasta that you might need later. In IGV, this is done with the menu Genomes --> Load Genome from file menu and by selecting the relevant fasta file. Then, choose the reference you need from the drop-down menu (see figure below). You will not yet see much, but you can choose one of the two subgenomes (contig 1 or 2) and double click on a chromosome position to inspect the reference sequence. The next step will visualize the mapped files on IGV. Each mapped genome can be seen in IGV against the reference file of choice. To load an aligned file, first download it together with the index file in .bai format. For example, you need to download both results/HIFI_alignment/PacBio_clover_alignment_1.sort.bam and results/HIFI_alignment/PacBio_clover_alignment_1.sort.bam.bai to see this alignment (you need to open only the .bam file with IGV). If you open more files, their alignments will be distributed in the IGV interface, and you can change the size of each visualization yourself (below shown with only one opened alignment). Now compare in IGV the two bam files PacBio_clover_alignment_1.sort.bam and PacBio_clover_alignment_2.sort.bam . What do you observe when comparing the two BAM files? Have a look at the polymorphic regions in IGV. Are they true polymorphisms? Add to the visualization the third alignment PacBio_clover_alignment_1_2_mappb.sort.bam in IGV. Why do you see fluctuations in coverage and large regions without any apparent subgenome SNPs? What are the major differences between the stats for the reads mapped to Contigs1&2 versus contig1 and contig2? What is your interpretation of the differences?","title":"Hifi data mapping"},{"location":"nb/align.html#rna-seq-mapping","text":"In the ../Data folder you will find 24 RNA-seq libraries, 12 S10 libraries and 12 Tienshan libraries. Each library is paired-end, which is denoted by R1 and R2 at the end of two files having the same name, such as S10_1_1.R1.fastq and S10_1_1.R2.fastq . We will align each library separately and then merge the alignments to create two final samples for S10 and Tienshan . First, we need to create a genome file for the reference fasta file of contigs 1 and 2 . This is done with STAR , using the option --runMode genomeGenerate . We also need to convert the gene annotation from gff to gtf format with gffread to allow counting gene transcripts. STAR is a very complex tool with many options, so it is always useful to have a reference manual . In [17]: Copied! %% bash gffread - T - o reference_data / white_clover_genes . gtf ../ Data / Clover_Data / white_clover_genes . gff %%bash gffread -T -o reference_data/white_clover_genes.gtf ../Data/Clover_Data/white_clover_genes.gff In [18]: Copied! %% bash STAR -- runThreadN 8 \\ -- runMode genomeGenerate \\ -- genomeDir results / STAR_output / indexing_contigs_1_2 \\ -- genomeFastaFiles reference_data / DNA_Contig1_2 . fasta \\ -- sjdbGTFfile reference_data / white_clover_genes . gtf %%bash STAR --runThreadN 8 \\ --runMode genomeGenerate \\ --genomeDir results/STAR_output/indexing_contigs_1_2 \\ --genomeFastaFiles reference_data/DNA_Contig1_2.fasta \\ --sjdbGTFfile reference_data/white_clover_genes.gtf STAR --runThreadN 8 --runMode genomeGenerate --genomeDir results/STAR_output/indexing_contigs_1_2 --genomeFastaFiles reference_data/DNA_Contig1_2.fasta --sjdbGTFfile reference_data/white_clover_genes.gtf STAR version: 2.7.10a compiled: 2022-01-14T18:50:00-05:00 :/home/dobin/data/STAR/STARcode/STAR.master/source Jun 19 10:23:03 ..... started STAR run Jun 19 10:23:03 ... starting to generate Genome files Jun 19 10:23:03 ..... processing annotations GTF !!!!! WARNING: --genomeSAindexNbases 14 is too large for the genome size=2089554, which may cause seg-fault at the mapping step. Re-run genome generation with recommended --genomeSAindexNbases 9 Jun 19 10:23:03 ... starting to sort Suffix Array. This may take a long time... Jun 19 10:23:03 ... sorting Suffix Array chunks and saving them to disk... Jun 19 10:23:04 ... loading chunks from disk, packing SA... Jun 19 10:23:04 ... finished generating suffix array Jun 19 10:23:04 ... generating Suffix Array index Jun 19 10:23:08 ... completed Suffix Array index Jun 19 10:23:08 ..... inserting junctions into the genome indices Jun 19 10:23:16 ... writing Genome to disk ... Jun 19 10:23:16 ... writing Suffix Array to disk ... Jun 19 10:23:17 ... writing SAindex to disk Jun 19 10:23:18 ..... finished successfully We got a warning saying !!!!! WARNING: --genomeSAindexNbases 14 is too large for the genome size=2089554, which may cause seg-fault at the mapping step. Re-run genome generation with recommended --genomeSAindexNbases 9 meaning we need shorter strings of bases (9 bases instead of 14) to be indexed, as our reference genome is very short, and too long strings would cause many alignment errors. So we rerun the command with the suggested option. In [19]: Copied! %% bash STAR -- runThreadN 8 \\ -- runMode genomeGenerate \\ -- genomeDir results / STAR_output / indexing_contigs_1_2 \\ -- genomeFastaFiles reference_data / DNA_Contig1_2 . fasta \\ -- sjdbGTFfile reference_data / white_clover_genes . gtf \\ -- genomeSAindexNbases 9 %%bash STAR --runThreadN 8 \\ --runMode genomeGenerate \\ --genomeDir results/STAR_output/indexing_contigs_1_2 \\ --genomeFastaFiles reference_data/DNA_Contig1_2.fasta \\ --sjdbGTFfile reference_data/white_clover_genes.gtf \\ --genomeSAindexNbases 9 STAR --runThreadN 8 --runMode genomeGenerate --genomeDir results/STAR_output/indexing_contigs_1_2 --genomeFastaFiles reference_data/DNA_Contig1_2.fasta --sjdbGTFfile reference_data/white_clover_genes.gtf --genomeSAindexNbases 9 STAR version: 2.7.10a compiled: 2022-01-14T18:50:00-05:00 :/home/dobin/data/STAR/STARcode/STAR.master/source Jun 19 10:23:18 ..... started STAR run Jun 19 10:23:18 ... starting to generate Genome files Jun 19 10:23:18 ..... processing annotations GTF Jun 19 10:23:20 ... starting to sort Suffix Array. This may take a long time... Jun 19 10:23:20 ... sorting Suffix Array chunks and saving them to disk... Jun 19 10:23:21 ... loading chunks from disk, packing SA... Jun 19 10:23:21 ... finished generating suffix array Jun 19 10:23:21 ... generating Suffix Array index Jun 19 10:23:21 ... completed Suffix Array index Jun 19 10:23:22 ..... inserting junctions into the genome indices Jun 19 10:23:23 ... writing Genome to disk ... Jun 19 10:23:23 ... writing Suffix Array to disk ... Jun 19 10:23:23 ... writing SAindex to disk Jun 19 10:23:24 ..... finished successfully We use again STAR to align every single library for S10 . We extract the library name of each file and run STAR through each pair of files. Note that plant introns are very rarely more than 5000 bp and that you are mapping to two homoeologous contigs that show high similarity, especially in genic regions. We set the maximum size to 5000 using --alignIntronMax 5000 . In [20]: Copied! %% bash for i in ` ls ../ Data / Clover_Data / S10 *. R1 . fastq ` do PREFIXNAME = ` basename $ i . R1 . fastq ` echo \"###############################################\" echo \"##### ALIGNING PAIRED-END READS \" $ PREFIXNAME echo \"###############################################\" STAR -- genomeDir results / STAR_output / indexing_contigs_1_2 / \\ -- runThreadN 8 \\ -- runMode alignReads \\ -- readFilesIn ../ Data / Clover_Data / $ PREFIXNAME . R1 . fastq ../ Data / Clover_Data / $ PREFIXNAME . R2 . fastq \\ -- outFileNamePrefix results / STAR_output / S10_align_contigs_1_2 / $ PREFIXNAME \\ -- outSAMtype BAM SortedByCoordinate \\ -- outSAMattributes Standard \\ -- quantMode GeneCounts \\ -- alignIntronMax 5000 done %%bash for i in `ls ../Data/Clover_Data/S10*.R1.fastq` do PREFIXNAME=`basename $i .R1.fastq` echo \"###############################################\" echo \"##### ALIGNING PAIRED-END READS \"$PREFIXNAME echo \"###############################################\" STAR --genomeDir results/STAR_output/indexing_contigs_1_2/ \\ --runThreadN 8 \\ --runMode alignReads \\ --readFilesIn ../Data/Clover_Data/$PREFIXNAME.R1.fastq ../Data/Clover_Data/$PREFIXNAME.R2.fastq \\ --outFileNamePrefix results/STAR_output/S10_align_contigs_1_2/$PREFIXNAME \\ --outSAMtype BAM SortedByCoordinate \\ --outSAMattributes Standard \\ --quantMode GeneCounts \\ --alignIntronMax 5000 done ############################################### ##### ALIGNING PAIRED-END READS S10_1_1 ############################################### STAR --genomeDir results/STAR_output/indexing_contigs_1_2/ --runThreadN 8 --runMode alignReads --readFilesIn ../Data/Clover_Data/S10_1_1.R1.fastq ../Data/Clover_Data/S10_1_1.R2.fastq --outFileNamePrefix results/STAR_output/S10_align_contigs_1_2/S10_1_1 --outSAMtype BAM SortedByCoordinate --outSAMattributes Standard --quantMode GeneCounts --alignIntronMax 5000 STAR version: 2.7.10a compiled: 2022-01-14T18:50:00-05:00 :/home/dobin/data/STAR/STARcode/STAR.master/source Jun 19 10:23:24 ..... started STAR run Jun 19 10:23:24 ..... loading genome Jun 19 10:23:24 ..... started mapping Jun 19 10:23:35 ..... finished mapping Jun 19 10:23:35 ..... started sorting BAM Jun 19 10:23:37 ..... finished successfully ############################################### ##### ALIGNING PAIRED-END READS S10_1_2 ############################################### STAR --genomeDir results/STAR_output/indexing_contigs_1_2/ --runThreadN 8 --runMode alignReads --readFilesIn ../Data/Clover_Data/S10_1_2.R1.fastq ../Data/Clover_Data/S10_1_2.R2.fastq --outFileNamePrefix results/STAR_output/S10_align_contigs_1_2/S10_1_2 --outSAMtype BAM SortedByCoordinate --outSAMattributes Standard --quantMode GeneCounts --alignIntronMax 5000 STAR version: 2.7.10a compiled: 2022-01-14T18:50:00-05:00 :/home/dobin/data/STAR/STARcode/STAR.master/source Jun 19 10:23:38 ..... started STAR run Jun 19 10:23:38 ..... loading genome Jun 19 10:23:38 ..... started mapping Jun 19 10:23:48 ..... finished mapping Jun 19 10:23:48 ..... started sorting BAM Jun 19 10:23:51 ..... finished successfully ############################################### ##### ALIGNING PAIRED-END READS S10_1_3 ############################################### STAR --genomeDir results/STAR_output/indexing_contigs_1_2/ --runThreadN 8 --runMode alignReads --readFilesIn ../Data/Clover_Data/S10_1_3.R1.fastq ../Data/Clover_Data/S10_1_3.R2.fastq --outFileNamePrefix results/STAR_output/S10_align_contigs_1_2/S10_1_3 --outSAMtype BAM SortedByCoordinate --outSAMattributes Standard --quantMode GeneCounts --alignIntronMax 5000 STAR version: 2.7.10a compiled: 2022-01-14T18:50:00-05:00 :/home/dobin/data/STAR/STARcode/STAR.master/source Jun 19 10:23:52 ..... started STAR run Jun 19 10:23:52 ..... loading genome Jun 19 10:23:52 ..... started mapping Jun 19 10:24:03 ..... finished mapping Jun 19 10:24:03 ..... started sorting BAM Jun 19 10:24:06 ..... finished successfully ############################################### ##### ALIGNING PAIRED-END READS S10_2_1 ############################################### STAR --genomeDir results/STAR_output/indexing_contigs_1_2/ --runThreadN 8 --runMode alignReads --readFilesIn ../Data/Clover_Data/S10_2_1.R1.fastq ../Data/Clover_Data/S10_2_1.R2.fastq --outFileNamePrefix results/STAR_output/S10_align_contigs_1_2/S10_2_1 --outSAMtype BAM SortedByCoordinate --outSAMattributes Standard --quantMode GeneCounts --alignIntronMax 5000 STAR version: 2.7.10a compiled: 2022-01-14T18:50:00-05:00 :/home/dobin/data/STAR/STARcode/STAR.master/source Jun 19 10:24:06 ..... started STAR run Jun 19 10:24:06 ..... loading genome Jun 19 10:24:06 ..... started mapping Jun 19 10:24:16 ..... finished mapping Jun 19 10:24:16 ..... started sorting BAM Jun 19 10:24:18 ..... finished successfully ############################################### ##### ALIGNING PAIRED-END READS S10_2_2 ############################################### STAR --genomeDir results/STAR_output/indexing_contigs_1_2/ --runThreadN 8 --runMode alignReads --readFilesIn ../Data/Clover_Data/S10_2_2.R1.fastq ../Data/Clover_Data/S10_2_2.R2.fastq --outFileNamePrefix results/STAR_output/S10_align_contigs_1_2/S10_2_2 --outSAMtype BAM SortedByCoordinate --outSAMattributes Standard --quantMode GeneCounts --alignIntronMax 5000 STAR version: 2.7.10a compiled: 2022-01-14T18:50:00-05:00 :/home/dobin/data/STAR/STARcode/STAR.master/source Jun 19 10:24:20 ..... started STAR run Jun 19 10:24:20 ..... loading genome Jun 19 10:24:20 ..... started mapping Jun 19 10:24:31 ..... finished mapping Jun 19 10:24:31 ..... started sorting BAM Jun 19 10:24:33 ..... finished successfully ############################################### ##### ALIGNING PAIRED-END READS S10_2_3 ############################################### STAR --genomeDir results/STAR_output/indexing_contigs_1_2/ --runThreadN 8 --runMode alignReads --readFilesIn ../Data/Clover_Data/S10_2_3.R1.fastq ../Data/Clover_Data/S10_2_3.R2.fastq --outFileNamePrefix results/STAR_output/S10_align_contigs_1_2/S10_2_3 --outSAMtype BAM SortedByCoordinate --outSAMattributes Standard --quantMode GeneCounts --alignIntronMax 5000 STAR version: 2.7.10a compiled: 2022-01-14T18:50:00-05:00 :/home/dobin/data/STAR/STARcode/STAR.master/source Jun 19 10:24:33 ..... started STAR run Jun 19 10:24:33 ..... loading genome Jun 19 10:24:33 ..... started mapping Jun 19 10:24:45 ..... finished mapping Jun 19 10:24:45 ..... started sorting BAM Jun 19 10:24:48 ..... finished successfully Do the same alignment for Tienshan libraries In [21]: Copied! %% bash for i in ` ls ../ Data / Clover_Data / TI *. R1 . fastq ` do PREFIXNAME = ` basename $ i . R1 . fastq ` echo \"###############################################\" echo \"##### ALIGNING PAIRED-END READS \" $ PREFIXNAME echo \"###############################################\" STAR -- genomeDir results / STAR_output / indexing_contigs_1_2 / \\ -- runThreadN 8 \\ -- readFilesIn ../ Data / Clover_Data / $ PREFIXNAME . R1 . fastq ../ Data / Clover_Data / $ PREFIXNAME . R2 . fastq \\ -- outFileNamePrefix results / STAR_output / TI_align_contigs_1_2 / $ PREFIXNAME \\ -- outSAMtype BAM SortedByCoordinate \\ -- outSAMattributes Standard \\ -- quantMode GeneCounts \\ -- alignIntronMax 5000 done %%bash for i in `ls ../Data/Clover_Data/TI*.R1.fastq` do PREFIXNAME=`basename $i .R1.fastq` echo \"###############################################\" echo \"##### ALIGNING PAIRED-END READS \"$PREFIXNAME echo \"###############################################\" STAR --genomeDir results/STAR_output/indexing_contigs_1_2/ \\ --runThreadN 8 \\ --readFilesIn ../Data/Clover_Data/$PREFIXNAME.R1.fastq ../Data/Clover_Data/$PREFIXNAME.R2.fastq \\ --outFileNamePrefix results/STAR_output/TI_align_contigs_1_2/$PREFIXNAME \\ --outSAMtype BAM SortedByCoordinate \\ --outSAMattributes Standard \\ --quantMode GeneCounts \\ --alignIntronMax 5000 done ############################################### ##### ALIGNING PAIRED-END READS TI_1_1 ############################################### STAR --genomeDir results/STAR_output/indexing_contigs_1_2/ --runThreadN 8 --readFilesIn ../Data/Clover_Data/TI_1_1.R1.fastq ../Data/Clover_Data/TI_1_1.R2.fastq --outFileNamePrefix results/STAR_output/TI_align_contigs_1_2/TI_1_1 --outSAMtype BAM SortedByCoordinate --outSAMattributes Standard --quantMode GeneCounts --alignIntronMax 5000 STAR version: 2.7.10a compiled: 2022-01-14T18:50:00-05:00 :/home/dobin/data/STAR/STARcode/STAR.master/source Jun 19 10:24:50 ..... started STAR run Jun 19 10:24:50 ..... loading genome Jun 19 10:24:50 ..... started mapping Jun 19 10:25:03 ..... finished mapping Jun 19 10:25:04 ..... started sorting BAM Jun 19 10:25:18 ..... finished successfully ############################################### ##### ALIGNING PAIRED-END READS TI_1_2 ############################################### STAR --genomeDir results/STAR_output/indexing_contigs_1_2/ --runThreadN 8 --readFilesIn ../Data/Clover_Data/TI_1_2.R1.fastq ../Data/Clover_Data/TI_1_2.R2.fastq --outFileNamePrefix results/STAR_output/TI_align_contigs_1_2/TI_1_2 --outSAMtype BAM SortedByCoordinate --outSAMattributes Standard --quantMode GeneCounts --alignIntronMax 5000 STAR version: 2.7.10a compiled: 2022-01-14T18:50:00-05:00 :/home/dobin/data/STAR/STARcode/STAR.master/source Jun 19 10:25:18 ..... started STAR run Jun 19 10:25:18 ..... loading genome Jun 19 10:25:18 ..... started mapping Jun 19 10:25:30 ..... finished mapping Jun 19 10:25:30 ..... started sorting BAM Jun 19 10:25:33 ..... finished successfully ############################################### ##### ALIGNING PAIRED-END READS TI_1_3 ############################################### STAR --genomeDir results/STAR_output/indexing_contigs_1_2/ --runThreadN 8 --readFilesIn ../Data/Clover_Data/TI_1_3.R1.fastq ../Data/Clover_Data/TI_1_3.R2.fastq --outFileNamePrefix results/STAR_output/TI_align_contigs_1_2/TI_1_3 --outSAMtype BAM SortedByCoordinate --outSAMattributes Standard --quantMode GeneCounts --alignIntronMax 5000 STAR version: 2.7.10a compiled: 2022-01-14T18:50:00-05:00 :/home/dobin/data/STAR/STARcode/STAR.master/source Jun 19 10:25:36 ..... started STAR run Jun 19 10:25:36 ..... loading genome Jun 19 10:25:36 ..... started mapping Jun 19 10:25:50 ..... finished mapping Jun 19 10:25:50 ..... started sorting BAM Jun 19 10:25:51 ..... finished successfully ############################################### ##### ALIGNING PAIRED-END READS TI_2_1 ############################################### STAR --genomeDir results/STAR_output/indexing_contigs_1_2/ --runThreadN 8 --readFilesIn ../Data/Clover_Data/TI_2_1.R1.fastq ../Data/Clover_Data/TI_2_1.R2.fastq --outFileNamePrefix results/STAR_output/TI_align_contigs_1_2/TI_2_1 --outSAMtype BAM SortedByCoordinate --outSAMattributes Standard --quantMode GeneCounts --alignIntronMax 5000 STAR version: 2.7.10a compiled: 2022-01-14T18:50:00-05:00 :/home/dobin/data/STAR/STARcode/STAR.master/source Jun 19 10:25:51 ..... started STAR run Jun 19 10:25:51 ..... loading genome Jun 19 10:25:52 ..... started mapping Jun 19 10:26:06 ..... finished mapping Jun 19 10:26:06 ..... started sorting BAM Jun 19 10:26:09 ..... finished successfully ############################################### ##### ALIGNING PAIRED-END READS TI_2_2 ############################################### STAR --genomeDir results/STAR_output/indexing_contigs_1_2/ --runThreadN 8 --readFilesIn ../Data/Clover_Data/TI_2_2.R1.fastq ../Data/Clover_Data/TI_2_2.R2.fastq --outFileNamePrefix results/STAR_output/TI_align_contigs_1_2/TI_2_2 --outSAMtype BAM SortedByCoordinate --outSAMattributes Standard --quantMode GeneCounts --alignIntronMax 5000 STAR version: 2.7.10a compiled: 2022-01-14T18:50:00-05:00 :/home/dobin/data/STAR/STARcode/STAR.master/source Jun 19 10:26:09 ..... started STAR run Jun 19 10:26:09 ..... loading genome Jun 19 10:26:09 ..... started mapping Jun 19 10:26:23 ..... finished mapping Jun 19 10:26:23 ..... started sorting BAM Jun 19 10:26:31 ..... finished successfully ############################################### ##### ALIGNING PAIRED-END READS TI_2_3 ############################################### STAR --genomeDir results/STAR_output/indexing_contigs_1_2/ --runThreadN 8 --readFilesIn ../Data/Clover_Data/TI_2_3.R1.fastq ../Data/Clover_Data/TI_2_3.R2.fastq --outFileNamePrefix results/STAR_output/TI_align_contigs_1_2/TI_2_3 --outSAMtype BAM SortedByCoordinate --outSAMattributes Standard --quantMode GeneCounts --alignIntronMax 5000 STAR version: 2.7.10a compiled: 2022-01-14T18:50:00-05:00 :/home/dobin/data/STAR/STARcode/STAR.master/source Jun 19 10:26:31 ..... started STAR run Jun 19 10:26:31 ..... loading genome Jun 19 10:26:31 ..... started mapping Jun 19 10:26:46 ..... finished mapping Jun 19 10:26:46 ..... started sorting BAM Jun 19 10:26:49 ..... finished successfully Run quality control on each aligned library with MultiQC . In this way there will be a whole report to compare S10 files and Tienshan files. In [22]: Copied! %% bash multiqc -- outdir results / multiqc_output / TI_STAR_align_1_2 \\ results / STAR_output / TI_align_contigs_1_2 / %%bash multiqc --outdir results/multiqc_output/TI_STAR_align_1_2 \\ results/STAR_output/TI_align_contigs_1_2/ / / / \u001b]8;id=121575;https://multiqc.info\u001b\\ MultiQC \u001b]8;;\u001b\\ \ud83d\udd0d | v1.14 | multiqc | Search path : /work/SamueleSoraggi/Notebooks/results/STAR_output/TI_align_contigs_1_2 | searching | \u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501 100% 36/36 og.o\u2026 | star | Found 6 reports and 6 gene count files | multiqc | Compressing plot data | multiqc | Report : results/multiqc_output/TI_STAR_align_1_2/multiqc_report.html | multiqc | Data : results/multiqc_output/TI_STAR_align_1_2/multiqc_data | multiqc | MultiQC complete In [23]: Copied! %% bash multiqc -- outdir results / multiqc_output / S10_STAR_align_1_2 \\ results / STAR_output / S10_align_contigs_1_2 / %%bash multiqc --outdir results/multiqc_output/S10_STAR_align_1_2 \\ results/STAR_output/S10_align_contigs_1_2/ / / / \u001b]8;id=596946;https://multiqc.info\u001b\\ MultiQC \u001b]8;;\u001b\\ \ud83d\udd0d | v1.14 | multiqc | Search path : /work/SamueleSoraggi/Notebooks/results/STAR_output/S10_align_contigs_1_2 | searching | \u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501 100% 36/36 ut.t\u2026 | star | Found 6 reports and 6 gene count files | multiqc | Compressing plot data | multiqc | Report : results/multiqc_output/S10_STAR_align_1_2/multiqc_report.html | multiqc | Data : results/multiqc_output/S10_STAR_align_1_2/multiqc_data | multiqc | MultiQC complete We merge the outputs of each group of aligned libraries. Here is how the files look like for the Tienshan . In [24]: Copied! ! ls - lh results / STAR_output / TI_align_contigs_1_2 / TI_ *. sortedByCoord . out . bam !ls -lh results/STAR_output/TI_align_contigs_1_2/TI_*.sortedByCoord.out.bam -rw-r--r--. 1 ucloud users 6.6M Jun 19 10:25 results/STAR_output/TI_align_contigs_1_2/TI_1_1Aligned.sortedByCoord.out.bam -rw-r--r--. 1 ucloud users 6.0M Jun 19 10:25 results/STAR_output/TI_align_contigs_1_2/TI_1_2Aligned.sortedByCoord.out.bam -rw-r--r--. 1 ucloud users 7.1M Jun 19 10:25 results/STAR_output/TI_align_contigs_1_2/TI_1_3Aligned.sortedByCoord.out.bam -rw-r--r--. 1 ucloud users 9.6M Jun 19 10:26 results/STAR_output/TI_align_contigs_1_2/TI_2_1Aligned.sortedByCoord.out.bam -rw-r--r--. 1 ucloud users 7.2M Jun 19 10:26 results/STAR_output/TI_align_contigs_1_2/TI_2_2Aligned.sortedByCoord.out.bam -rw-r--r--. 1 ucloud users 8.2M Jun 19 10:26 results/STAR_output/TI_align_contigs_1_2/TI_2_3Aligned.sortedByCoord.out.bam Apply samtools merge In [25]: Copied! %% bash mkdir - p results / STAR_output / TI_align_contigs_1_2_merge / samtools merge - f results / STAR_output / TI_align_contigs_1_2_merge / TI . sorted . bam results / STAR_output / TI_align_contigs_1_2 / TI_ *. sortedByCoord . out . bam %%bash mkdir -p results/STAR_output/TI_align_contigs_1_2_merge/ samtools merge -f results/STAR_output/TI_align_contigs_1_2_merge/TI.sorted.bam results/STAR_output/TI_align_contigs_1_2/TI_*.sortedByCoord.out.bam In [26]: Copied! %% bash mkdir - p results / STAR_output / S10_align_contigs_1_2_merge / samtools merge - f results / STAR_output / S10_align_contigs_1_2_merge / S10 . sorted . bam results / STAR_output / S10_align_contigs_1_2 / S10_ *. sortedByCoord . out . bam %%bash mkdir -p results/STAR_output/S10_align_contigs_1_2_merge/ samtools merge -f results/STAR_output/S10_align_contigs_1_2_merge/S10.sorted.bam results/STAR_output/S10_align_contigs_1_2/S10_*.sortedByCoord.out.bam Index both merging outputs. A file in format bam.bai will appear in their respective folders. In [27]: Copied! %% bash samtools index results / STAR_output / S10_align_contigs_1_2_merge / S10 . sorted . bam %%bash samtools index results/STAR_output/S10_align_contigs_1_2_merge/S10.sorted.bam In [28]: Copied! %% bash samtools index results / STAR_output / TI_align_contigs_1_2_merge / TI . sorted . bam %%bash samtools index results/STAR_output/TI_align_contigs_1_2_merge/TI.sorted.bam","title":"RNA-seq mapping"},{"location":"nb/align.html#wrapping-up","text":"In this exercise, you learnt to align various types of data after performing quality control for raw data. We looked at some of the options for the aligners and at how to use some of the basic samtools manipulation programs. 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padding-left: 5px; padding-right: 5px; } td.linenos .special { color: #000000; background-color: #ffffc0; padding-left: 5px; padding-right: 5px; } span.linenos.special { color: #000000; background-color: #ffffc0; padding-left: 5px; padding-right: 5px; } .highlight-ipynb .hll { background-color: var(--jp-cell-editor-active-background) } .highlight-ipynb { background: var(--jp-cell-editor-background); color: var(--jp-mirror-editor-variable-color) } .highlight-ipynb .c { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment */ .highlight-ipynb .err { color: var(--jp-mirror-editor-error-color) } /* Error */ .highlight-ipynb .k { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword */ .highlight-ipynb .o { color: var(--jp-mirror-editor-operator-color); font-weight: bold } /* Operator */ .highlight-ipynb .p { color: var(--jp-mirror-editor-punctuation-color) } /* Punctuation */ .highlight-ipynb .ch { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.Hashbang */ .highlight-ipynb .cm { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.Multiline */ .highlight-ipynb .cp { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.Preproc */ .highlight-ipynb .cpf { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.PreprocFile */ .highlight-ipynb .c1 { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.Single */ .highlight-ipynb .cs { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.Special */ .highlight-ipynb .kc { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Constant */ .highlight-ipynb .kd { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Declaration */ .highlight-ipynb .kn { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Namespace */ .highlight-ipynb .kp { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Pseudo */ .highlight-ipynb .kr { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Reserved */ .highlight-ipynb .kt { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Type */ .highlight-ipynb .m { color: var(--jp-mirror-editor-number-color) } /* Literal.Number */ .highlight-ipynb .s { color: var(--jp-mirror-editor-string-color) } /* Literal.String */ .highlight-ipynb .ow { color: var(--jp-mirror-editor-operator-color); font-weight: bold } /* Operator.Word */ .highlight-ipynb .w { color: var(--jp-mirror-editor-variable-color) } /* Text.Whitespace */ .highlight-ipynb .mb { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Bin */ .highlight-ipynb .mf { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Float */ .highlight-ipynb .mh { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Hex */ .highlight-ipynb .mi { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Integer */ .highlight-ipynb .mo { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Oct */ .highlight-ipynb .sa { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Affix */ .highlight-ipynb .sb { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Backtick */ .highlight-ipynb .sc { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Char */ .highlight-ipynb .dl { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Delimiter */ .highlight-ipynb .sd { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Doc */ .highlight-ipynb .s2 { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Double */ .highlight-ipynb .se { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Escape */ .highlight-ipynb .sh { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Heredoc */ .highlight-ipynb .si { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Interpol */ .highlight-ipynb .sx { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Other */ .highlight-ipynb .sr { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Regex */ .highlight-ipynb .s1 { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Single */ .highlight-ipynb .ss { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Symbol */ .highlight-ipynb .il { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Integer.Long */ /* This file is taken from the built JupyterLab theme.css Found on share/nbconvert/templates/lab/static Some changes have been made and marked with CHANGE */ .jupyter-wrapper { /* Elevation * * We style box-shadows using Material Design's idea of elevation. These particular numbers are taken from here: * * https://github.com/material-components/material-components-web * https://material-components-web.appspot.com/elevation.html */ --jp-shadow-base-lightness: 0; --jp-shadow-umbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.2 ); --jp-shadow-penumbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.14 ); --jp-shadow-ambient-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.12 ); --jp-elevation-z0: none; --jp-elevation-z1: 0px 2px 1px -1px var(--jp-shadow-umbra-color), 0px 1px 1px 0px var(--jp-shadow-penumbra-color), 0px 1px 3px 0px var(--jp-shadow-ambient-color); --jp-elevation-z2: 0px 3px 1px -2px var(--jp-shadow-umbra-color), 0px 2px 2px 0px var(--jp-shadow-penumbra-color), 0px 1px 5px 0px var(--jp-shadow-ambient-color); --jp-elevation-z4: 0px 2px 4px -1px var(--jp-shadow-umbra-color), 0px 4px 5px 0px var(--jp-shadow-penumbra-color), 0px 1px 10px 0px var(--jp-shadow-ambient-color); --jp-elevation-z6: 0px 3px 5px -1px var(--jp-shadow-umbra-color), 0px 6px 10px 0px var(--jp-shadow-penumbra-color), 0px 1px 18px 0px var(--jp-shadow-ambient-color); --jp-elevation-z8: 0px 5px 5px -3px var(--jp-shadow-umbra-color), 0px 8px 10px 1px var(--jp-shadow-penumbra-color), 0px 3px 14px 2px var(--jp-shadow-ambient-color); --jp-elevation-z12: 0px 7px 8px -4px var(--jp-shadow-umbra-color), 0px 12px 17px 2px var(--jp-shadow-penumbra-color), 0px 5px 22px 4px var(--jp-shadow-ambient-color); --jp-elevation-z16: 0px 8px 10px -5px var(--jp-shadow-umbra-color), 0px 16px 24px 2px var(--jp-shadow-penumbra-color), 0px 6px 30px 5px var(--jp-shadow-ambient-color); --jp-elevation-z20: 0px 10px 13px -6px var(--jp-shadow-umbra-color), 0px 20px 31px 3px var(--jp-shadow-penumbra-color), 0px 8px 38px 7px var(--jp-shadow-ambient-color); --jp-elevation-z24: 0px 11px 15px -7px var(--jp-shadow-umbra-color), 0px 24px 38px 3px var(--jp-shadow-penumbra-color), 0px 9px 46px 8px var(--jp-shadow-ambient-color); /* Borders * * The following variables, specify the visual styling of borders in JupyterLab. */ --jp-border-width: 1px; --jp-border-color0: var(--md-grey-400); --jp-border-color1: var(--md-grey-400); --jp-border-color2: var(--md-grey-300); --jp-border-color3: var(--md-grey-200); --jp-border-radius: 2px; /* UI Fonts * * The UI font CSS variables are used for the typography all of the JupyterLab * user interface elements that are not directly user generated content. * * The font sizing here is done assuming that the body font size of --jp-ui-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-ui-font-scale-factor: 1.2; --jp-ui-font-size0: 0.83333em; --jp-ui-font-size1: 13px; /* Base font size */ --jp-ui-font-size2: 1.2em; --jp-ui-font-size3: 1.44em; --jp-ui-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Use these font colors against the corresponding main layout colors. * In a light theme, these go from dark to light. */ /* Defaults use Material Design specification */ --jp-ui-font-color0: rgba(0, 0, 0, 1); --jp-ui-font-color1: rgba(0, 0, 0, 0.87); --jp-ui-font-color2: rgba(0, 0, 0, 0.54); --jp-ui-font-color3: rgba(0, 0, 0, 0.38); /* * Use these against the brand/accent/warn/error colors. * These will typically go from light to darker, in both a dark and light theme. */ --jp-ui-inverse-font-color0: rgba(255, 255, 255, 1); --jp-ui-inverse-font-color1: rgba(255, 255, 255, 1); --jp-ui-inverse-font-color2: rgba(255, 255, 255, 0.7); --jp-ui-inverse-font-color3: rgba(255, 255, 255, 0.5); /* Content Fonts * * Content font variables are used for typography of user generated content. * * The font sizing here is done assuming that the body font size of --jp-content-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-content-line-height: 1.6; --jp-content-font-scale-factor: 1.2; --jp-content-font-size0: 0.83333em; --jp-content-font-size1: 14px; /* Base font size */ --jp-content-font-size2: 1.2em; --jp-content-font-size3: 1.44em; --jp-content-font-size4: 1.728em; --jp-content-font-size5: 2.0736em; /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-content-presentation-font-size1: 17px; --jp-content-heading-line-height: 1; --jp-content-heading-margin-top: 1.2em; --jp-content-heading-margin-bottom: 0.8em; --jp-content-heading-font-weight: 500; /* Defaults use Material Design specification */ --jp-content-font-color0: rgba(0, 0, 0, 1); --jp-content-font-color1: rgba(0, 0, 0, 0.87); --jp-content-font-color2: rgba(0, 0, 0, 0.54); --jp-content-font-color3: rgba(0, 0, 0, 0.38); --jp-content-link-color: var(--md-blue-700); --jp-content-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Code Fonts * * Code font variables are used for typography of code and other monospaces content. */ --jp-code-font-size: 13px; --jp-code-line-height: 1.3077; /* 17px for 13px base */ --jp-code-padding: 5px; /* 5px for 13px base, codemirror highlighting needs integer px value */ --jp-code-font-family-default: Menlo, Consolas, \"DejaVu Sans Mono\", monospace; --jp-code-font-family: var(--jp-code-font-family-default); /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-code-presentation-font-size: 16px; /* may need to tweak cursor width if you change font size */ --jp-code-cursor-width0: 1.4px; --jp-code-cursor-width1: 2px; --jp-code-cursor-width2: 4px; /* Layout * * The following are the main layout colors use in JupyterLab. In a light * theme these would go from light to dark. */ --jp-layout-color0: white; --jp-layout-color1: white; --jp-layout-color2: var(--md-grey-200); --jp-layout-color3: var(--md-grey-400); --jp-layout-color4: var(--md-grey-600); /* Inverse Layout * * The following are the inverse layout colors use in JupyterLab. In a light * theme these would go from dark to light. */ --jp-inverse-layout-color0: #111111; --jp-inverse-layout-color1: var(--md-grey-900); --jp-inverse-layout-color2: var(--md-grey-800); --jp-inverse-layout-color3: var(--md-grey-700); --jp-inverse-layout-color4: var(--md-grey-600); /* Brand/accent */ --jp-brand-color0: var(--md-blue-900); --jp-brand-color1: var(--md-blue-700); --jp-brand-color2: var(--md-blue-300); --jp-brand-color3: var(--md-blue-100); --jp-brand-color4: var(--md-blue-50); --jp-accent-color0: var(--md-green-900); --jp-accent-color1: var(--md-green-700); --jp-accent-color2: var(--md-green-300); --jp-accent-color3: var(--md-green-100); /* State colors (warn, error, success, info) */ --jp-warn-color0: var(--md-orange-900); --jp-warn-color1: var(--md-orange-700); --jp-warn-color2: var(--md-orange-300); --jp-warn-color3: var(--md-orange-100); --jp-error-color0: var(--md-red-900); --jp-error-color1: var(--md-red-700); --jp-error-color2: var(--md-red-300); --jp-error-color3: var(--md-red-100); --jp-success-color0: var(--md-green-900); --jp-success-color1: var(--md-green-700); --jp-success-color2: var(--md-green-300); --jp-success-color3: var(--md-green-100); --jp-info-color0: var(--md-cyan-900); --jp-info-color1: var(--md-cyan-700); --jp-info-color2: var(--md-cyan-300); --jp-info-color3: var(--md-cyan-100); /* Cell specific styles */ --jp-cell-padding: 5px; --jp-cell-collapser-width: 8px; --jp-cell-collapser-min-height: 20px; --jp-cell-collapser-not-active-hover-opacity: 0.6; --jp-cell-editor-background: var(--md-grey-100); --jp-cell-editor-border-color: var(--md-grey-300); --jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-cell-editor-active-background: var(--jp-layout-color0); --jp-cell-editor-active-border-color: var(--jp-brand-color1); --jp-cell-prompt-width: 64px; --jp-cell-prompt-font-family: var(--jp-code-font-family-default); --jp-cell-prompt-letter-spacing: 0px; --jp-cell-prompt-opacity: 1; --jp-cell-prompt-not-active-opacity: 0.5; --jp-cell-prompt-not-active-font-color: var(--md-grey-700); /* A custom blend of MD grey and blue 600 * See https://meyerweb.com/eric/tools/color-blend/#546E7A:1E88E5:5:hex */ --jp-cell-inprompt-font-color: #307fc1; /* A custom blend of MD grey and orange 600 * https://meyerweb.com/eric/tools/color-blend/#546E7A:F4511E:5:hex */ --jp-cell-outprompt-font-color: #bf5b3d; /* Notebook specific styles */ --jp-notebook-padding: 10px; --jp-notebook-select-background: var(--jp-layout-color1); --jp-notebook-multiselected-color: var(--md-blue-50); /* The scroll padding is calculated to fill enough space at the bottom of the notebook to show one single-line cell (with appropriate padding) at the top when the notebook is scrolled all the way to the bottom. We also subtract one pixel so that no scrollbar appears if we have just one single-line cell in the notebook. This padding is to enable a 'scroll past end' feature in a notebook. */ --jp-notebook-scroll-padding: calc( 100% - var(--jp-code-font-size) * var(--jp-code-line-height) - var(--jp-code-padding) - var(--jp-cell-padding) - 1px ); /* Rendermime styles */ --jp-rendermime-error-background: #fdd; --jp-rendermime-table-row-background: var(--md-grey-100); --jp-rendermime-table-row-hover-background: var(--md-light-blue-50); /* Dialog specific styles */ --jp-dialog-background: rgba(0, 0, 0, 0.25); /* Console specific styles */ --jp-console-padding: 10px; /* Toolbar specific styles */ --jp-toolbar-border-color: var(--jp-border-color1); --jp-toolbar-micro-height: 8px; --jp-toolbar-background: var(--jp-layout-color1); --jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, 0.24); --jp-toolbar-header-margin: 4px 4px 0px 4px; --jp-toolbar-active-background: var(--md-grey-300); /* Statusbar specific styles */ --jp-statusbar-height: 24px; /* Input field styles */ --jp-input-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-input-active-background: var(--jp-layout-color1); --jp-input-hover-background: var(--jp-layout-color1); --jp-input-background: var(--md-grey-100); --jp-input-border-color: var(--jp-border-color1); --jp-input-active-border-color: var(--jp-brand-color1); --jp-input-active-box-shadow-color: rgba(19, 124, 189, 0.3); /* General editor styles */ --jp-editor-selected-background: #d9d9d9; --jp-editor-selected-focused-background: #d7d4f0; --jp-editor-cursor-color: var(--jp-ui-font-color0); /* Code mirror specific styles */ --jp-mirror-editor-keyword-color: #008000; --jp-mirror-editor-atom-color: #88f; --jp-mirror-editor-number-color: #080; --jp-mirror-editor-def-color: #00f; --jp-mirror-editor-variable-color: var(--md-grey-900); --jp-mirror-editor-variable-2-color: #05a; --jp-mirror-editor-variable-3-color: #085; --jp-mirror-editor-punctuation-color: #05a; --jp-mirror-editor-property-color: #05a; --jp-mirror-editor-operator-color: #aa22ff; --jp-mirror-editor-comment-color: #408080; --jp-mirror-editor-string-color: #ba2121; --jp-mirror-editor-string-2-color: #708; --jp-mirror-editor-meta-color: #aa22ff; --jp-mirror-editor-qualifier-color: #555; --jp-mirror-editor-builtin-color: #008000; --jp-mirror-editor-bracket-color: #997; --jp-mirror-editor-tag-color: #170; --jp-mirror-editor-attribute-color: #00c; --jp-mirror-editor-header-color: blue; --jp-mirror-editor-quote-color: #090; --jp-mirror-editor-link-color: #00c; --jp-mirror-editor-error-color: #f00; --jp-mirror-editor-hr-color: #999; /* Vega extension styles */ --jp-vega-background: white; /* Sidebar-related styles */ --jp-sidebar-min-width: 250px; /* Search-related styles */ --jp-search-toggle-off-opacity: 0.5; --jp-search-toggle-hover-opacity: 0.8; --jp-search-toggle-on-opacity: 1; --jp-search-selected-match-background-color: rgb(245, 200, 0); --jp-search-selected-match-color: black; --jp-search-unselected-match-background-color: var( --jp-inverse-layout-color0 ); --jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0); /* Icon colors that work well with light or dark backgrounds */ --jp-icon-contrast-color0: var(--md-purple-600); --jp-icon-contrast-color1: var(--md-green-600); --jp-icon-contrast-color2: var(--md-pink-600); --jp-icon-contrast-color3: var(--md-blue-600); } [data-md-color-scheme=\"slate\"] .jupyter-wrapper { /* Elevation * * We style box-shadows using Material Design's idea of elevation. These particular numbers are taken from here: * * https://github.com/material-components/material-components-web * https://material-components-web.appspot.com/elevation.html */ /* The dark theme shadows need a bit of work, but this will probably also require work on the core layout * colors used in the theme as well. */ --jp-shadow-base-lightness: 32; --jp-shadow-umbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.2 ); --jp-shadow-penumbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.14 ); --jp-shadow-ambient-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.12 ); --jp-elevation-z0: none; --jp-elevation-z1: 0px 2px 1px -1px var(--jp-shadow-umbra-color), 0px 1px 1px 0px var(--jp-shadow-penumbra-color), 0px 1px 3px 0px var(--jp-shadow-ambient-color); --jp-elevation-z2: 0px 3px 1px -2px var(--jp-shadow-umbra-color), 0px 2px 2px 0px var(--jp-shadow-penumbra-color), 0px 1px 5px 0px var(--jp-shadow-ambient-color); --jp-elevation-z4: 0px 2px 4px -1px var(--jp-shadow-umbra-color), 0px 4px 5px 0px var(--jp-shadow-penumbra-color), 0px 1px 10px 0px var(--jp-shadow-ambient-color); --jp-elevation-z6: 0px 3px 5px -1px var(--jp-shadow-umbra-color), 0px 6px 10px 0px var(--jp-shadow-penumbra-color), 0px 1px 18px 0px var(--jp-shadow-ambient-color); --jp-elevation-z8: 0px 5px 5px -3px var(--jp-shadow-umbra-color), 0px 8px 10px 1px var(--jp-shadow-penumbra-color), 0px 3px 14px 2px var(--jp-shadow-ambient-color); --jp-elevation-z12: 0px 7px 8px -4px var(--jp-shadow-umbra-color), 0px 12px 17px 2px var(--jp-shadow-penumbra-color), 0px 5px 22px 4px var(--jp-shadow-ambient-color); --jp-elevation-z16: 0px 8px 10px -5px var(--jp-shadow-umbra-color), 0px 16px 24px 2px var(--jp-shadow-penumbra-color), 0px 6px 30px 5px var(--jp-shadow-ambient-color); --jp-elevation-z20: 0px 10px 13px -6px var(--jp-shadow-umbra-color), 0px 20px 31px 3px var(--jp-shadow-penumbra-color), 0px 8px 38px 7px var(--jp-shadow-ambient-color); --jp-elevation-z24: 0px 11px 15px -7px var(--jp-shadow-umbra-color), 0px 24px 38px 3px var(--jp-shadow-penumbra-color), 0px 9px 46px 8px var(--jp-shadow-ambient-color); /* Borders * * The following variables, specify the visual styling of borders in JupyterLab. */ --jp-border-width: 1px; --jp-border-color0: var(--md-grey-700); --jp-border-color1: var(--md-grey-700); --jp-border-color2: var(--md-grey-800); --jp-border-color3: var(--md-grey-900); --jp-border-radius: 2px; /* UI Fonts * * The UI font CSS variables are used for the typography all of the JupyterLab * user interface elements that are not directly user generated content. * * The font sizing here is done assuming that the body font size of --jp-ui-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-ui-font-scale-factor: 1.2; --jp-ui-font-size0: 0.83333em; --jp-ui-font-size1: 13px; /* Base font size */ --jp-ui-font-size2: 1.2em; --jp-ui-font-size3: 1.44em; --jp-ui-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Use these font colors against the corresponding main layout colors. * In a light theme, these go from dark to light. */ /* Defaults use Material Design specification */ --jp-ui-font-color0: rgba(255, 255, 255, 1); --jp-ui-font-color1: rgba(255, 255, 255, 0.87); --jp-ui-font-color2: rgba(255, 255, 255, 0.54); --jp-ui-font-color3: rgba(255, 255, 255, 0.38); /* * Use these against the brand/accent/warn/error colors. * These will typically go from light to darker, in both a dark and light theme. */ --jp-ui-inverse-font-color0: rgba(0, 0, 0, 1); --jp-ui-inverse-font-color1: rgba(0, 0, 0, 0.8); --jp-ui-inverse-font-color2: rgba(0, 0, 0, 0.5); --jp-ui-inverse-font-color3: rgba(0, 0, 0, 0.3); /* Content Fonts * * Content font variables are used for typography of user generated content. * * The font sizing here is done assuming that the body font size of --jp-content-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-content-line-height: 1.6; --jp-content-font-scale-factor: 1.2; --jp-content-font-size0: 0.83333em; --jp-content-font-size1: 14px; /* Base font size */ --jp-content-font-size2: 1.2em; --jp-content-font-size3: 1.44em; --jp-content-font-size4: 1.728em; --jp-content-font-size5: 2.0736em; /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-content-presentation-font-size1: 17px; --jp-content-heading-line-height: 1; --jp-content-heading-margin-top: 1.2em; --jp-content-heading-margin-bottom: 0.8em; --jp-content-heading-font-weight: 500; /* Defaults use Material Design specification */ --jp-content-font-color0: rgba(255, 255, 255, 1); --jp-content-font-color1: rgba(255, 255, 255, 1); --jp-content-font-color2: rgba(255, 255, 255, 0.7); --jp-content-font-color3: rgba(255, 255, 255, 0.5); --jp-content-link-color: var(--md-blue-300); --jp-content-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Code Fonts * * Code font variables are used for typography of code and other monospaces content. */ --jp-code-font-size: 13px; --jp-code-line-height: 1.3077; /* 17px for 13px base */ --jp-code-padding: 5px; /* 5px for 13px base, codemirror highlighting needs integer px value */ --jp-code-font-family-default: Menlo, Consolas, \"DejaVu Sans Mono\", monospace; --jp-code-font-family: var(--jp-code-font-family-default); /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-code-presentation-font-size: 16px; /* may need to tweak cursor width if you change font size */ --jp-code-cursor-width0: 1.4px; --jp-code-cursor-width1: 2px; --jp-code-cursor-width2: 4px; /* Layout * * The following are the main layout colors use in JupyterLab. In a light * theme these would go from light to dark. */ --jp-layout-color0: #111111; --jp-layout-color1: var(--md-grey-900); --jp-layout-color2: var(--md-grey-800); --jp-layout-color3: var(--md-grey-700); --jp-layout-color4: var(--md-grey-600); /* Inverse Layout * * The following are the inverse layout colors use in JupyterLab. In a light * theme these would go from dark to light. */ --jp-inverse-layout-color0: white; --jp-inverse-layout-color1: white; --jp-inverse-layout-color2: var(--md-grey-200); --jp-inverse-layout-color3: var(--md-grey-400); --jp-inverse-layout-color4: var(--md-grey-600); /* Brand/accent */ --jp-brand-color0: var(--md-blue-700); --jp-brand-color1: var(--md-blue-500); --jp-brand-color2: var(--md-blue-300); --jp-brand-color3: var(--md-blue-100); --jp-brand-color4: var(--md-blue-50); --jp-accent-color0: var(--md-green-700); --jp-accent-color1: var(--md-green-500); --jp-accent-color2: var(--md-green-300); --jp-accent-color3: var(--md-green-100); /* State colors (warn, error, success, info) */ --jp-warn-color0: var(--md-orange-700); --jp-warn-color1: var(--md-orange-500); --jp-warn-color2: var(--md-orange-300); --jp-warn-color3: var(--md-orange-100); --jp-error-color0: var(--md-red-700); --jp-error-color1: var(--md-red-500); --jp-error-color2: var(--md-red-300); --jp-error-color3: var(--md-red-100); --jp-success-color0: var(--md-green-700); --jp-success-color1: var(--md-green-500); --jp-success-color2: var(--md-green-300); --jp-success-color3: var(--md-green-100); --jp-info-color0: var(--md-cyan-700); --jp-info-color1: var(--md-cyan-500); --jp-info-color2: var(--md-cyan-300); --jp-info-color3: var(--md-cyan-100); /* Cell specific styles */ --jp-cell-padding: 5px; --jp-cell-collapser-width: 8px; --jp-cell-collapser-min-height: 20px; --jp-cell-collapser-not-active-hover-opacity: 0.6; --jp-cell-editor-background: var(--jp-layout-color1); --jp-cell-editor-border-color: var(--md-grey-700); --jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-cell-editor-active-background: var(--jp-layout-color0); --jp-cell-editor-active-border-color: var(--jp-brand-color1); --jp-cell-prompt-width: 64px; --jp-cell-prompt-font-family: var(--jp-code-font-family-default); --jp-cell-prompt-letter-spacing: 0px; --jp-cell-prompt-opacity: 1; --jp-cell-prompt-not-active-opacity: 1; --jp-cell-prompt-not-active-font-color: var(--md-grey-300); /* A custom blend of MD grey and blue 600 * See https://meyerweb.com/eric/tools/color-blend/#546E7A:1E88E5:5:hex */ --jp-cell-inprompt-font-color: #307fc1; /* A custom blend of MD grey and orange 600 * https://meyerweb.com/eric/tools/color-blend/#546E7A:F4511E:5:hex */ --jp-cell-outprompt-font-color: #bf5b3d; /* Notebook specific styles */ --jp-notebook-padding: 10px; --jp-notebook-select-background: var(--jp-layout-color1); --jp-notebook-multiselected-color: rgba(33, 150, 243, 0.24); /* The scroll padding is calculated to fill enough space at the bottom of the notebook to show one single-line cell (with appropriate padding) at the top when the notebook is scrolled all the way to the bottom. We also subtract one pixel so that no scrollbar appears if we have just one single-line cell in the notebook. This padding is to enable a 'scroll past end' feature in a notebook. */ --jp-notebook-scroll-padding: calc( 100% - var(--jp-code-font-size) * var(--jp-code-line-height) - var(--jp-code-padding) - var(--jp-cell-padding) - 1px ); /* Rendermime styles */ --jp-rendermime-error-background: rgba(244, 67, 54, 0.28); --jp-rendermime-table-row-background: var(--md-grey-900); --jp-rendermime-table-row-hover-background: rgba(3, 169, 244, 0.2); /* Dialog specific styles */ --jp-dialog-background: rgba(0, 0, 0, 0.6); /* Console specific styles */ --jp-console-padding: 10px; /* Toolbar specific styles */ --jp-toolbar-border-color: var(--jp-border-color2); --jp-toolbar-micro-height: 8px; --jp-toolbar-background: var(--jp-layout-color1); --jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, 0.8); --jp-toolbar-header-margin: 4px 4px 0px 4px; --jp-toolbar-active-background: var(--jp-layout-color0); /* Statusbar specific styles */ --jp-statusbar-height: 24px; /* Input field styles */ --jp-input-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-input-active-background: var(--jp-layout-color0); --jp-input-hover-background: var(--jp-layout-color2); --jp-input-background: var(--md-grey-800); --jp-input-border-color: var(--jp-border-color1); --jp-input-active-border-color: var(--jp-brand-color1); --jp-input-active-box-shadow-color: rgba(19, 124, 189, 0.3); /* General editor styles */ --jp-editor-selected-background: var(--jp-layout-color2); --jp-editor-selected-focused-background: rgba(33, 150, 243, 0.24); --jp-editor-cursor-color: var(--jp-ui-font-color0); /* Code mirror specific styles */ --jp-mirror-editor-keyword-color: var(--md-green-500); --jp-mirror-editor-atom-color: var(--md-blue-300); --jp-mirror-editor-number-color: var(--md-green-400); --jp-mirror-editor-def-color: var(--md-blue-600); --jp-mirror-editor-variable-color: var(--md-grey-300); --jp-mirror-editor-variable-2-color: var(--md-blue-400); --jp-mirror-editor-variable-3-color: var(--md-green-600); --jp-mirror-editor-punctuation-color: var(--md-blue-400); --jp-mirror-editor-property-color: var(--md-blue-400); --jp-mirror-editor-operator-color: #aa22ff; --jp-mirror-editor-comment-color: #408080; --jp-mirror-editor-string-color: #ff7070; --jp-mirror-editor-string-2-color: var(--md-purple-300); --jp-mirror-editor-meta-color: #aa22ff; --jp-mirror-editor-qualifier-color: #555; --jp-mirror-editor-builtin-color: var(--md-green-600); --jp-mirror-editor-bracket-color: #997; --jp-mirror-editor-tag-color: var(--md-green-700); --jp-mirror-editor-attribute-color: var(--md-blue-700); --jp-mirror-editor-header-color: var(--md-blue-500); --jp-mirror-editor-quote-color: var(--md-green-300); --jp-mirror-editor-link-color: var(--md-blue-700); --jp-mirror-editor-error-color: #f00; --jp-mirror-editor-hr-color: #999; /* Vega extension styles */ --jp-vega-background: var(--md-grey-400); /* Sidebar-related styles */ --jp-sidebar-min-width: 250px; /* Search-related styles */ --jp-search-toggle-off-opacity: 0.6; --jp-search-toggle-hover-opacity: 0.8; --jp-search-toggle-on-opacity: 1; --jp-search-selected-match-background-color: rgb(255, 225, 0); --jp-search-selected-match-color: black; --jp-search-unselected-match-background-color: var( --jp-inverse-layout-color0 ); --jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0); /* scrollbar related styles. Supports every browser except Edge. */ /* colors based on JetBrain's Darcula theme */ --jp-scrollbar-background-color: #3f4244; --jp-scrollbar-thumb-color: 88, 96, 97; /* need to specify thumb color as an RGB triplet */ --jp-scrollbar-endpad: 3px; /* the minimum gap between the thumb and the ends of a scrollbar */ /* hacks for setting the thumb shape. These do nothing in Firefox */ --jp-scrollbar-thumb-margin: 3.5px; /* the space in between the sides of the thumb and the track */ --jp-scrollbar-thumb-radius: 9px; /* set to a large-ish value for rounded endcaps on the thumb */ /* Icon colors that work well with light or dark backgrounds */ --jp-icon-contrast-color0: var(--md-purple-600); --jp-icon-contrast-color1: var(--md-green-600); --jp-icon-contrast-color2: var(--md-pink-600); --jp-icon-contrast-color3: var(--md-blue-600); } :root{--md-red-50: #ffebee;--md-red-100: #ffcdd2;--md-red-200: #ef9a9a;--md-red-300: #e57373;--md-red-400: #ef5350;--md-red-500: #f44336;--md-red-600: #e53935;--md-red-700: #d32f2f;--md-red-800: #c62828;--md-red-900: #b71c1c;--md-red-A100: #ff8a80;--md-red-A200: #ff5252;--md-red-A400: #ff1744;--md-red-A700: #d50000;--md-pink-50: #fce4ec;--md-pink-100: #f8bbd0;--md-pink-200: #f48fb1;--md-pink-300: #f06292;--md-pink-400: #ec407a;--md-pink-500: #e91e63;--md-pink-600: #d81b60;--md-pink-700: #c2185b;--md-pink-800: #ad1457;--md-pink-900: #880e4f;--md-pink-A100: #ff80ab;--md-pink-A200: #ff4081;--md-pink-A400: #f50057;--md-pink-A700: #c51162;--md-purple-50: #f3e5f5;--md-purple-100: #e1bee7;--md-purple-200: #ce93d8;--md-purple-300: #ba68c8;--md-purple-400: #ab47bc;--md-purple-500: #9c27b0;--md-purple-600: #8e24aa;--md-purple-700: #7b1fa2;--md-purple-800: #6a1b9a;--md-purple-900: #4a148c;--md-purple-A100: #ea80fc;--md-purple-A200: #e040fb;--md-purple-A400: #d500f9;--md-purple-A700: #aa00ff;--md-deep-purple-50: #ede7f6;--md-deep-purple-100: #d1c4e9;--md-deep-purple-200: #b39ddb;--md-deep-purple-300: #9575cd;--md-deep-purple-400: #7e57c2;--md-deep-purple-500: #673ab7;--md-deep-purple-600: #5e35b1;--md-deep-purple-700: #512da8;--md-deep-purple-800: #4527a0;--md-deep-purple-900: #311b92;--md-deep-purple-A100: #b388ff;--md-deep-purple-A200: #7c4dff;--md-deep-purple-A400: #651fff;--md-deep-purple-A700: #6200ea;--md-indigo-50: #e8eaf6;--md-indigo-100: #c5cae9;--md-indigo-200: #9fa8da;--md-indigo-300: #7986cb;--md-indigo-400: #5c6bc0;--md-indigo-500: #3f51b5;--md-indigo-600: #3949ab;--md-indigo-700: #303f9f;--md-indigo-800: #283593;--md-indigo-900: #1a237e;--md-indigo-A100: #8c9eff;--md-indigo-A200: #536dfe;--md-indigo-A400: #3d5afe;--md-indigo-A700: #304ffe;--md-blue-50: #e3f2fd;--md-blue-100: #bbdefb;--md-blue-200: #90caf9;--md-blue-300: #64b5f6;--md-blue-400: #42a5f5;--md-blue-500: #2196f3;--md-blue-600: #1e88e5;--md-blue-700: #1976d2;--md-blue-800: #1565c0;--md-blue-900: #0d47a1;--md-blue-A100: #82b1ff;--md-blue-A200: #448aff;--md-blue-A400: #2979ff;--md-blue-A700: #2962ff;--md-light-blue-50: #e1f5fe;--md-light-blue-100: #b3e5fc;--md-light-blue-200: #81d4fa;--md-light-blue-300: #4fc3f7;--md-light-blue-400: #29b6f6;--md-light-blue-500: #03a9f4;--md-light-blue-600: #039be5;--md-light-blue-700: #0288d1;--md-light-blue-800: #0277bd;--md-light-blue-900: #01579b;--md-light-blue-A100: #80d8ff;--md-light-blue-A200: #40c4ff;--md-light-blue-A400: #00b0ff;--md-light-blue-A700: #0091ea;--md-cyan-50: #e0f7fa;--md-cyan-100: #b2ebf2;--md-cyan-200: #80deea;--md-cyan-300: #4dd0e1;--md-cyan-400: #26c6da;--md-cyan-500: #00bcd4;--md-cyan-600: #00acc1;--md-cyan-700: #0097a7;--md-cyan-800: #00838f;--md-cyan-900: #006064;--md-cyan-A100: #84ffff;--md-cyan-A200: #18ffff;--md-cyan-A400: #00e5ff;--md-cyan-A700: #00b8d4;--md-teal-50: #e0f2f1;--md-teal-100: #b2dfdb;--md-teal-200: #80cbc4;--md-teal-300: #4db6ac;--md-teal-400: #26a69a;--md-teal-500: #009688;--md-teal-600: #00897b;--md-teal-700: #00796b;--md-teal-800: #00695c;--md-teal-900: #004d40;--md-teal-A100: #a7ffeb;--md-teal-A200: #64ffda;--md-teal-A400: #1de9b6;--md-teal-A700: #00bfa5;--md-green-50: #e8f5e9;--md-green-100: #c8e6c9;--md-green-200: #a5d6a7;--md-green-300: #81c784;--md-green-400: #66bb6a;--md-green-500: #4caf50;--md-green-600: #43a047;--md-green-700: #388e3c;--md-green-800: #2e7d32;--md-green-900: #1b5e20;--md-green-A100: #b9f6ca;--md-green-A200: #69f0ae;--md-green-A400: #00e676;--md-green-A700: #00c853;--md-light-green-50: #f1f8e9;--md-light-green-100: #dcedc8;--md-light-green-200: #c5e1a5;--md-light-green-300: #aed581;--md-light-green-400: #9ccc65;--md-light-green-500: #8bc34a;--md-light-green-600: #7cb342;--md-light-green-700: #689f38;--md-light-green-800: #558b2f;--md-light-green-900: #33691e;--md-light-green-A100: #ccff90;--md-light-green-A200: #b2ff59;--md-light-green-A400: #76ff03;--md-light-green-A700: #64dd17;--md-lime-50: #f9fbe7;--md-lime-100: #f0f4c3;--md-lime-200: #e6ee9c;--md-lime-300: #dce775;--md-lime-400: #d4e157;--md-lime-500: #cddc39;--md-lime-600: #c0ca33;--md-lime-700: #afb42b;--md-lime-800: #9e9d24;--md-lime-900: #827717;--md-lime-A100: #f4ff81;--md-lime-A200: #eeff41;--md-lime-A400: #c6ff00;--md-lime-A700: #aeea00;--md-yellow-50: #fffde7;--md-yellow-100: #fff9c4;--md-yellow-200: #fff59d;--md-yellow-300: #fff176;--md-yellow-400: #ffee58;--md-yellow-500: #ffeb3b;--md-yellow-600: #fdd835;--md-yellow-700: #fbc02d;--md-yellow-800: #f9a825;--md-yellow-900: #f57f17;--md-yellow-A100: #ffff8d;--md-yellow-A200: #ffff00;--md-yellow-A400: #ffea00;--md-yellow-A700: #ffd600;--md-amber-50: #fff8e1;--md-amber-100: #ffecb3;--md-amber-200: #ffe082;--md-amber-300: #ffd54f;--md-amber-400: #ffca28;--md-amber-500: #ffc107;--md-amber-600: #ffb300;--md-amber-700: #ffa000;--md-amber-800: #ff8f00;--md-amber-900: #ff6f00;--md-amber-A100: #ffe57f;--md-amber-A200: #ffd740;--md-amber-A400: #ffc400;--md-amber-A700: #ffab00;--md-orange-50: #fff3e0;--md-orange-100: #ffe0b2;--md-orange-200: #ffcc80;--md-orange-300: #ffb74d;--md-orange-400: #ffa726;--md-orange-500: #ff9800;--md-orange-600: #fb8c00;--md-orange-700: #f57c00;--md-orange-800: #ef6c00;--md-orange-900: #e65100;--md-orange-A100: #ffd180;--md-orange-A200: #ffab40;--md-orange-A400: #ff9100;--md-orange-A700: #ff6d00;--md-deep-orange-50: #fbe9e7;--md-deep-orange-100: #ffccbc;--md-deep-orange-200: #ffab91;--md-deep-orange-300: #ff8a65;--md-deep-orange-400: #ff7043;--md-deep-orange-500: 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sourceMappingURL=mkdocs-jupyter.css.map*/ init_mathjax = function() { if (window.MathJax) { // MathJax loaded MathJax.Hub.Config({ TeX: { equationNumbers: { autoNumber: \"AMS\", useLabelIds: true } }, tex2jax: { inlineMath: [ ['$','$'], [\"\\\\(\",\"\\\\)\"] ], displayMath: [ ['$$','$$'], [\"\\\\[\",\"\\\\]\"] ], processEscapes: true, processEnvironments: true }, displayAlign: 'center', CommonHTML: { linebreaks: { automatic: true } } }); MathJax.Hub.Queue([\"Typeset\", MathJax.Hub]); } } init_mathjax(); RNA-seq differential gene expression This tutorial will cover the steps for performing Differential Gene Expression on the RNA-seq data obtained from Galaxy. At the end of this tutorial you will be able to use R to Perform preliminary analyses of the RNA-seq results Find differentially expressed genes between two conditions using edgeR The present tutorial, like the rest of the course material, is available at our open-source github repository . To use this notebook, use the NGS (R) kernel that contains the packages. Choose it by selecting Kernel -> Change Kernel in the menu on top of the window. A few introductory points to run this notebook (click to show): To use this notebook, use the NGS (R) kernel that contains the packages. Choose it by selecting Kernel -> Change Kernel in the menu on top of the window. In this notebook you will use only R commands On some computers, you might see the result of the commands once they are done running. This means you will wait some time while the computer is crunching, and only afterwards you will see the result of the command you have executed You can run the code in each cell by clicking on the run cell button, or by pressing Shift + Enter . When the code is done running, a small green check sign will appear on the left side You need to run the cells in sequential order, please do not run a cell until the one above finished running and do not skip any cells Each cell contains a short description of the code and the output you should get. Please try not to focus on understanding the code for each command in too much detail, but rather try to focus on the output You can create new code cells by pressing + in the Menu bar above. Load the necessary R libraries In [1]: Copied! library ( VennDiagram ) library ( dplyr ) library ( tibble ) library ( formattable ) library ( mixOmics ) library ( pheatmap ) library ( edgeR ) library(VennDiagram) library(dplyr) library(tibble) library(formattable) library(mixOmics) library(pheatmap) library(edgeR) Warning message: \u201cpackage \u2018VennDiagram\u2019 was built under R version 4.1.3\u201d Loading required package: grid Loading required package: futile.logger Warning message: \u201cpackage \u2018futile.logger\u2019 was built under R version 4.1.3\u201d Warning message: \u201cpackage \u2018dplyr\u2019 was built under R version 4.1.3\u201d Attaching package: \u2018dplyr\u2019 The following objects are masked from \u2018package:stats\u2019: filter, lag The following objects are masked from \u2018package:base\u2019: intersect, setdiff, setequal, union Warning message: \u201cpackage \u2018tibble\u2019 was built under R version 4.1.3\u201d Warning message: \u201cpackage \u2018formattable\u2019 was built under R version 4.1.3\u201d Loading required package: MASS Warning message: \u201cpackage \u2018MASS\u2019 was built under R version 4.1.3\u201d Attaching package: \u2018MASS\u2019 The following object is masked from \u2018package:formattable\u2019: area The following object is masked from \u2018package:dplyr\u2019: select Loading required package: lattice Warning message: \u201cpackage \u2018lattice\u2019 was built under R version 4.1.3\u201d Loading required package: ggplot2 Warning message: \u201cpackage \u2018ggplot2\u2019 was built under R version 4.1.3\u201d Loaded mixOmics 6.17.26 Thank you for using mixOmics! Tutorials: http://mixomics.org Bookdown vignette: https://mixomicsteam.github.io/Bookdown Questions, issues: Follow the prompts at http://mixomics.org/contact-us Cite us: citation('mixOmics') Warning message: \u201cpackage \u2018pheatmap\u2019 was built under R version 4.1.3\u201d Warning message: \u201cpackage \u2018edgeR\u2019 was built under R version 4.1.3\u201d Loading required package: limma Warning message: \u201cpackage \u2018limma\u2019 was built under R version 4.1.3\u201d EdgeR - Data filtering and Normalization \u00b6 We will use the edgeR package to test for differentially expressed genes between our Control and Treatment Samples. You can read more about the edgeR package and its functionalities here . This exercise largely follows their user manual. File processing \u00b6 The data for this exercise comes from the 12 tabular files with Reads per Gene counts generated by STAR Mapping in the raw-data alignment part of this course. We want to create a table where each column is a sample, and the content of the table are the read counts from STAR. We must merge the 12 files with Reads per Gene information into a single file. If you aligned datasets in the first notebook with jupyterlab, then you will find the files using the following command: In [2]: Copied! samples <- sort ( system ( \"find results/STAR_output/*_align_contigs_1_2 -name \\\"*ReadsPerGene.out.tab\\\"\" , intern = TRUE )) print ( samples ) Read_counts <- do.call ( cbind , lapply ( samples , function ( x ) read.delim ( file = x , header = FALSE ))) samples < - sort(system(\"find results/STAR_output/*_align_contigs_1_2 -name \\\"*ReadsPerGene.out.tab\\\"\", intern=TRUE)) print(samples) Read_counts < - do.call(cbind, lapply(samples, function(x) read.delim(file=x, header = FALSE))) [1] \"results/STAR_output/S10_align_contigs_1_2/S10_1_1ReadsPerGene.out.tab\" [2] \"results/STAR_output/S10_align_contigs_1_2/S10_1_2ReadsPerGene.out.tab\" [3] \"results/STAR_output/S10_align_contigs_1_2/S10_1_3ReadsPerGene.out.tab\" [4] \"results/STAR_output/S10_align_contigs_1_2/S10_2_1ReadsPerGene.out.tab\" [5] \"results/STAR_output/S10_align_contigs_1_2/S10_2_2ReadsPerGene.out.tab\" [6] \"results/STAR_output/S10_align_contigs_1_2/S10_2_3ReadsPerGene.out.tab\" [7] \"results/STAR_output/TI_align_contigs_1_2/TI_1_1ReadsPerGene.out.tab\" [8] \"results/STAR_output/TI_align_contigs_1_2/TI_1_2ReadsPerGene.out.tab\" [9] \"results/STAR_output/TI_align_contigs_1_2/TI_1_3ReadsPerGene.out.tab\" [10] \"results/STAR_output/TI_align_contigs_1_2/TI_2_1ReadsPerGene.out.tab\" [11] \"results/STAR_output/TI_align_contigs_1_2/TI_2_2ReadsPerGene.out.tab\" [12] \"results/STAR_output/TI_align_contigs_1_2/TI_2_3ReadsPerGene.out.tab\" If you aligned datasets interactively with Galaxy , then you will need to create the folder tabular_files into the results folder copy the tabular files from STAR into the created folder. Each file must have the sample name and end by ReadsPerGene.out.tab . For example S10_1_1ReadsPerGene.out.tab run the following commands removing the # symbol In [ ]: Copied! #samples <- sort(system(\"find results/tabular_files/ -name \\\"*ReadsPerGene.out.tab\\\"\", intern=TRUE)) #print(samples) #Read_counts <- do.call(cbind, lapply(samples, function(x) read.delim(file=x, header = FALSE))) #samples < - sort(system(\"find results/tabular_files/ -name \\\"*ReadsPerGene.out.tab\\\"\", intern=TRUE)) #print(samples) #Read_counts < - do.call(cbind, lapply(samples, function(x) read.delim(file=x, header = FALSE))) The data frame has genes as rows and samples as columns and stores the gene expression counts (value representing the total number of sequence reads that originated from a particular gene in a sample) for each of the 12 samples. This data frame should have 12 columns and 366 rows. In [3]: Copied! rownames ( Read_counts ) <- Read_counts [, 1 ] Read_counts <- Read_counts [ c ( 5 : nrow ( Read_counts )), c ( seq ( 2 , 46 , by = 4 ))] colnames ( Read_counts ) <- c ( \"S10_1_1\" , \"S10_1_2\" , \"S10_1_3\" , \"S10_2_1\" , \"S10_2_2\" , \"S10_2_3\" , \"TI_1_1\" , \"TI_1_2\" , \"TI_1_3\" , \"TI_2_1\" , \"TI_2_2\" , \"TI_2_3\" ) head ( Read_counts , n = 10 ) dim ( Read_counts ) # dimensions of the data frame rownames(Read_counts) < - Read_counts[,1] Read_counts < - Read_counts[c(5:nrow(Read_counts)), c(seq(2, 46, by=4))] colnames(Read_counts) < - c(\"S10_1_1\", \"S10_1_2\", \"S10_1_3\", \"S10_2_1\", \"S10_2_2\", \"S10_2_3\", \"TI_1_1\", \"TI_1_2\", \"TI_1_3\", \"TI_2_1\", \"TI_2_2\", \"TI_2_3\") head(Read_counts, n=10) dim(Read_counts) # dimensions of the data frame A data.frame: 10 \u00d7 12 S10_1_1 S10_1_2 S10_1_3 S10_2_1 S10_2_2 S10_2_3 TI_1_1 TI_1_2 TI_1_3 TI_2_1 TI_2_2 TI_2_3 g175 143 226 227 217 206 193 234 205 198 221 206 165 g176 17 12 15 8 14 18 29 19 30 6 11 23 g177 0 0 0 0 0 0 0 0 0 0 0 0 g178 0 0 0 0 0 0 0 0 2 2 1 2 g179 0 0 0 0 0 0 0 0 0 0 0 0 g180 2773 5223 4350 4139 3209 4644 1015 1313 889 2675 1023 2565 g181 473 679 583 437 390 450 642 700 697 650 459 410 g182 19 15 20 9 8 4 13 7 8 6 5 4 g183 0 0 0 0 0 0 0 0 0 0 0 0 g184 30 22 50 57 103 83 36 29 18 107 69 89 .list-inline {list-style: none; margin:0; padding: 0} .list-inline>li {display: inline-block} .list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex} 366 12 Import the Metadata table. This file, as its name suggests, contains information about each of the 12 RNA-seq samples, such as the treatment (Condition), genotype and replicate. Note that the order of the rows in the Metadata table should be the same as the columns in the Read_counts file generated above. In [4]: Copied! metadata <- read.csv ( \"../Data/Clover_Data/metadata.csv\" , sep = \";\" , row.names = 1 , stringsAsFactors = TRUE ) metadata metadata < - read.csv(\"../Data/Clover_Data/metadata.csv\", sep =\";\", row.names=1, stringsAsFactors=TRUE) metadata A data.frame: 12 \u00d7 3 Condition Genotype Replicate S10_1_1 Control S10 1 S10_1_2 Control S10 2 S10_1_3 Control S10 3 S10_2_1 Treatment S10 1 S10_2_2 Treatment S10 2 S10_2_3 Treatment S10 3 TI_1_1 Control Tienshan 1 TI_1_2 Control Tienshan 2 TI_1_3 Control Tienshan 3 TI_2_1 Treatment Tienshan 1 TI_2_2 Treatment Tienshan 2 TI_2_3 Treatment Tienshan 3 In order to aid the following steps, we will create a Group for each sample (a new column in the metadata) based on the Genotype&Condition of each sample and assign the three replicates to this group. In [5]: Copied! Group <- factor ( paste ( metadata $ Genotype , metadata $ Condition , sep = \"_\" )) metadata <- cbind ( metadata , Group = Group ) metadata Group < - factor(paste(metadata$Genotype, metadata$Condition, sep=\"_\")) metadata < - cbind(metadata,Group=Group) metadata A data.frame: 12 \u00d7 4 Condition Genotype Replicate Group S10_1_1 Control S10 1 S10_Control S10_1_2 Control S10 2 S10_Control S10_1_3 Control S10 3 S10_Control S10_2_1 Treatment S10 1 S10_Treatment S10_2_2 Treatment S10 2 S10_Treatment S10_2_3 Treatment S10 3 S10_Treatment TI_1_1 Control Tienshan 1 Tienshan_Control TI_1_2 Control Tienshan 2 Tienshan_Control TI_1_3 Control Tienshan 3 Tienshan_Control TI_2_1 Treatment Tienshan 1 Tienshan_Treatment TI_2_2 Treatment Tienshan 2 Tienshan_Treatment TI_2_3 Treatment Tienshan 3 Tienshan_Treatment Create the DGEList object \u00b6 We will merge the read counts and the metadata into a list-based data object named DGEList, which can be manipulated as any list object in R. The main components of the DGEList object are the matrix \"counts\" containing our read per gene counts and a data.frame \"samples\" containing the metadata. Note that all the genes with zero counts across all samples were eliminated. In [6]: Copied! DGEList <- DGEList ( Read_counts , remove.zeros = TRUE ) DGEList $ samples $ Condition <- relevel ( metadata $ Condition , ref = \"Control\" ) DGEList $ samples $ Genotype <- metadata $ Genotype DGEList $ samples $ group <- metadata $ Group DGEList DGEList < - DGEList(Read_counts, remove.zeros = TRUE) DGEList$samples$Condition < - relevel(metadata$Condition, ref = \"Control\") DGEList$samples$Genotype < - metadata$Genotype DGEList$samples$group < - metadata$Group DGEList Removing 99 rows with all zero counts $counts A matrix: 267 \u00d7 12 of type int S10_1_1 S10_1_2 S10_1_3 S10_2_1 S10_2_2 S10_2_3 TI_1_1 TI_1_2 TI_1_3 TI_2_1 TI_2_2 TI_2_3 g175 143 226 227 217 206 193 234 205 198 221 206 165 g176 17 12 15 8 14 18 29 19 30 6 11 23 g178 0 0 0 0 0 0 0 0 2 2 1 2 g180 2773 5223 4350 4139 3209 4644 1015 1313 889 2675 1023 2565 g181 473 679 583 437 390 450 642 700 697 650 459 410 g182 19 15 20 9 8 4 13 7 8 6 5 4 g184 30 22 50 57 103 83 36 29 18 107 69 89 g185 0 2 0 0 0 0 0 1 0 0 0 1 g186 1 0 0 0 0 0 0 0 0 0 0 0 g187 1 1 2 0 1 1 58 49 57 28 32 29 g188 316 285 417 200 184 322 1276 855 1341 402 809 547 g190 14 18 29 25 17 31 7 6 12 20 7 6 g191 2 2 1 1 0 0 0 0 0 0 0 0 g192 1 1 1 1 1 0 6 10 2 3 8 2 g193 208 231 301 289 161 279 70 61 60 128 160 91 g194 40 41 37 44 17 43 8 10 7 8 105 25 g195 54 85 85 71 54 43 56 54 42 67 68 56 g196 77 120 118 186 152 177 5 9 5 42 20 34 g197 1 6 5 18 14 21 15 44 12 32 16 17 g198 14 27 10 11 2 2 6 6 1 2 2 0 g199 0 0 0 0 0 0 0 0 0 0 1 0 g200 57 80 94 11 8 23 89 47 67 5 47 5 g203 815 1188 1412 1139 1034 1127 911 852 662 892 781 784 g204 250 312 348 277 275 264 125 104 133 119 91 130 g205 109 132 181 140 114 151 89 73 66 78 46 50 g206 0 1 2 7 4 9 4 0 7 0 1 2 g207 0 1 0 0 4 3 1 3 4 4 3 3 g208 0 1 0 0 0 0 0 1 0 1 0 0 g209 0 0 0 0 0 0 0 0 1 0 0 0 g210 52 76 60 12 12 13 61 73 94 54 45 21 \u22ee \u22ee \u22ee \u22ee \u22ee \u22ee \u22ee \u22ee \u22ee \u22ee \u22ee \u22ee \u22ee g135 0 0 0 1 0 0 5 1 0 2 0 0 g139 0 1 1 0 0 0 12 13 11 4 3 3 g140 0 5 1 0 0 2 5 4 0 0 2 0 g141 177 217 224 131 117 166 117 85 67 80 79 77 g142 488 713 557 241 154 228 983 1057 1023 1075 794 933 g144 3319 3987 4763 2249 1996 2260 4405 3880 4585 5072 3618 3590 g146 293 330 350 913 775 731 108 92 115 431 151 226 g147 6 3 3 0 8 10 1 1 1 0 1 0 g148 19 30 24 15 4 12 23 19 25 10 19 8 g149 35 62 65 57 58 53 114 103 91 92 79 93 g150 263 411 278 752 1378 1300 106 112 70 516 200 508 g151 44 44 50 92 92 84 23 26 25 56 43 31 g152 242 258 294 425 381 521 15 8 8 16 21 15 g153 142 144 181 233 177 233 31 49 50 50 45 57 g155 0 4 2 1 0 0 1 0 3 0 0 1 g156 0 0 0 0 0 0 0 3 2 0 0 1 g157 6 1 3 223 52 152 4 7 4 153 47 155 g158 0 0 0 7 1 3 0 0 1 1 0 0 g161 25 38 18 1 9 2 63 23 43 0 24 6 g162 31 43 38 43 25 28 0 0 0 0 0 0 g163 1 0 12 37 8 18 9 4 8 16 13 15 g164 69 58 97 294 123 168 56 58 60 146 101 135 g165 203 376 594 1343 2062 1610 265 229 214 488 270 490 g167 2 0 0 2 0 0 0 0 0 0 0 0 g168 2 14 6 0 0 5 31 26 38 3 27 10 g169 0 0 1 0 1 1 0 0 0 0 0 0 g170 0 0 0 0 0 0 0 1 0 0 0 0 g171 0 5 0 0 0 5 0 0 0 1 0 0 g172 1 2 2 6 4 3 3 1 2 3 3 0 g173 80 107 120 78 138 121 67 86 69 96 105 105 $samples A data.frame: 12 \u00d7 5 group lib.size norm.factors Condition Genotype S10_1_1 S10_Control 43614 1 Control S10 S10_1_2 S10_Control 58803 1 Control S10 S10_1_3 S10_Control 62654 1 Control S10 S10_2_1 S10_Treatment 59909 1 Treatment S10 S10_2_2 S10_Treatment 56761 1 Treatment S10 S10_2_3 S10_Treatment 63152 1 Treatment S10 TI_1_1 Tienshan_Control 43387 1 Control Tienshan TI_1_2 Tienshan_Control 38355 1 Control Tienshan TI_1_3 Tienshan_Control 40304 1 Control Tienshan TI_2_1 Tienshan_Treatment 55509 1 Treatment Tienshan TI_2_2 Tienshan_Treatment 39909 1 Treatment Tienshan TI_2_3 Tienshan_Treatment 45008 1 Treatment Tienshan Preliminary data analysis \u00b6 First, we will calculate the \"pseudoCounts\" as log2 of the reads per gene counts. This is not part of the actual differential gene expression analysis but is helpful for data exploration and quality assessment. We will look at a histogram of one of the samples and a boxplot representation of the log2 counts for all the 12 samples. Note that there are many genes with a low number of mapped reads and that there are differences between the average read counts for each library. In [7]: Copied! pseudoCounts <- log2 ( DGEList $ counts + 1 ) head ( pseudoCounts ) hist ( pseudoCounts [ , \"S10_1_1\" ], main = \"\" , xlab = \"Read counts\" ) boxplot ( pseudoCounts , col = \"gray\" , las = 3 , cex.names = 1 ) pseudoCounts < - log2(DGEList$counts + 1) head(pseudoCounts) hist(pseudoCounts[ ,\"S10_1_1\"], main = \"\", xlab = \"Read counts\") boxplot(pseudoCounts, col = \"gray\", las = 3, cex.names = 1) A matrix: 6 \u00d7 12 of type dbl S10_1_1 S10_1_2 S10_1_3 S10_2_1 S10_2_2 S10_2_3 TI_1_1 TI_1_2 TI_1_3 TI_2_1 TI_2_2 TI_2_3 g175 7.169925 7.826548 7.832890 7.768184 7.693487 7.599913 7.876517 7.686501 7.636625 7.794416 7.693487 7.375039 g176 4.169925 3.700440 4.000000 3.169925 3.906891 4.247928 4.906891 4.321928 4.954196 2.807355 3.584963 4.584963 g178 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 1.584963 1.584963 1.000000 1.584963 g180 11.437752 12.350939 12.087131 12.015415 11.648358 12.181463 9.988685 10.359750 9.797662 11.385862 10.000000 11.325305 g181 8.888743 9.409391 9.189825 8.774787 8.611025 8.816984 9.328675 9.453271 9.447083 9.346514 8.845490 8.682995 g182 4.321928 4.000000 4.392317 3.321928 3.169925 2.321928 3.807355 3.000000 3.169925 2.807355 2.584963 2.321928 We can also create a PCA plot of the samples in order to assess the differences between the Genotypes and Conditions, but also between the replicates. In this plot the samples that are similar cluster together, while samples that are different are further apart. In this type of plot, we would expect samples from the same group (the three replicates for each sample) to exhibit a similar gene expression profile thus clustering together while being separated from the other samples. In [8]: Copied! resPCA <- pca ( t ( pseudoCounts ), ncomp = 6 ) plotIndiv ( resPCA , group = metadata $ Genotype , pch = metadata $ Condition , legend = T , legend.title = 'Genotype' , legend.title.pch = 'Condition' , title = 'PCA plot raw counts' , style = 'ggplot2' , size.xlabel = 10 , size.ylabel = 10 ) resPCA < - pca(t(pseudoCounts), ncomp = 6) plotIndiv(resPCA, group = metadata$Genotype, pch=metadata$Condition, legend = T, legend.title = 'Genotype', legend.title.pch = 'Condition', title = 'PCA plot raw counts', style = 'ggplot2', size.xlabel = 10, size.ylabel = 10) Filtering the lowly expressed genes \u00b6 As seen previously, many genes have a low number of read counts in our samples. The genes with very low counts across all libraries provide little evidence for differential expression, thus we should eliminate these genes before the analysis. One of the filtering methods we can use is the \"filterByExpr\" function provided by the edgeR package. By default, this function keeps only the genes that have at least 10 reads per group, but other cutoffs can also be applied. In [9]: Copied! keep <- filterByExpr ( DGEList , group = metadata $ Group ) #create the filter DGEList <- DGEList [ keep , , keep.lib.sizes = FALSE ] #apply the filter to on the DGEList object table ( keep ) #Check the number of genes that passed the filter keep < - filterByExpr(DGEList, group=metadata$Group) #create the filter DGEList < - DGEList[keep, , keep.lib.sizes=FALSE] #apply the filter to on the DGEList object table(keep) #Check the number of genes that passed the filter keep FALSE TRUE 104 163 Normalization \u00b6 As we are working with multiple samples we need to normalize the read counts per gene in order to account for compositional and technical differences between the 12 RNA-seq libraries. For this, we will calculate normalization factors using the trimmed mean of M-values (TMM) method. You can read more about different normalization methods in the user manual. Note that running \"calcNormFactors\" does not change the actual reads per gene counts, it just fills the \"norm.factors\" column in DGEList$samples . These factors will be used to scale the read counts for each library. From the user guide: \"A normalization factor below one indicates that a small number of high count genes are monopolizing the sequencing, causing the counts for other genes to be lower than would be usual given the library size.\" In [10]: Copied! DGEList <- calcNormFactors ( DGEList , method = \"RLE\" ) DGEList $ samples DGEList < - calcNormFactors(DGEList, method=\"RLE\") DGEList$samples A data.frame: 12 \u00d7 5 group lib.size norm.factors Condition Genotype S10_1_1 S10_Control 43506 1.0897129 Control S10 S10_1_2 S10_Control 58660 1.0385093 Control S10 S10_1_3 S10_Control 62512 1.0362962 Control S10 S10_2_1 S10_Treatment 59741 0.9802860 Treatment S10 S10_2_2 S10_Treatment 56653 0.8418644 Treatment S10 S10_2_3 S10_Treatment 63008 0.8949144 Treatment S10 TI_1_1 Tienshan_Control 43174 1.0618440 Control Tienshan TI_1_2 Tienshan_Control 38203 1.0431678 Control Tienshan TI_1_3 Tienshan_Control 40178 1.0676351 Control Tienshan TI_2_1 Tienshan_Treatment 55391 0.9048196 Treatment Tienshan TI_2_2 Tienshan_Treatment 39810 1.1235077 Treatment Tienshan TI_2_3 Tienshan_Treatment 44910 0.9603761 Treatment Tienshan Normalized counts - Exploratory Data analysis \u00b6 For data analysis purposes normalized log2 counts can be extracted from the DGEList object using the function CPM (counts per million). We will generate the same plots as for the raw counts in order to compare the data before and after normalization. Do the plots for normalized counts look different compared with the plots computed before data filtering and normalization? In [11]: Copied! pseudoNormCounts <- cpm ( DGEList , log = TRUE , prior.count = 1 ) head ( pseudoNormCounts ) hist ( pseudoNormCounts [ , \"S10_1_1\" ], main = \"\" , xlab = \"counts\" ) boxplot ( pseudoNormCounts , col = \"gray\" , las = 3 , cex.names = 1 ) resPCA <- pca ( t ( pseudoNormCounts ), ncomp = 6 ) plotIndiv ( resPCA , group = metadata $ Genotype , pch = metadata $ Condition , legend = T , legend.title = 'Genotype' , legend.title.pch = 'Condition' , title = 'PCA plot normalized counts' , style = 'ggplot2' , size.xlabel = 10 , size.ylabel = 10 ) pseudoNormCounts < - cpm(DGEList, log = TRUE, prior.count = 1) head(pseudoNormCounts) hist(pseudoNormCounts[ ,\"S10_1_1\"], main = \"\", xlab = \"counts\") boxplot(pseudoNormCounts, col = \"gray\", las = 3, cex.names = 1) resPCA < - pca(t(pseudoNormCounts), ncomp = 6) plotIndiv(resPCA, group = metadata$Genotype, pch=metadata$Condition, legend = T, legend.title = 'Genotype', legend.title.pch = 'Condition', title = 'PCA plot normalized counts', style = 'ggplot2', size.xlabel = 10, size.ylabel = 10) A matrix: 6 \u00d7 12 of type dbl S10_1_1 S10_1_2 S10_1_3 S10_2_1 S10_2_2 S10_2_3 TI_1_1 TI_1_2 TI_1_3 TI_2_1 TI_2_2 TI_2_3 g175 11.568005 11.864813 11.782957 11.863090 12.083127 11.749272 12.323049 12.334198 12.178544 12.112851 12.175386 11.908906 g176 8.564108 7.760868 7.974199 7.290213 8.292080 8.405708 9.349782 8.956131 9.490388 7.125494 8.054467 9.111569 g180 15.836359 16.387906 16.035457 16.109276 16.038322 16.329943 14.435626 15.008665 14.340400 15.704307 14.482504 15.860317 g181 13.287211 13.446752 13.138771 12.869139 13.000828 12.965820 13.775545 14.101988 13.989757 13.664955 13.327806 13.217591 g182 8.716557 8.056048 8.360376 7.439590 7.551908 6.505541 8.245462 7.611500 7.689402 7.125494 7.041192 6.815835 g184 9.350249 8.573811 9.628852 9.955925 11.089735 10.542877 9.653135 9.546090 8.779819 11.073313 10.609695 11.024686 EdgeR - Testing for Differentially expressed genes (DEGs) \u00b6 We use edgeR for Differential Gene Expression using the GLM method (Generalized linear model) with treatment and genotype effects After we concluded the exploratory data analysis and the filtering and normalization steps we can now start testing for differentially expressed genes between our samples. For this, we first need to define a \"design matrix\" which describes our experimental design. We construct a matrix containing samples as rows and our previously defined groups (S10_Control, S10_Treatment, Tienshan_Control, Tienshan_Treatment) as columns. The samples belonging to each group are assigned a \"1\" in this matrix. In [12]: Copied! design.matrix <- model.matrix ( ~ 0 + Group ) rownames ( design.matrix ) <- colnames ( DGEList ) colnames ( design.matrix ) <- levels ( metadata $ Group ) design.matrix design.matrix < - model.matrix(~0+Group) rownames(design.matrix) < - colnames(DGEList) colnames(design.matrix) < - levels(metadata$Group) design.matrix A matrix: 12 \u00d7 4 of type dbl S10_Control S10_Treatment Tienshan_Control Tienshan_Treatment S10_1_1 1 0 0 0 S10_1_2 1 0 0 0 S10_1_3 1 0 0 0 S10_2_1 0 1 0 0 S10_2_2 0 1 0 0 S10_2_3 0 1 0 0 TI_1_1 0 0 1 0 TI_1_2 0 0 1 0 TI_1_3 0 0 1 0 TI_2_1 0 0 0 1 TI_2_2 0 0 0 1 TI_2_3 0 0 0 1 For easier pairwise comparisons between the groups we can create some \"contrasts\" using the \"makeContrasts\" function. This will tell the program which samples we will like to consider as our \"Control\" and which as our \"Treatment\" groups. The Control groups will be assigned a \"-1\" and the Treatment groups a \"1\". When using multiple Control and Treatment samples in the same contrast we need to devide by the number of samples used as for example in the contrast S10_Tienshan, where we use both S10 and Ti samples. For example, the contrast S10 = S10_Treatment - S10_Control will test for differentially expressed genes between the S10 treatment samples vs the S10 Control samples. In [13]: Copied! contrasts <- makeContrasts ( S10 = S10_Treatment - S10_Control , Tienshan = Tienshan_Treatment - Tienshan_Control , S10_Tienshan = ( S10_Treatment + Tienshan_Treatment ) / 2 - ( S10_Control + Tienshan_Control ) / 2 , levels = design.matrix ) contrasts contrasts < - makeContrasts( S10 = S10_Treatment-S10_Control, Tienshan = Tienshan_Treatment-Tienshan_Control, S10_Tienshan= (S10_Treatment+Tienshan_Treatment)/2 - (S10_Control+Tienshan_Control)/2, levels=design.matrix) contrasts A matrix: 4 \u00d7 3 of type dbl S10 Tienshan S10_Tienshan S10_Control -1 0 -0.5 S10_Treatment 1 0 0.5 Tienshan_Control 0 -1 -0.5 Tienshan_Treatment 0 1 0.5 Any ideas for other contrasts that migth be interesting to explore? The following cell will run two mandatory steps in order to fit a model in edgeR. First, the dispersion needs to be estimated, this is a measure of the degree of inter-library variation for each gene. Afterwards, a QL ( quasi-likelihood) model representing the sample design is fitted using the \"glmQLFit\" function. You can read more about these steps in the edgeR user guide. In [14]: Copied! DGEList <- estimateDisp ( DGEList , design.matrix ) fit <- glmQLFit ( DGEList , design.matrix ) DGEList < - estimateDisp(DGEList, design.matrix) fit < - glmQLFit(DGEList, design.matrix) DEGs for White clover S10 plants Treatment vs Control \u00b6 Find DEGs for White clover S10 plants in Treatment condition compared with the Control condition We will test for differentially expressed genes using the quasi-likelihood F-tests method. This is easily done just by running the \"glmQFTest\" function on the fit model and selecting one of the contrasts we created above (in this case the \"S10\" contrast). Next, we will use the topTags function to extract the information about all the genes. In [15]: Copied! glmqlf_S10 <- glmQLFTest ( fit , contrast = contrasts [, \"S10\" ]) DEG_S10 <- topTags ( glmqlf_S10 , n = nrow ( DGEList $ counts )) DEG_S10 glmqlf_S10 < - glmQLFTest(fit, contrast=contrasts[,\"S10\"]) DEG_S10 < - topTags(glmqlf_S10, n = nrow(DGEList$counts)) DEG_S10 $table A data.frame: 163 \u00d7 5 logFC logCPM F PValue FDR g39 3.5861702 14.124630 336.66158 5.227331e-10 8.520550e-08 g232 4.0494125 11.950441 157.50997 3.685988e-08 2.591779e-06 g6 2.3225347 12.999541 150.27854 4.770146e-08 2.591779e-06 g350 2.0530029 13.324566 97.56453 4.890245e-07 1.992775e-05 g99 -1.5387874 11.681004 85.17478 9.976659e-07 3.195090e-05 g142 -1.4235276 13.830692 82.52308 1.176107e-06 3.195090e-05 g240 1.3603712 14.708794 79.98114 1.383201e-06 3.220882e-05 g269 -1.4191681 11.834217 75.66563 1.841279e-06 3.751606e-05 g26 2.1254241 11.366827 68.55898 3.047273e-06 5.518949e-05 g129 -5.3324794 8.679594 60.45459 5.738115e-06 9.353127e-05 g272 3.1405978 13.860652 59.08187 6.432977e-06 9.532503e-05 g157 5.3813801 10.414972 57.03712 7.659515e-06 1.040417e-04 g42 -1.9856547 9.962762 53.97164 1.005146e-05 1.260298e-04 g165 2.2666761 13.690016 51.66777 1.243606e-05 1.447913e-04 g101 3.1488142 13.181625 48.21727 1.736838e-05 1.887363e-04 g15 2.7410696 10.684840 47.29143 1.906062e-05 1.941800e-04 g210 -2.2430064 9.993002 46.52627 2.060621e-05 1.975772e-04 g299 -2.3509276 10.984491 45.75893 2.230566e-05 2.019901e-04 g103 2.6810410 10.579777 44.26963 2.609599e-05 2.238762e-04 g144 -0.7963477 16.186987 43.17707 2.936057e-05 2.392886e-04 g77 1.0984806 14.118231 41.46740 3.548119e-05 2.754016e-04 g125 -1.3099534 10.195970 39.85537 4.266319e-05 3.160955e-04 g298 -2.0471647 9.837072 33.49832 9.395967e-05 6.658881e-04 g104 2.4081173 9.219160 38.54240 1.121281e-04 7.615370e-04 g122 -0.6485441 12.457152 30.90261 1.341148e-04 8.744287e-04 g164 1.4415064 11.156457 30.50383 1.419308e-04 8.897970e-04 g146 1.3983889 12.824184 29.92911 1.541544e-04 9.306356e-04 g313 2.2492040 13.614815 28.41728 1.926822e-04 1.121686e-03 g150 1.9650641 13.229706 27.90020 2.083770e-04 1.171222e-03 g13 -0.6522187 13.101579 26.45593 2.608176e-04 1.417109e-03 \u22ee \u22ee \u22ee \u22ee \u22ee \u22ee g96 -1.873792302 8.939397 0.4844512655 0.4999546 0.6041584 g10 0.233134670 14.382992 0.4834790913 0.5003766 0.6041584 g253 0.052426914 13.741219 0.3075042687 0.5896191 0.7066759 g289 -0.212573283 9.390864 0.2887857862 0.6010328 0.7150974 g312 0.093543041 10.849110 0.2666731965 0.6151559 0.7265972 g301 -0.102000599 10.135184 0.2572520652 0.6214023 0.7274503 g221 -0.277095720 8.469909 0.2522121463 0.6248039 0.7274503 g162 -0.145155616 8.517263 0.2278112475 0.6463283 0.7413085 g50 -0.170907352 8.749274 0.2158314533 0.6507284 0.7413085 g203 0.062836598 14.227601 0.2124612164 0.6532634 0.7413085 g88 0.045949497 14.235850 0.2103049178 0.6548983 0.7413085 g355 -0.287897714 8.890754 0.1803234432 0.6787703 0.7630314 g204 -0.055975779 11.947887 0.1408988543 0.7140799 0.7972262 g306 0.054269738 11.294732 0.0923937800 0.7664733 0.8498990 g193 0.049946343 11.682386 0.0642557849 0.8042743 0.8857886 g205 0.037877227 10.986961 0.0473457745 0.8314821 0.9029713 g194 -0.142990391 9.403497 0.0447552162 0.8360811 0.9029713 g362 0.025629967 11.336529 0.0415198735 0.8420254 0.9029713 g315 0.027958374 11.878331 0.0409766489 0.8430470 0.9029713 g180 0.059680256 15.711409 0.0386140448 0.8475743 0.9029713 g282 -0.026492114 11.756078 0.0247991469 0.8775434 0.9288284 g176 -0.061055115 8.587203 0.0144510429 0.9063467 0.9531258 g126 0.061707217 8.311212 0.0024786868 0.9648334 1.0000000 g128 0.063825269 7.671043 0.0019414812 0.9655950 1.0000000 g227 -0.025740630 7.939955 0.0011908008 0.9730516 1.0000000 g121 0.002660852 12.896620 0.0005233398 0.9821327 1.0000000 g284 0.000000000 8.538720 0.0000000000 1.0000000 1.0000000 g116 0.000000000 7.180297 0.0000000000 1.0000000 1.0000000 g117 0.000000000 7.033307 0.0000000000 1.0000000 1.0000000 g118 0.000000000 7.515646 0.0000000000 1.0000000 1.0000000 $adjust.method 'BH' $comparison '-1*S10_Control 1*S10_Treatment' $test 'glm' We have now created a table with certain parameters calculated for each of the genes analysed. logFC represents the base 2 logarithm of the fold change and it shows us how much the expression of the gene has changed between the two conditions. A logFC of 1 means a doubling in the read per gene count between the control and treatment samples. The genes with a logFC higher than 0 are upregulated while the genes with a logFC lower than 0 are downregulated between the control and treatment samples. logCPM represents the average log2-counts-per-million, the abundance of the gene. F - F-statistic. PValue is the raw p-value. FDR (The false discovery rate) represents the adjusted p-value and is calculated using Benjamini and Hochberg's algorithm. It controls the rate of false positive values under multiple testing. Usually, a threshold of under 5% is set for the FDR. The important information for us in this table is stored in the \"logFC\" and the \"FDR\" columns. The top DE genes have small FDR values and large fold changes Many of the genes in the samples are uninteresting for us, as they have a high FDR and/or low logFC values so we cannot consider them as differentially expressed. We will apply a filtering step in order to keep only the statistically significant genes. We will filter out the genes with an FDR higher than 0.05 and an absolute logFC lower than 1. In [16]: Copied! DEG_S10_filtered <- DEG_S10 $ table [ DEG_S10 $ table $ FDR < 0.05 & abs ( DEG_S10 $ table $ logFC ) > 1 ,] #Filtering DEG_S10_filtered <- rownames_to_column ( DEG_S10_filtered ) %>% rename ( gene_ID = rowname ) #Adding the gene_ID column head ( DEG_S10_filtered ) nrow ( DEG_S10_filtered ) # finding the number of genes that passed the filter DEG_S10_filtered < - DEG_S10$table[DEG_S10$table$FDR < 0.05 & abs(DEG_S10$table$logFC) > 1,] #Filtering DEG_S10_filtered < - rownames_to_column(DEG_S10_filtered) %>% rename(gene_ID = rowname) #Adding the gene_ID column head(DEG_S10_filtered) nrow(DEG_S10_filtered) # finding the number of genes that passed the filter A data.frame: 6 \u00d7 6 gene_ID logFC logCPM F PValue FDR 1 g39 3.586170 14.12463 336.66158 5.227331e-10 8.520550e-08 2 g232 4.049413 11.95044 157.50997 3.685988e-08 2.591779e-06 3 g6 2.322535 12.99954 150.27854 4.770146e-08 2.591779e-06 4 g350 2.053003 13.32457 97.56453 4.890245e-07 1.992775e-05 5 g99 -1.538787 11.68100 85.17478 9.976659e-07 3.195090e-05 6 g142 -1.423528 13.83069 82.52308 1.176107e-06 3.195090e-05 52 We can also visualize the selected genes by plotting a Smear plot or a Volcano plot. The Genes that passed the filter are coloured in red, and the top 10 genes with the lowest FDR value are labelled with their gene ID. We can see that the majority of the genes analysed are either not statistically significant or have a very small logFC. In [17]: Copied! plotSmear ( glmqlf_S10 , de.tags = rownames ( DEG_S10 $ table )[ which ( DEG_S10 $ table $ FDR < 0.05 & abs ( DEG_S10 $ table $ logFC ) > 1 )]) text ( x = DEG_S10_filtered $ logCPM [ 1 : 10 ], y = DEG_S10_filtered $ logFC [ 1 : 10 ], labels = DEG_S10_filtered $ gene_ID [ 1 : 10 ], cex = 0.7 , pos = 1 ) abline ( h = c ( -1 , 1 ), col = \"blue\" ) plotSmear(glmqlf_S10, de.tags = rownames(DEG_S10$table)[which(DEG_S10$table$FDR < 0.05 & abs(DEG_S10$table$logFC) > 1)]) text(x=DEG_S10_filtered$logCPM[1:10], y=DEG_S10_filtered$logFC[1:10], labels=DEG_S10_filtered$gene_ID[1:10], cex=0.7, pos=1) abline(h = c(-1, 1), col = \"blue\") We can also create a heatmap with the log2 read counts of the selected differentially expressed genes so that we can visualise the differences in normalized counts between the Control and the Treatment samples. The genes are ordered by the FDR value. In [18]: Copied! annot_col <- data.frame ( row.names = colnames ( pseudoNormCounts )[ 1 : 6 ], Condition = c ( rep ( \"Control\" , 3 ), rep ( \"Treatment\" , 3 ))) pheatmap ( as.matrix ( pseudoNormCounts [ DEG_S10_filtered $ gene_ID , c ( 1 : 6 )]), cluster_rows = F , cluster_col = F , annotation_col = annot_col ) annot_col < - data.frame(row.names = colnames(pseudoNormCounts)[1:6], Condition = c(rep(\"Control\", 3),rep( \"Treatment\", 3))) pheatmap(as.matrix(pseudoNormCounts[DEG_S10_filtered$gene_ID,c(1:6)]), cluster_rows = F, cluster_col = F, annotation_col = annot_col) DEGs for White clover Tienshan Treatment vs Control \u00b6 We can do this using the same functions as above and changing the contrast. This time we will directly filter the differentially expressed genes using the same parameters as for the S10 samples. In [19]: Copied! glmqlf_Ti <- glmQLFTest ( fit , contrast = contrasts [, \"Tienshan\" ]) DEG_Ti <- topTags ( glmqlf_Ti , n = nrow ( DGEList $ counts )) DEG_Ti_filtered <- DEG_Ti $ table [ DEG_Ti $ table $ FDR < 0.05 & abs ( DEG_Ti $ table $ logFC ) > 1 ,] DEG_Ti_filtered <- rownames_to_column ( DEG_Ti_filtered ) %>% rename ( gene_ID = rowname ) print ( head ( DEG_Ti_filtered )) print ( \"Nr of differentially expressed genes:\" ) print ( nrow ( DEG_Ti_filtered )) glmqlf_Ti < - glmQLFTest(fit, contrast=contrasts[,\"Tienshan\"]) DEG_Ti < - topTags(glmqlf_Ti, n = nrow(DGEList$counts)) DEG_Ti_filtered < - DEG_Ti$table[DEG_Ti$table$FDR < 0.05 & abs(DEG_Ti$table$logFC) > 1,] DEG_Ti_filtered < - rownames_to_column(DEG_Ti_filtered) %>% rename(gene_ID = rowname) print(head(DEG_Ti_filtered)) print(\"Nr of differentially expressed genes:\") print(nrow(DEG_Ti_filtered)) gene_ID logFC logCPM F PValue FDR 1 g234 -3.348667 13.00417 278.01679 1.547626e-09 2.522630e-07 2 g269 -1.885843 11.83422 127.39949 1.172628e-07 9.556922e-06 3 g26 2.423725 11.36683 74.33983 2.016212e-06 8.910761e-05 4 g101 4.303290 13.18162 73.17140 2.186690e-06 8.910761e-05 5 g272 3.391225 13.86065 66.80131 3.475790e-06 9.840357e-05 6 g99 -1.430395 11.68100 66.25814 3.622217e-06 9.840357e-05 [1] \"Nr of differentially expressed genes:\" [1] 42 We can now plot a Venn diagram with the DEGs for the two genotypes in order to observe the number of common and specific differentially expressed genes between the two genotypes as response to the cold exposure. You can see that a high percentage of the identified genes are common for the two genotypes, while each genotype has also specific genes. In [20]: Copied! vd <- venn.diagram ( x = list ( DEG_S10_filtered $ gene_ID , DEG_Ti_filtered $ gene_ID ), category.names = c ( \"S10\" , \"Tienshan\" ), lwd = 4 , fill = c ( \"cornflowerblue\" , \"yellowgreen\" ), filename = NULL , cat.cex = 1 , cat.fontface = \"bold\" , output = TRUE ) grid.draw ( vd ) vd < - venn.diagram( x = list(DEG_S10_filtered$gene_ID, DEG_Ti_filtered$gene_ID), category.names = c(\"S10\" , \"Tienshan\"), lwd = 4, fill = c(\"cornflowerblue\", \"yellowgreen\"), filename = NULL, cat.cex = 1, cat.fontface = \"bold\", output=TRUE ) grid.draw(vd) DEGs for S10+Tienshan Treatment vs Control w/o genotype effects \u00b6 Until now we tested for DEGs specific for each of the two genotypes under cold treatment. We can also run a test where we ignore the genotype and just test for the differences in the cold response. Consider the counts for both genotypes as a single dataset using the previously created contrast \"S10_Tienshan\". Do the results look different compared with the previous tests? In [21]: Copied! glmqlf_S10_Ti <- glmQLFTest ( fit , contrast = contrasts [, \"S10_Tienshan\" ]) DEG_S10_Ti <- topTags ( glmqlf_S10_Ti , n = nrow ( DGEList $ counts )) DEG_S10_Ti_filtered <- DEG_S10_Ti $ table [ DEG_S10_Ti $ table $ FDR < 0.05 & abs ( DEG_S10_Ti $ table $ logFC ) > 1 ,] DEG_S10_Ti_filtered <- rownames_to_column ( DEG_S10_Ti_filtered ) %>% rename ( gene_ID = rowname ) print ( head ( DEG_S10_Ti_filtered )) print ( \"Nr of differentially expressed genes:\" ) print ( nrow ( DEG_S10_Ti_filtered )) glmqlf_S10_Ti < - glmQLFTest(fit, contrast=contrasts[,\"S10_Tienshan\"]) DEG_S10_Ti < - topTags(glmqlf_S10_Ti, n = nrow(DGEList$counts)) DEG_S10_Ti_filtered < - DEG_S10_Ti$table[DEG_S10_Ti$table$FDR < 0.05 & abs(DEG_S10_Ti$table$logFC) > 1,] DEG_S10_Ti_filtered < - rownames_to_column(DEG_S10_Ti_filtered) %>% rename(gene_ID = rowname) print(head(DEG_S10_Ti_filtered)) print(\"Nr of differentially expressed genes:\") print(nrow(DEG_S10_Ti_filtered)) gene_ID logFC logCPM F PValue FDR 1 g39 2.057128 14.12463 246.6519 3.039491e-09 4.954370e-07 2 g232 3.261008 11.95044 203.4580 8.934889e-09 5.288546e-07 3 g269 -1.652505 11.83422 200.3564 9.733520e-09 5.288546e-07 4 g6 1.714849 12.99954 166.2297 2.740066e-08 1.116577e-06 5 g99 -1.484591 11.68100 150.3246 4.762148e-08 1.552460e-06 6 g26 2.274575 11.36683 142.8625 6.290957e-08 1.623160e-06 [1] \"Nr of differentially expressed genes:\" [1] 52 Plot the results from the 3 tests in a Venn diagram to visualize the number of common and unique genes In [22]: Copied! vd <- venn.diagram ( x = list ( DEG_S10_filtered $ gene_ID , DEG_Ti_filtered $ gene_ID , DEG_S10_Ti_filtered $ gene_ID ), category.names = c ( \"S10\" , \"Tienshan\" , \"S10_Tienshan\" ), lwd = 3 , fill = c ( \"cornflowerblue\" , \"yellowgreen\" , \"thistle3\" ), filename = NULL , cat.cex = 1 , cat.fontface = \"bold\" , output = TRUE ) grid.draw ( vd ) vd < - venn.diagram( x = list(DEG_S10_filtered$gene_ID, DEG_Ti_filtered$gene_ID, DEG_S10_Ti_filtered$gene_ID), category.names = c(\"S10\" , \"Tienshan\", \"S10_Tienshan\"), lwd = 3, fill = c(\"cornflowerblue\", \"yellowgreen\", \"thistle3\"), filename = NULL, cat.cex = 1, cat.fontface = \"bold\", output=TRUE ) grid.draw(vd) Explore DGE results \u00b6 Select the genes which appear only in the analysis using both genotypes for further examination. \u00b6 We can use the \"anti_join\" function from the dplyr package to keep only the unique genes that appear only when using both genotypes. In [23]: Copied! S10_Ti_unique <- anti_join ( DEG_S10_Ti_filtered , DEG_S10_filtered , by = \"gene_ID\" ) %>% anti_join ( DEG_Ti_filtered , by = \"gene_ID\" ) S10_Ti_unique S10_Ti_unique < - anti_join(DEG_S10_Ti_filtered, DEG_S10_filtered, by=\"gene_ID\") %>% anti_join(DEG_Ti_filtered, by=\"gene_ID\") S10_Ti_unique A data.frame: 2 \u00d7 6 gene_ID logFC logCPM F PValue FDR g247 -1.412652 8.424523 10.877350 0.006535994 0.01500517 g360 1.180849 7.900076 9.804067 0.013809376 0.02813660 Linking the genes selected as differentially expressed back to the raw read counts \u00b6 How do the counts look for these genes, does it make sense that they are differentially expressed only when using the two genotypes? In [24]: Copied! Read_counts <- rownames_to_column ( Read_counts ) %>% rename ( gene_ID = rowname ) S10_Ti_unique_counts <- inner_join ( S10_Ti_unique [, c ( 1 , 2 , 6 )], Read_counts , by = \"gene_ID\" ) S10_Ti_unique_counts Read_counts < - rownames_to_column(Read_counts) %>% rename(gene_ID = rowname) S10_Ti_unique_counts < - inner_join(S10_Ti_unique[,c(1,2,6)], Read_counts, by=\"gene_ID\") S10_Ti_unique_counts A data.frame: 2 \u00d7 15 gene_ID logFC FDR S10_1_1 S10_1_2 S10_1_3 S10_2_1 S10_2_2 S10_2_3 TI_1_1 TI_1_2 TI_1_3 TI_2_1 TI_2_2 TI_2_3 g247 -1.412652 0.01500517 4 9 3 1 2 1 42 27 32 8 22 22 g360 1.180849 0.02813660 1 0 1 3 2 3 16 10 16 22 19 22 Adding functional annotation to DEGs \u00b6 Until now we looked only at gene IDs, but we can also add functional annotations to the DEGs. The functional annotations were generated using protein sequences and the EggNOG software. Identifying the molecular function of the differentially expressed genes can help us do a literature survey in order to check if any of the genes discovered have been previously identified as being involved in the cold response. In [25]: Copied! Functional_annotations <- read.delim ( \"../Data/Clover_Data/Functional_Annotations.txt\" ) S10_Ti_unique_FA <- inner_join ( S10_Ti_unique [, c ( 1 , 2 , 6 )], Functional_annotations , by = \"gene_ID\" ) S10_Ti_unique_FA Functional_annotations < - read.delim(\"../Data/Clover_Data/Functional_Annotations.txt\") S10_Ti_unique_FA < - inner_join(S10_Ti_unique[,c(1,2,6)], Functional_annotations, by=\"gene_ID\") S10_Ti_unique_FA A data.frame: 2 \u00d7 4 gene_ID logFC FDR Human.Readable.Description g247 -1.412652 0.01500517 Nitrogen regulatory protein P-II homolog g360 1.180849 0.02813660 Unknown protein Tasks and Questions Based on the results obtained in the analysis so far, would you change the cut-off for the FDR and logFC to be more strict or more permissive? Look back at the raw counts for different FDR and logFC values and set the thresholds as you find appropriate. You can also plot histograms with the FDR and logFC values. In [ ]: Copied! hist ( DEG_S10 $ table $ FDR , main = \"\" , xlab = \"FDR\" , breaks = 200 , xlim = range ( c ( 0 , 0.1 ))) hist(DEG_S10$table$FDR , main = \"\", xlab = \"FDR\", breaks= 200, xlim = range(c(0, 0.1))) In [ ]: Copied! hist ( DEG_S10 $ table $ logFC , main = \"\" , xlab = \"logFC\" , breaks = 50 , xlim = range ( c ( -6 , 6 ))) hist(DEG_S10$table$logFC , main = \"\", xlab = \"logFC\", breaks= 50, xlim = range(c(-6, 6))) Separate the upregulated and downregulated genes for each genotype and append functional annotations to them. Identify the genes that are commonly upregulated in S10 and Tienshan samples and the uniquely upregulated genes for each genotype. Why do you think some of the proteins appear in duplicates? To answer these questions, it may be convenient to save summary tables from R and open them in excel. See the code below for examples of how to do this. Files can be downloaded by right-clicking on the file name. If you are familiar with R functions, you are welcome to use those for counting. In [ ]: Copied! dir.create ( \"DEG_Output_tables\" , showWarnings = FALSE ) dir.create(\"DEG_Output_tables\", showWarnings = FALSE) In [ ]: Copied! #Create the table with the DEGs, Raw counts(just for the Ti samples in this case, change to columns (2:7) for the S10 samples) and Functional annotations DEG_Ti_counts_FA <- inner_join ( DEG_Ti_filtered [, c ( 1 , 2 , 6 )], Read_counts [, c ( 1 , 8 : 13 )], by = \"gene_ID\" ) %>% inner_join ( Functional_annotations , by = \"gene_ID\" ) #Write the table to file write.table ( DEG_Ti_counts_FA , file = \"DEG_Output_tables/Ti_Treatment_Control_DGE.txt\" , quote = FALSE , row.names = FALSE , sep = \"\\t\" ) #Display the first 10 rows of the table head ( DEG_Ti_counts_FA , n = 10 ) #Create the table with the DEGs, Raw counts(just for the Ti samples in this case, change to columns (2:7) for the S10 samples) and Functional annotations DEG_Ti_counts_FA < - inner_join(DEG_Ti_filtered[,c(1, 2, 6)], Read_counts[, c(1, 8:13)], by=\"gene_ID\") %>% inner_join(Functional_annotations, by=\"gene_ID\") #Write the table to file write.table(DEG_Ti_counts_FA, file = \"DEG_Output_tables/Ti_Treatment_Control_DGE.txt\", quote = FALSE, row.names = FALSE, sep = \"\\t\") #Display the first 10 rows of the table head(DEG_Ti_counts_FA, n=10) Example for filtering and counting the Up/Down genes. You can easily filter using the \"filter()\" function just by specifying the dataframe and a logical argument. In [ ]: Copied! DEG_Ti_up <- filter ( DEG_Ti_counts_FA , logFC > 0 ) print ( \"Nr of upregulated genes:\" ) print ( nrow ( DEG_Ti_up )) DEG_Ti_down <- filter ( DEG_Ti_counts_FA , logFC < 0 ) print ( \"Nr of downregulated genes:\" ) print ( nrow ( DEG_Ti_down )) DEG_Ti_up < - filter(DEG_Ti_counts_FA, logFC > 0) print(\"Nr of upregulated genes:\") print(nrow(DEG_Ti_up)) DEG_Ti_down < - filter(DEG_Ti_counts_FA, logFC < 0) print(\"Nr of downregulated genes:\") print(nrow(DEG_Ti_down)) You can use the \"inner_join\" and the \"anti_join\" functions from the dplyr package to select the common and unique genes for each genotype: example_file_joined <- inner_join(file1, file2, by=\"gene_ID\") Wrapping up \ud83c\udf89 \ud83c\udf89 \ud83c\udf89 \u00b6 In this notebook, you have worked with RNA-seq results for two white clover genotypes exposed to one night of cold treatment, aiming to identify genes that change their expression in response to the cold treatment. You have learned to perform exploratory data analysis of raw and normalized RNA-seq read counts. You have also performed differential gene expression using edgeR .","title":"3.3.Bulk RNA data analysis"},{"location":"nb/bulk.html#edger-data-filtering-and-normalization","text":"We will use the edgeR package to test for differentially expressed genes between our Control and Treatment Samples. You can read more about the edgeR package and its functionalities here . This exercise largely follows their user manual.","title":"EdgeR - Data filtering and Normalization"},{"location":"nb/bulk.html#file-processing","text":"The data for this exercise comes from the 12 tabular files with Reads per Gene counts generated by STAR Mapping in the raw-data alignment part of this course. We want to create a table where each column is a sample, and the content of the table are the read counts from STAR. We must merge the 12 files with Reads per Gene information into a single file. If you aligned datasets in the first notebook with jupyterlab, then you will find the files using the following command: In [2]: Copied! samples <- sort ( system ( \"find results/STAR_output/*_align_contigs_1_2 -name \\\"*ReadsPerGene.out.tab\\\"\" , intern = TRUE )) print ( samples ) Read_counts <- do.call ( cbind , lapply ( samples , function ( x ) read.delim ( file = x , header = FALSE ))) samples < - sort(system(\"find results/STAR_output/*_align_contigs_1_2 -name \\\"*ReadsPerGene.out.tab\\\"\", intern=TRUE)) print(samples) Read_counts < - do.call(cbind, lapply(samples, function(x) read.delim(file=x, header = FALSE))) [1] \"results/STAR_output/S10_align_contigs_1_2/S10_1_1ReadsPerGene.out.tab\" [2] \"results/STAR_output/S10_align_contigs_1_2/S10_1_2ReadsPerGene.out.tab\" [3] \"results/STAR_output/S10_align_contigs_1_2/S10_1_3ReadsPerGene.out.tab\" [4] \"results/STAR_output/S10_align_contigs_1_2/S10_2_1ReadsPerGene.out.tab\" [5] \"results/STAR_output/S10_align_contigs_1_2/S10_2_2ReadsPerGene.out.tab\" [6] \"results/STAR_output/S10_align_contigs_1_2/S10_2_3ReadsPerGene.out.tab\" [7] \"results/STAR_output/TI_align_contigs_1_2/TI_1_1ReadsPerGene.out.tab\" [8] \"results/STAR_output/TI_align_contigs_1_2/TI_1_2ReadsPerGene.out.tab\" [9] \"results/STAR_output/TI_align_contigs_1_2/TI_1_3ReadsPerGene.out.tab\" [10] \"results/STAR_output/TI_align_contigs_1_2/TI_2_1ReadsPerGene.out.tab\" [11] \"results/STAR_output/TI_align_contigs_1_2/TI_2_2ReadsPerGene.out.tab\" [12] \"results/STAR_output/TI_align_contigs_1_2/TI_2_3ReadsPerGene.out.tab\" If you aligned datasets interactively with Galaxy , then you will need to create the folder tabular_files into the results folder copy the tabular files from STAR into the created folder. Each file must have the sample name and end by ReadsPerGene.out.tab . For example S10_1_1ReadsPerGene.out.tab run the following commands removing the # symbol In [ ]: Copied! #samples <- sort(system(\"find results/tabular_files/ -name \\\"*ReadsPerGene.out.tab\\\"\", intern=TRUE)) #print(samples) #Read_counts <- do.call(cbind, lapply(samples, function(x) read.delim(file=x, header = FALSE))) #samples < - sort(system(\"find results/tabular_files/ -name \\\"*ReadsPerGene.out.tab\\\"\", intern=TRUE)) #print(samples) #Read_counts < - do.call(cbind, lapply(samples, function(x) read.delim(file=x, header = FALSE))) The data frame has genes as rows and samples as columns and stores the gene expression counts (value representing the total number of sequence reads that originated from a particular gene in a sample) for each of the 12 samples. This data frame should have 12 columns and 366 rows. In [3]: Copied! rownames ( Read_counts ) <- Read_counts [, 1 ] Read_counts <- Read_counts [ c ( 5 : nrow ( Read_counts )), c ( seq ( 2 , 46 , by = 4 ))] colnames ( Read_counts ) <- c ( \"S10_1_1\" , \"S10_1_2\" , \"S10_1_3\" , \"S10_2_1\" , \"S10_2_2\" , \"S10_2_3\" , \"TI_1_1\" , \"TI_1_2\" , \"TI_1_3\" , \"TI_2_1\" , \"TI_2_2\" , \"TI_2_3\" ) head ( Read_counts , n = 10 ) dim ( Read_counts ) # dimensions of the data frame rownames(Read_counts) < - Read_counts[,1] Read_counts < - Read_counts[c(5:nrow(Read_counts)), c(seq(2, 46, by=4))] colnames(Read_counts) < - c(\"S10_1_1\", \"S10_1_2\", \"S10_1_3\", \"S10_2_1\", \"S10_2_2\", \"S10_2_3\", \"TI_1_1\", \"TI_1_2\", \"TI_1_3\", \"TI_2_1\", \"TI_2_2\", \"TI_2_3\") head(Read_counts, n=10) dim(Read_counts) # dimensions of the data frame A data.frame: 10 \u00d7 12 S10_1_1 S10_1_2 S10_1_3 S10_2_1 S10_2_2 S10_2_3 TI_1_1 TI_1_2 TI_1_3 TI_2_1 TI_2_2 TI_2_3 g175 143 226 227 217 206 193 234 205 198 221 206 165 g176 17 12 15 8 14 18 29 19 30 6 11 23 g177 0 0 0 0 0 0 0 0 0 0 0 0 g178 0 0 0 0 0 0 0 0 2 2 1 2 g179 0 0 0 0 0 0 0 0 0 0 0 0 g180 2773 5223 4350 4139 3209 4644 1015 1313 889 2675 1023 2565 g181 473 679 583 437 390 450 642 700 697 650 459 410 g182 19 15 20 9 8 4 13 7 8 6 5 4 g183 0 0 0 0 0 0 0 0 0 0 0 0 g184 30 22 50 57 103 83 36 29 18 107 69 89 .list-inline {list-style: none; margin:0; padding: 0} .list-inline>li {display: inline-block} .list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex} 366 12 Import the Metadata table. This file, as its name suggests, contains information about each of the 12 RNA-seq samples, such as the treatment (Condition), genotype and replicate. Note that the order of the rows in the Metadata table should be the same as the columns in the Read_counts file generated above. In [4]: Copied! metadata <- read.csv ( \"../Data/Clover_Data/metadata.csv\" , sep = \";\" , row.names = 1 , stringsAsFactors = TRUE ) metadata metadata < - read.csv(\"../Data/Clover_Data/metadata.csv\", sep =\";\", row.names=1, stringsAsFactors=TRUE) metadata A data.frame: 12 \u00d7 3 Condition Genotype Replicate S10_1_1 Control S10 1 S10_1_2 Control S10 2 S10_1_3 Control S10 3 S10_2_1 Treatment S10 1 S10_2_2 Treatment S10 2 S10_2_3 Treatment S10 3 TI_1_1 Control Tienshan 1 TI_1_2 Control Tienshan 2 TI_1_3 Control Tienshan 3 TI_2_1 Treatment Tienshan 1 TI_2_2 Treatment Tienshan 2 TI_2_3 Treatment Tienshan 3 In order to aid the following steps, we will create a Group for each sample (a new column in the metadata) based on the Genotype&Condition of each sample and assign the three replicates to this group. In [5]: Copied! Group <- factor ( paste ( metadata $ Genotype , metadata $ Condition , sep = \"_\" )) metadata <- cbind ( metadata , Group = Group ) metadata Group < - factor(paste(metadata$Genotype, metadata$Condition, sep=\"_\")) metadata < - cbind(metadata,Group=Group) metadata A data.frame: 12 \u00d7 4 Condition Genotype Replicate Group S10_1_1 Control S10 1 S10_Control S10_1_2 Control S10 2 S10_Control S10_1_3 Control S10 3 S10_Control S10_2_1 Treatment S10 1 S10_Treatment S10_2_2 Treatment S10 2 S10_Treatment S10_2_3 Treatment S10 3 S10_Treatment TI_1_1 Control Tienshan 1 Tienshan_Control TI_1_2 Control Tienshan 2 Tienshan_Control TI_1_3 Control Tienshan 3 Tienshan_Control TI_2_1 Treatment Tienshan 1 Tienshan_Treatment TI_2_2 Treatment Tienshan 2 Tienshan_Treatment TI_2_3 Treatment Tienshan 3 Tienshan_Treatment","title":"File processing"},{"location":"nb/bulk.html#create-the-dgelist-object","text":"We will merge the read counts and the metadata into a list-based data object named DGEList, which can be manipulated as any list object in R. The main components of the DGEList object are the matrix \"counts\" containing our read per gene counts and a data.frame \"samples\" containing the metadata. Note that all the genes with zero counts across all samples were eliminated. In [6]: Copied! DGEList <- DGEList ( Read_counts , remove.zeros = TRUE ) DGEList $ samples $ Condition <- relevel ( metadata $ Condition , ref = \"Control\" ) DGEList $ samples $ Genotype <- metadata $ Genotype DGEList $ samples $ group <- metadata $ Group DGEList DGEList < - DGEList(Read_counts, remove.zeros = TRUE) DGEList$samples$Condition < - relevel(metadata$Condition, ref = \"Control\") DGEList$samples$Genotype < - metadata$Genotype DGEList$samples$group < - metadata$Group DGEList Removing 99 rows with all zero counts $counts A matrix: 267 \u00d7 12 of type int S10_1_1 S10_1_2 S10_1_3 S10_2_1 S10_2_2 S10_2_3 TI_1_1 TI_1_2 TI_1_3 TI_2_1 TI_2_2 TI_2_3 g175 143 226 227 217 206 193 234 205 198 221 206 165 g176 17 12 15 8 14 18 29 19 30 6 11 23 g178 0 0 0 0 0 0 0 0 2 2 1 2 g180 2773 5223 4350 4139 3209 4644 1015 1313 889 2675 1023 2565 g181 473 679 583 437 390 450 642 700 697 650 459 410 g182 19 15 20 9 8 4 13 7 8 6 5 4 g184 30 22 50 57 103 83 36 29 18 107 69 89 g185 0 2 0 0 0 0 0 1 0 0 0 1 g186 1 0 0 0 0 0 0 0 0 0 0 0 g187 1 1 2 0 1 1 58 49 57 28 32 29 g188 316 285 417 200 184 322 1276 855 1341 402 809 547 g190 14 18 29 25 17 31 7 6 12 20 7 6 g191 2 2 1 1 0 0 0 0 0 0 0 0 g192 1 1 1 1 1 0 6 10 2 3 8 2 g193 208 231 301 289 161 279 70 61 60 128 160 91 g194 40 41 37 44 17 43 8 10 7 8 105 25 g195 54 85 85 71 54 43 56 54 42 67 68 56 g196 77 120 118 186 152 177 5 9 5 42 20 34 g197 1 6 5 18 14 21 15 44 12 32 16 17 g198 14 27 10 11 2 2 6 6 1 2 2 0 g199 0 0 0 0 0 0 0 0 0 0 1 0 g200 57 80 94 11 8 23 89 47 67 5 47 5 g203 815 1188 1412 1139 1034 1127 911 852 662 892 781 784 g204 250 312 348 277 275 264 125 104 133 119 91 130 g205 109 132 181 140 114 151 89 73 66 78 46 50 g206 0 1 2 7 4 9 4 0 7 0 1 2 g207 0 1 0 0 4 3 1 3 4 4 3 3 g208 0 1 0 0 0 0 0 1 0 1 0 0 g209 0 0 0 0 0 0 0 0 1 0 0 0 g210 52 76 60 12 12 13 61 73 94 54 45 21 \u22ee \u22ee \u22ee \u22ee \u22ee \u22ee \u22ee \u22ee \u22ee \u22ee \u22ee \u22ee \u22ee g135 0 0 0 1 0 0 5 1 0 2 0 0 g139 0 1 1 0 0 0 12 13 11 4 3 3 g140 0 5 1 0 0 2 5 4 0 0 2 0 g141 177 217 224 131 117 166 117 85 67 80 79 77 g142 488 713 557 241 154 228 983 1057 1023 1075 794 933 g144 3319 3987 4763 2249 1996 2260 4405 3880 4585 5072 3618 3590 g146 293 330 350 913 775 731 108 92 115 431 151 226 g147 6 3 3 0 8 10 1 1 1 0 1 0 g148 19 30 24 15 4 12 23 19 25 10 19 8 g149 35 62 65 57 58 53 114 103 91 92 79 93 g150 263 411 278 752 1378 1300 106 112 70 516 200 508 g151 44 44 50 92 92 84 23 26 25 56 43 31 g152 242 258 294 425 381 521 15 8 8 16 21 15 g153 142 144 181 233 177 233 31 49 50 50 45 57 g155 0 4 2 1 0 0 1 0 3 0 0 1 g156 0 0 0 0 0 0 0 3 2 0 0 1 g157 6 1 3 223 52 152 4 7 4 153 47 155 g158 0 0 0 7 1 3 0 0 1 1 0 0 g161 25 38 18 1 9 2 63 23 43 0 24 6 g162 31 43 38 43 25 28 0 0 0 0 0 0 g163 1 0 12 37 8 18 9 4 8 16 13 15 g164 69 58 97 294 123 168 56 58 60 146 101 135 g165 203 376 594 1343 2062 1610 265 229 214 488 270 490 g167 2 0 0 2 0 0 0 0 0 0 0 0 g168 2 14 6 0 0 5 31 26 38 3 27 10 g169 0 0 1 0 1 1 0 0 0 0 0 0 g170 0 0 0 0 0 0 0 1 0 0 0 0 g171 0 5 0 0 0 5 0 0 0 1 0 0 g172 1 2 2 6 4 3 3 1 2 3 3 0 g173 80 107 120 78 138 121 67 86 69 96 105 105 $samples A data.frame: 12 \u00d7 5 group lib.size norm.factors Condition Genotype S10_1_1 S10_Control 43614 1 Control S10 S10_1_2 S10_Control 58803 1 Control S10 S10_1_3 S10_Control 62654 1 Control S10 S10_2_1 S10_Treatment 59909 1 Treatment S10 S10_2_2 S10_Treatment 56761 1 Treatment S10 S10_2_3 S10_Treatment 63152 1 Treatment S10 TI_1_1 Tienshan_Control 43387 1 Control Tienshan TI_1_2 Tienshan_Control 38355 1 Control Tienshan TI_1_3 Tienshan_Control 40304 1 Control Tienshan TI_2_1 Tienshan_Treatment 55509 1 Treatment Tienshan TI_2_2 Tienshan_Treatment 39909 1 Treatment Tienshan TI_2_3 Tienshan_Treatment 45008 1 Treatment Tienshan","title":"Create the DGEList object"},{"location":"nb/bulk.html#preliminary-data-analysis","text":"First, we will calculate the \"pseudoCounts\" as log2 of the reads per gene counts. This is not part of the actual differential gene expression analysis but is helpful for data exploration and quality assessment. We will look at a histogram of one of the samples and a boxplot representation of the log2 counts for all the 12 samples. Note that there are many genes with a low number of mapped reads and that there are differences between the average read counts for each library. In [7]: Copied! pseudoCounts <- log2 ( DGEList $ counts + 1 ) head ( pseudoCounts ) hist ( pseudoCounts [ , \"S10_1_1\" ], main = \"\" , xlab = \"Read counts\" ) boxplot ( pseudoCounts , col = \"gray\" , las = 3 , cex.names = 1 ) pseudoCounts < - log2(DGEList$counts + 1) head(pseudoCounts) hist(pseudoCounts[ ,\"S10_1_1\"], main = \"\", xlab = \"Read counts\") boxplot(pseudoCounts, col = \"gray\", las = 3, cex.names = 1) A matrix: 6 \u00d7 12 of type dbl S10_1_1 S10_1_2 S10_1_3 S10_2_1 S10_2_2 S10_2_3 TI_1_1 TI_1_2 TI_1_3 TI_2_1 TI_2_2 TI_2_3 g175 7.169925 7.826548 7.832890 7.768184 7.693487 7.599913 7.876517 7.686501 7.636625 7.794416 7.693487 7.375039 g176 4.169925 3.700440 4.000000 3.169925 3.906891 4.247928 4.906891 4.321928 4.954196 2.807355 3.584963 4.584963 g178 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 1.584963 1.584963 1.000000 1.584963 g180 11.437752 12.350939 12.087131 12.015415 11.648358 12.181463 9.988685 10.359750 9.797662 11.385862 10.000000 11.325305 g181 8.888743 9.409391 9.189825 8.774787 8.611025 8.816984 9.328675 9.453271 9.447083 9.346514 8.845490 8.682995 g182 4.321928 4.000000 4.392317 3.321928 3.169925 2.321928 3.807355 3.000000 3.169925 2.807355 2.584963 2.321928 We can also create a PCA plot of the samples in order to assess the differences between the Genotypes and Conditions, but also between the replicates. In this plot the samples that are similar cluster together, while samples that are different are further apart. In this type of plot, we would expect samples from the same group (the three replicates for each sample) to exhibit a similar gene expression profile thus clustering together while being separated from the other samples. In [8]: Copied! resPCA <- pca ( t ( pseudoCounts ), ncomp = 6 ) plotIndiv ( resPCA , group = metadata $ Genotype , pch = metadata $ Condition , legend = T , legend.title = 'Genotype' , legend.title.pch = 'Condition' , title = 'PCA plot raw counts' , style = 'ggplot2' , size.xlabel = 10 , size.ylabel = 10 ) resPCA < - pca(t(pseudoCounts), ncomp = 6) plotIndiv(resPCA, group = metadata$Genotype, pch=metadata$Condition, legend = T, legend.title = 'Genotype', legend.title.pch = 'Condition', title = 'PCA plot raw counts', style = 'ggplot2', size.xlabel = 10, size.ylabel = 10)","title":"Preliminary data analysis"},{"location":"nb/bulk.html#filtering-the-lowly-expressed-genes","text":"As seen previously, many genes have a low number of read counts in our samples. The genes with very low counts across all libraries provide little evidence for differential expression, thus we should eliminate these genes before the analysis. One of the filtering methods we can use is the \"filterByExpr\" function provided by the edgeR package. By default, this function keeps only the genes that have at least 10 reads per group, but other cutoffs can also be applied. In [9]: Copied! keep <- filterByExpr ( DGEList , group = metadata $ Group ) #create the filter DGEList <- DGEList [ keep , , keep.lib.sizes = FALSE ] #apply the filter to on the DGEList object table ( keep ) #Check the number of genes that passed the filter keep < - filterByExpr(DGEList, group=metadata$Group) #create the filter DGEList < - DGEList[keep, , keep.lib.sizes=FALSE] #apply the filter to on the DGEList object table(keep) #Check the number of genes that passed the filter keep FALSE TRUE 104 163","title":"Filtering the lowly expressed genes"},{"location":"nb/bulk.html#normalization","text":"As we are working with multiple samples we need to normalize the read counts per gene in order to account for compositional and technical differences between the 12 RNA-seq libraries. For this, we will calculate normalization factors using the trimmed mean of M-values (TMM) method. You can read more about different normalization methods in the user manual. Note that running \"calcNormFactors\" does not change the actual reads per gene counts, it just fills the \"norm.factors\" column in DGEList$samples . These factors will be used to scale the read counts for each library. From the user guide: \"A normalization factor below one indicates that a small number of high count genes are monopolizing the sequencing, causing the counts for other genes to be lower than would be usual given the library size.\" In [10]: Copied! DGEList <- calcNormFactors ( DGEList , method = \"RLE\" ) DGEList $ samples DGEList < - calcNormFactors(DGEList, method=\"RLE\") DGEList$samples A data.frame: 12 \u00d7 5 group lib.size norm.factors Condition Genotype S10_1_1 S10_Control 43506 1.0897129 Control S10 S10_1_2 S10_Control 58660 1.0385093 Control S10 S10_1_3 S10_Control 62512 1.0362962 Control S10 S10_2_1 S10_Treatment 59741 0.9802860 Treatment S10 S10_2_2 S10_Treatment 56653 0.8418644 Treatment S10 S10_2_3 S10_Treatment 63008 0.8949144 Treatment S10 TI_1_1 Tienshan_Control 43174 1.0618440 Control Tienshan TI_1_2 Tienshan_Control 38203 1.0431678 Control Tienshan TI_1_3 Tienshan_Control 40178 1.0676351 Control Tienshan TI_2_1 Tienshan_Treatment 55391 0.9048196 Treatment Tienshan TI_2_2 Tienshan_Treatment 39810 1.1235077 Treatment Tienshan TI_2_3 Tienshan_Treatment 44910 0.9603761 Treatment Tienshan","title":"Normalization"},{"location":"nb/bulk.html#normalized-counts-exploratory-data-analysis","text":"For data analysis purposes normalized log2 counts can be extracted from the DGEList object using the function CPM (counts per million). We will generate the same plots as for the raw counts in order to compare the data before and after normalization. Do the plots for normalized counts look different compared with the plots computed before data filtering and normalization? In [11]: Copied! pseudoNormCounts <- cpm ( DGEList , log = TRUE , prior.count = 1 ) head ( pseudoNormCounts ) hist ( pseudoNormCounts [ , \"S10_1_1\" ], main = \"\" , xlab = \"counts\" ) boxplot ( pseudoNormCounts , col = \"gray\" , las = 3 , cex.names = 1 ) resPCA <- pca ( t ( pseudoNormCounts ), ncomp = 6 ) plotIndiv ( resPCA , group = metadata $ Genotype , pch = metadata $ Condition , legend = T , legend.title = 'Genotype' , legend.title.pch = 'Condition' , title = 'PCA plot normalized counts' , style = 'ggplot2' , size.xlabel = 10 , size.ylabel = 10 ) pseudoNormCounts < - cpm(DGEList, log = TRUE, prior.count = 1) head(pseudoNormCounts) hist(pseudoNormCounts[ ,\"S10_1_1\"], main = \"\", xlab = \"counts\") boxplot(pseudoNormCounts, col = \"gray\", las = 3, cex.names = 1) resPCA < - pca(t(pseudoNormCounts), ncomp = 6) plotIndiv(resPCA, group = metadata$Genotype, pch=metadata$Condition, legend = T, legend.title = 'Genotype', legend.title.pch = 'Condition', title = 'PCA plot normalized counts', style = 'ggplot2', size.xlabel = 10, size.ylabel = 10) A matrix: 6 \u00d7 12 of type dbl S10_1_1 S10_1_2 S10_1_3 S10_2_1 S10_2_2 S10_2_3 TI_1_1 TI_1_2 TI_1_3 TI_2_1 TI_2_2 TI_2_3 g175 11.568005 11.864813 11.782957 11.863090 12.083127 11.749272 12.323049 12.334198 12.178544 12.112851 12.175386 11.908906 g176 8.564108 7.760868 7.974199 7.290213 8.292080 8.405708 9.349782 8.956131 9.490388 7.125494 8.054467 9.111569 g180 15.836359 16.387906 16.035457 16.109276 16.038322 16.329943 14.435626 15.008665 14.340400 15.704307 14.482504 15.860317 g181 13.287211 13.446752 13.138771 12.869139 13.000828 12.965820 13.775545 14.101988 13.989757 13.664955 13.327806 13.217591 g182 8.716557 8.056048 8.360376 7.439590 7.551908 6.505541 8.245462 7.611500 7.689402 7.125494 7.041192 6.815835 g184 9.350249 8.573811 9.628852 9.955925 11.089735 10.542877 9.653135 9.546090 8.779819 11.073313 10.609695 11.024686","title":"Normalized counts - Exploratory Data analysis"},{"location":"nb/bulk.html#edger-testing-for-differentially-expressed-genes-degs","text":"We use edgeR for Differential Gene Expression using the GLM method (Generalized linear model) with treatment and genotype effects After we concluded the exploratory data analysis and the filtering and normalization steps we can now start testing for differentially expressed genes between our samples. For this, we first need to define a \"design matrix\" which describes our experimental design. We construct a matrix containing samples as rows and our previously defined groups (S10_Control, S10_Treatment, Tienshan_Control, Tienshan_Treatment) as columns. The samples belonging to each group are assigned a \"1\" in this matrix. In [12]: Copied! design.matrix <- model.matrix ( ~ 0 + Group ) rownames ( design.matrix ) <- colnames ( DGEList ) colnames ( design.matrix ) <- levels ( metadata $ Group ) design.matrix design.matrix < - model.matrix(~0+Group) rownames(design.matrix) < - colnames(DGEList) colnames(design.matrix) < - levels(metadata$Group) design.matrix A matrix: 12 \u00d7 4 of type dbl S10_Control S10_Treatment Tienshan_Control Tienshan_Treatment S10_1_1 1 0 0 0 S10_1_2 1 0 0 0 S10_1_3 1 0 0 0 S10_2_1 0 1 0 0 S10_2_2 0 1 0 0 S10_2_3 0 1 0 0 TI_1_1 0 0 1 0 TI_1_2 0 0 1 0 TI_1_3 0 0 1 0 TI_2_1 0 0 0 1 TI_2_2 0 0 0 1 TI_2_3 0 0 0 1 For easier pairwise comparisons between the groups we can create some \"contrasts\" using the \"makeContrasts\" function. This will tell the program which samples we will like to consider as our \"Control\" and which as our \"Treatment\" groups. The Control groups will be assigned a \"-1\" and the Treatment groups a \"1\". When using multiple Control and Treatment samples in the same contrast we need to devide by the number of samples used as for example in the contrast S10_Tienshan, where we use both S10 and Ti samples. For example, the contrast S10 = S10_Treatment - S10_Control will test for differentially expressed genes between the S10 treatment samples vs the S10 Control samples. In [13]: Copied! contrasts <- makeContrasts ( S10 = S10_Treatment - S10_Control , Tienshan = Tienshan_Treatment - Tienshan_Control , S10_Tienshan = ( S10_Treatment + Tienshan_Treatment ) / 2 - ( S10_Control + Tienshan_Control ) / 2 , levels = design.matrix ) contrasts contrasts < - makeContrasts( S10 = S10_Treatment-S10_Control, Tienshan = Tienshan_Treatment-Tienshan_Control, S10_Tienshan= (S10_Treatment+Tienshan_Treatment)/2 - (S10_Control+Tienshan_Control)/2, levels=design.matrix) contrasts A matrix: 4 \u00d7 3 of type dbl S10 Tienshan S10_Tienshan S10_Control -1 0 -0.5 S10_Treatment 1 0 0.5 Tienshan_Control 0 -1 -0.5 Tienshan_Treatment 0 1 0.5 Any ideas for other contrasts that migth be interesting to explore? The following cell will run two mandatory steps in order to fit a model in edgeR. First, the dispersion needs to be estimated, this is a measure of the degree of inter-library variation for each gene. Afterwards, a QL ( quasi-likelihood) model representing the sample design is fitted using the \"glmQLFit\" function. You can read more about these steps in the edgeR user guide. In [14]: Copied! DGEList <- estimateDisp ( DGEList , design.matrix ) fit <- glmQLFit ( DGEList , design.matrix ) DGEList < - estimateDisp(DGEList, design.matrix) fit < - glmQLFit(DGEList, design.matrix)","title":"EdgeR - Testing for Differentially expressed genes (DEGs)"},{"location":"nb/bulk.html#degs-for-white-clover-s10-plants-treatment-vs-control","text":"Find DEGs for White clover S10 plants in Treatment condition compared with the Control condition We will test for differentially expressed genes using the quasi-likelihood F-tests method. This is easily done just by running the \"glmQFTest\" function on the fit model and selecting one of the contrasts we created above (in this case the \"S10\" contrast). Next, we will use the topTags function to extract the information about all the genes. In [15]: Copied! glmqlf_S10 <- glmQLFTest ( fit , contrast = contrasts [, \"S10\" ]) DEG_S10 <- topTags ( glmqlf_S10 , n = nrow ( DGEList $ counts )) DEG_S10 glmqlf_S10 < - glmQLFTest(fit, contrast=contrasts[,\"S10\"]) DEG_S10 < - topTags(glmqlf_S10, n = nrow(DGEList$counts)) DEG_S10 $table A data.frame: 163 \u00d7 5 logFC logCPM F PValue FDR g39 3.5861702 14.124630 336.66158 5.227331e-10 8.520550e-08 g232 4.0494125 11.950441 157.50997 3.685988e-08 2.591779e-06 g6 2.3225347 12.999541 150.27854 4.770146e-08 2.591779e-06 g350 2.0530029 13.324566 97.56453 4.890245e-07 1.992775e-05 g99 -1.5387874 11.681004 85.17478 9.976659e-07 3.195090e-05 g142 -1.4235276 13.830692 82.52308 1.176107e-06 3.195090e-05 g240 1.3603712 14.708794 79.98114 1.383201e-06 3.220882e-05 g269 -1.4191681 11.834217 75.66563 1.841279e-06 3.751606e-05 g26 2.1254241 11.366827 68.55898 3.047273e-06 5.518949e-05 g129 -5.3324794 8.679594 60.45459 5.738115e-06 9.353127e-05 g272 3.1405978 13.860652 59.08187 6.432977e-06 9.532503e-05 g157 5.3813801 10.414972 57.03712 7.659515e-06 1.040417e-04 g42 -1.9856547 9.962762 53.97164 1.005146e-05 1.260298e-04 g165 2.2666761 13.690016 51.66777 1.243606e-05 1.447913e-04 g101 3.1488142 13.181625 48.21727 1.736838e-05 1.887363e-04 g15 2.7410696 10.684840 47.29143 1.906062e-05 1.941800e-04 g210 -2.2430064 9.993002 46.52627 2.060621e-05 1.975772e-04 g299 -2.3509276 10.984491 45.75893 2.230566e-05 2.019901e-04 g103 2.6810410 10.579777 44.26963 2.609599e-05 2.238762e-04 g144 -0.7963477 16.186987 43.17707 2.936057e-05 2.392886e-04 g77 1.0984806 14.118231 41.46740 3.548119e-05 2.754016e-04 g125 -1.3099534 10.195970 39.85537 4.266319e-05 3.160955e-04 g298 -2.0471647 9.837072 33.49832 9.395967e-05 6.658881e-04 g104 2.4081173 9.219160 38.54240 1.121281e-04 7.615370e-04 g122 -0.6485441 12.457152 30.90261 1.341148e-04 8.744287e-04 g164 1.4415064 11.156457 30.50383 1.419308e-04 8.897970e-04 g146 1.3983889 12.824184 29.92911 1.541544e-04 9.306356e-04 g313 2.2492040 13.614815 28.41728 1.926822e-04 1.121686e-03 g150 1.9650641 13.229706 27.90020 2.083770e-04 1.171222e-03 g13 -0.6522187 13.101579 26.45593 2.608176e-04 1.417109e-03 \u22ee \u22ee \u22ee \u22ee \u22ee \u22ee g96 -1.873792302 8.939397 0.4844512655 0.4999546 0.6041584 g10 0.233134670 14.382992 0.4834790913 0.5003766 0.6041584 g253 0.052426914 13.741219 0.3075042687 0.5896191 0.7066759 g289 -0.212573283 9.390864 0.2887857862 0.6010328 0.7150974 g312 0.093543041 10.849110 0.2666731965 0.6151559 0.7265972 g301 -0.102000599 10.135184 0.2572520652 0.6214023 0.7274503 g221 -0.277095720 8.469909 0.2522121463 0.6248039 0.7274503 g162 -0.145155616 8.517263 0.2278112475 0.6463283 0.7413085 g50 -0.170907352 8.749274 0.2158314533 0.6507284 0.7413085 g203 0.062836598 14.227601 0.2124612164 0.6532634 0.7413085 g88 0.045949497 14.235850 0.2103049178 0.6548983 0.7413085 g355 -0.287897714 8.890754 0.1803234432 0.6787703 0.7630314 g204 -0.055975779 11.947887 0.1408988543 0.7140799 0.7972262 g306 0.054269738 11.294732 0.0923937800 0.7664733 0.8498990 g193 0.049946343 11.682386 0.0642557849 0.8042743 0.8857886 g205 0.037877227 10.986961 0.0473457745 0.8314821 0.9029713 g194 -0.142990391 9.403497 0.0447552162 0.8360811 0.9029713 g362 0.025629967 11.336529 0.0415198735 0.8420254 0.9029713 g315 0.027958374 11.878331 0.0409766489 0.8430470 0.9029713 g180 0.059680256 15.711409 0.0386140448 0.8475743 0.9029713 g282 -0.026492114 11.756078 0.0247991469 0.8775434 0.9288284 g176 -0.061055115 8.587203 0.0144510429 0.9063467 0.9531258 g126 0.061707217 8.311212 0.0024786868 0.9648334 1.0000000 g128 0.063825269 7.671043 0.0019414812 0.9655950 1.0000000 g227 -0.025740630 7.939955 0.0011908008 0.9730516 1.0000000 g121 0.002660852 12.896620 0.0005233398 0.9821327 1.0000000 g284 0.000000000 8.538720 0.0000000000 1.0000000 1.0000000 g116 0.000000000 7.180297 0.0000000000 1.0000000 1.0000000 g117 0.000000000 7.033307 0.0000000000 1.0000000 1.0000000 g118 0.000000000 7.515646 0.0000000000 1.0000000 1.0000000 $adjust.method 'BH' $comparison '-1*S10_Control 1*S10_Treatment' $test 'glm' We have now created a table with certain parameters calculated for each of the genes analysed. logFC represents the base 2 logarithm of the fold change and it shows us how much the expression of the gene has changed between the two conditions. A logFC of 1 means a doubling in the read per gene count between the control and treatment samples. The genes with a logFC higher than 0 are upregulated while the genes with a logFC lower than 0 are downregulated between the control and treatment samples. logCPM represents the average log2-counts-per-million, the abundance of the gene. F - F-statistic. PValue is the raw p-value. FDR (The false discovery rate) represents the adjusted p-value and is calculated using Benjamini and Hochberg's algorithm. It controls the rate of false positive values under multiple testing. Usually, a threshold of under 5% is set for the FDR. The important information for us in this table is stored in the \"logFC\" and the \"FDR\" columns. The top DE genes have small FDR values and large fold changes Many of the genes in the samples are uninteresting for us, as they have a high FDR and/or low logFC values so we cannot consider them as differentially expressed. We will apply a filtering step in order to keep only the statistically significant genes. We will filter out the genes with an FDR higher than 0.05 and an absolute logFC lower than 1. In [16]: Copied! DEG_S10_filtered <- DEG_S10 $ table [ DEG_S10 $ table $ FDR < 0.05 & abs ( DEG_S10 $ table $ logFC ) > 1 ,] #Filtering DEG_S10_filtered <- rownames_to_column ( DEG_S10_filtered ) %>% rename ( gene_ID = rowname ) #Adding the gene_ID column head ( DEG_S10_filtered ) nrow ( DEG_S10_filtered ) # finding the number of genes that passed the filter DEG_S10_filtered < - DEG_S10$table[DEG_S10$table$FDR < 0.05 & abs(DEG_S10$table$logFC) > 1,] #Filtering DEG_S10_filtered < - rownames_to_column(DEG_S10_filtered) %>% rename(gene_ID = rowname) #Adding the gene_ID column head(DEG_S10_filtered) nrow(DEG_S10_filtered) # finding the number of genes that passed the filter A data.frame: 6 \u00d7 6 gene_ID logFC logCPM F PValue FDR 1 g39 3.586170 14.12463 336.66158 5.227331e-10 8.520550e-08 2 g232 4.049413 11.95044 157.50997 3.685988e-08 2.591779e-06 3 g6 2.322535 12.99954 150.27854 4.770146e-08 2.591779e-06 4 g350 2.053003 13.32457 97.56453 4.890245e-07 1.992775e-05 5 g99 -1.538787 11.68100 85.17478 9.976659e-07 3.195090e-05 6 g142 -1.423528 13.83069 82.52308 1.176107e-06 3.195090e-05 52 We can also visualize the selected genes by plotting a Smear plot or a Volcano plot. The Genes that passed the filter are coloured in red, and the top 10 genes with the lowest FDR value are labelled with their gene ID. We can see that the majority of the genes analysed are either not statistically significant or have a very small logFC. In [17]: Copied! plotSmear ( glmqlf_S10 , de.tags = rownames ( DEG_S10 $ table )[ which ( DEG_S10 $ table $ FDR < 0.05 & abs ( DEG_S10 $ table $ logFC ) > 1 )]) text ( x = DEG_S10_filtered $ logCPM [ 1 : 10 ], y = DEG_S10_filtered $ logFC [ 1 : 10 ], labels = DEG_S10_filtered $ gene_ID [ 1 : 10 ], cex = 0.7 , pos = 1 ) abline ( h = c ( -1 , 1 ), col = \"blue\" ) plotSmear(glmqlf_S10, de.tags = rownames(DEG_S10$table)[which(DEG_S10$table$FDR < 0.05 & abs(DEG_S10$table$logFC) > 1)]) text(x=DEG_S10_filtered$logCPM[1:10], y=DEG_S10_filtered$logFC[1:10], labels=DEG_S10_filtered$gene_ID[1:10], cex=0.7, pos=1) abline(h = c(-1, 1), col = \"blue\") We can also create a heatmap with the log2 read counts of the selected differentially expressed genes so that we can visualise the differences in normalized counts between the Control and the Treatment samples. The genes are ordered by the FDR value. In [18]: Copied! annot_col <- data.frame ( row.names = colnames ( pseudoNormCounts )[ 1 : 6 ], Condition = c ( rep ( \"Control\" , 3 ), rep ( \"Treatment\" , 3 ))) pheatmap ( as.matrix ( pseudoNormCounts [ DEG_S10_filtered $ gene_ID , c ( 1 : 6 )]), cluster_rows = F , cluster_col = F , annotation_col = annot_col ) annot_col < - data.frame(row.names = colnames(pseudoNormCounts)[1:6], Condition = c(rep(\"Control\", 3),rep( \"Treatment\", 3))) pheatmap(as.matrix(pseudoNormCounts[DEG_S10_filtered$gene_ID,c(1:6)]), cluster_rows = F, cluster_col = F, annotation_col = annot_col)","title":"DEGs for White clover S10 plants Treatment vs Control"},{"location":"nb/bulk.html#degs-for-white-clover-tienshan-treatment-vs-control","text":"We can do this using the same functions as above and changing the contrast. This time we will directly filter the differentially expressed genes using the same parameters as for the S10 samples. In [19]: Copied! glmqlf_Ti <- glmQLFTest ( fit , contrast = contrasts [, \"Tienshan\" ]) DEG_Ti <- topTags ( glmqlf_Ti , n = nrow ( DGEList $ counts )) DEG_Ti_filtered <- DEG_Ti $ table [ DEG_Ti $ table $ FDR < 0.05 & abs ( DEG_Ti $ table $ logFC ) > 1 ,] DEG_Ti_filtered <- rownames_to_column ( DEG_Ti_filtered ) %>% rename ( gene_ID = rowname ) print ( head ( DEG_Ti_filtered )) print ( \"Nr of differentially expressed genes:\" ) print ( nrow ( DEG_Ti_filtered )) glmqlf_Ti < - glmQLFTest(fit, contrast=contrasts[,\"Tienshan\"]) DEG_Ti < - topTags(glmqlf_Ti, n = nrow(DGEList$counts)) DEG_Ti_filtered < - DEG_Ti$table[DEG_Ti$table$FDR < 0.05 & abs(DEG_Ti$table$logFC) > 1,] DEG_Ti_filtered < - rownames_to_column(DEG_Ti_filtered) %>% rename(gene_ID = rowname) print(head(DEG_Ti_filtered)) print(\"Nr of differentially expressed genes:\") print(nrow(DEG_Ti_filtered)) gene_ID logFC logCPM F PValue FDR 1 g234 -3.348667 13.00417 278.01679 1.547626e-09 2.522630e-07 2 g269 -1.885843 11.83422 127.39949 1.172628e-07 9.556922e-06 3 g26 2.423725 11.36683 74.33983 2.016212e-06 8.910761e-05 4 g101 4.303290 13.18162 73.17140 2.186690e-06 8.910761e-05 5 g272 3.391225 13.86065 66.80131 3.475790e-06 9.840357e-05 6 g99 -1.430395 11.68100 66.25814 3.622217e-06 9.840357e-05 [1] \"Nr of differentially expressed genes:\" [1] 42 We can now plot a Venn diagram with the DEGs for the two genotypes in order to observe the number of common and specific differentially expressed genes between the two genotypes as response to the cold exposure. You can see that a high percentage of the identified genes are common for the two genotypes, while each genotype has also specific genes. In [20]: Copied! vd <- venn.diagram ( x = list ( DEG_S10_filtered $ gene_ID , DEG_Ti_filtered $ gene_ID ), category.names = c ( \"S10\" , \"Tienshan\" ), lwd = 4 , fill = c ( \"cornflowerblue\" , \"yellowgreen\" ), filename = NULL , cat.cex = 1 , cat.fontface = \"bold\" , output = TRUE ) grid.draw ( vd ) vd < - venn.diagram( x = list(DEG_S10_filtered$gene_ID, DEG_Ti_filtered$gene_ID), category.names = c(\"S10\" , \"Tienshan\"), lwd = 4, fill = c(\"cornflowerblue\", \"yellowgreen\"), filename = NULL, cat.cex = 1, cat.fontface = \"bold\", output=TRUE ) grid.draw(vd)","title":"DEGs for White clover Tienshan Treatment vs Control"},{"location":"nb/bulk.html#degs-for-s10tienshan-treatment-vs-control-wo-genotype-effects","text":"Until now we tested for DEGs specific for each of the two genotypes under cold treatment. We can also run a test where we ignore the genotype and just test for the differences in the cold response. Consider the counts for both genotypes as a single dataset using the previously created contrast \"S10_Tienshan\". Do the results look different compared with the previous tests? In [21]: Copied! glmqlf_S10_Ti <- glmQLFTest ( fit , contrast = contrasts [, \"S10_Tienshan\" ]) DEG_S10_Ti <- topTags ( glmqlf_S10_Ti , n = nrow ( DGEList $ counts )) DEG_S10_Ti_filtered <- DEG_S10_Ti $ table [ DEG_S10_Ti $ table $ FDR < 0.05 & abs ( DEG_S10_Ti $ table $ logFC ) > 1 ,] DEG_S10_Ti_filtered <- rownames_to_column ( DEG_S10_Ti_filtered ) %>% rename ( gene_ID = rowname ) print ( head ( DEG_S10_Ti_filtered )) print ( \"Nr of differentially expressed genes:\" ) print ( nrow ( DEG_S10_Ti_filtered )) glmqlf_S10_Ti < - glmQLFTest(fit, contrast=contrasts[,\"S10_Tienshan\"]) DEG_S10_Ti < - topTags(glmqlf_S10_Ti, n = nrow(DGEList$counts)) DEG_S10_Ti_filtered < - DEG_S10_Ti$table[DEG_S10_Ti$table$FDR < 0.05 & abs(DEG_S10_Ti$table$logFC) > 1,] DEG_S10_Ti_filtered < - rownames_to_column(DEG_S10_Ti_filtered) %>% rename(gene_ID = rowname) print(head(DEG_S10_Ti_filtered)) print(\"Nr of differentially expressed genes:\") print(nrow(DEG_S10_Ti_filtered)) gene_ID logFC logCPM F PValue FDR 1 g39 2.057128 14.12463 246.6519 3.039491e-09 4.954370e-07 2 g232 3.261008 11.95044 203.4580 8.934889e-09 5.288546e-07 3 g269 -1.652505 11.83422 200.3564 9.733520e-09 5.288546e-07 4 g6 1.714849 12.99954 166.2297 2.740066e-08 1.116577e-06 5 g99 -1.484591 11.68100 150.3246 4.762148e-08 1.552460e-06 6 g26 2.274575 11.36683 142.8625 6.290957e-08 1.623160e-06 [1] \"Nr of differentially expressed genes:\" [1] 52 Plot the results from the 3 tests in a Venn diagram to visualize the number of common and unique genes In [22]: Copied! vd <- venn.diagram ( x = list ( DEG_S10_filtered $ gene_ID , DEG_Ti_filtered $ gene_ID , DEG_S10_Ti_filtered $ gene_ID ), category.names = c ( \"S10\" , \"Tienshan\" , \"S10_Tienshan\" ), lwd = 3 , fill = c ( \"cornflowerblue\" , \"yellowgreen\" , \"thistle3\" ), filename = NULL , cat.cex = 1 , cat.fontface = \"bold\" , output = TRUE ) grid.draw ( vd ) vd < - venn.diagram( x = list(DEG_S10_filtered$gene_ID, DEG_Ti_filtered$gene_ID, DEG_S10_Ti_filtered$gene_ID), category.names = c(\"S10\" , \"Tienshan\", \"S10_Tienshan\"), lwd = 3, fill = c(\"cornflowerblue\", \"yellowgreen\", \"thistle3\"), filename = NULL, cat.cex = 1, cat.fontface = \"bold\", output=TRUE ) grid.draw(vd)","title":"DEGs for S10+Tienshan Treatment vs Control w/o genotype effects"},{"location":"nb/bulk.html#explore-dge-results","text":"","title":"Explore DGE results"},{"location":"nb/bulk.html#select-the-genes-which-appear-only-in-the-analysis-using-both-genotypes-for-further-examination","text":"We can use the \"anti_join\" function from the dplyr package to keep only the unique genes that appear only when using both genotypes. In [23]: Copied! S10_Ti_unique <- anti_join ( DEG_S10_Ti_filtered , DEG_S10_filtered , by = \"gene_ID\" ) %>% anti_join ( DEG_Ti_filtered , by = \"gene_ID\" ) S10_Ti_unique S10_Ti_unique < - anti_join(DEG_S10_Ti_filtered, DEG_S10_filtered, by=\"gene_ID\") %>% anti_join(DEG_Ti_filtered, by=\"gene_ID\") S10_Ti_unique A data.frame: 2 \u00d7 6 gene_ID logFC logCPM F PValue FDR g247 -1.412652 8.424523 10.877350 0.006535994 0.01500517 g360 1.180849 7.900076 9.804067 0.013809376 0.02813660","title":"Select the genes which appear only in the analysis using both genotypes for further examination."},{"location":"nb/bulk.html#linking-the-genes-selected-as-differentially-expressed-back-to-the-raw-read-counts","text":"How do the counts look for these genes, does it make sense that they are differentially expressed only when using the two genotypes? In [24]: Copied! Read_counts <- rownames_to_column ( Read_counts ) %>% rename ( gene_ID = rowname ) S10_Ti_unique_counts <- inner_join ( S10_Ti_unique [, c ( 1 , 2 , 6 )], Read_counts , by = \"gene_ID\" ) S10_Ti_unique_counts Read_counts < - rownames_to_column(Read_counts) %>% rename(gene_ID = rowname) S10_Ti_unique_counts < - inner_join(S10_Ti_unique[,c(1,2,6)], Read_counts, by=\"gene_ID\") S10_Ti_unique_counts A data.frame: 2 \u00d7 15 gene_ID logFC FDR S10_1_1 S10_1_2 S10_1_3 S10_2_1 S10_2_2 S10_2_3 TI_1_1 TI_1_2 TI_1_3 TI_2_1 TI_2_2 TI_2_3 g247 -1.412652 0.01500517 4 9 3 1 2 1 42 27 32 8 22 22 g360 1.180849 0.02813660 1 0 1 3 2 3 16 10 16 22 19 22","title":"Linking the genes selected as differentially expressed back to the raw read counts"},{"location":"nb/bulk.html#adding-functional-annotation-to-degs","text":"Until now we looked only at gene IDs, but we can also add functional annotations to the DEGs. The functional annotations were generated using protein sequences and the EggNOG software. Identifying the molecular function of the differentially expressed genes can help us do a literature survey in order to check if any of the genes discovered have been previously identified as being involved in the cold response. In [25]: Copied! Functional_annotations <- read.delim ( \"../Data/Clover_Data/Functional_Annotations.txt\" ) S10_Ti_unique_FA <- inner_join ( S10_Ti_unique [, c ( 1 , 2 , 6 )], Functional_annotations , by = \"gene_ID\" ) S10_Ti_unique_FA Functional_annotations < - read.delim(\"../Data/Clover_Data/Functional_Annotations.txt\") S10_Ti_unique_FA < - inner_join(S10_Ti_unique[,c(1,2,6)], Functional_annotations, by=\"gene_ID\") S10_Ti_unique_FA A data.frame: 2 \u00d7 4 gene_ID logFC FDR Human.Readable.Description g247 -1.412652 0.01500517 Nitrogen regulatory protein P-II homolog g360 1.180849 0.02813660 Unknown protein Tasks and Questions Based on the results obtained in the analysis so far, would you change the cut-off for the FDR and logFC to be more strict or more permissive? Look back at the raw counts for different FDR and logFC values and set the thresholds as you find appropriate. You can also plot histograms with the FDR and logFC values. In [ ]: Copied! hist ( DEG_S10 $ table $ FDR , main = \"\" , xlab = \"FDR\" , breaks = 200 , xlim = range ( c ( 0 , 0.1 ))) hist(DEG_S10$table$FDR , main = \"\", xlab = \"FDR\", breaks= 200, xlim = range(c(0, 0.1))) In [ ]: Copied! hist ( DEG_S10 $ table $ logFC , main = \"\" , xlab = \"logFC\" , breaks = 50 , xlim = range ( c ( -6 , 6 ))) hist(DEG_S10$table$logFC , main = \"\", xlab = \"logFC\", breaks= 50, xlim = range(c(-6, 6))) Separate the upregulated and downregulated genes for each genotype and append functional annotations to them. Identify the genes that are commonly upregulated in S10 and Tienshan samples and the uniquely upregulated genes for each genotype. Why do you think some of the proteins appear in duplicates? To answer these questions, it may be convenient to save summary tables from R and open them in excel. See the code below for examples of how to do this. Files can be downloaded by right-clicking on the file name. If you are familiar with R functions, you are welcome to use those for counting. In [ ]: Copied! dir.create ( \"DEG_Output_tables\" , showWarnings = FALSE ) dir.create(\"DEG_Output_tables\", showWarnings = FALSE) In [ ]: Copied! #Create the table with the DEGs, Raw counts(just for the Ti samples in this case, change to columns (2:7) for the S10 samples) and Functional annotations DEG_Ti_counts_FA <- inner_join ( DEG_Ti_filtered [, c ( 1 , 2 , 6 )], Read_counts [, c ( 1 , 8 : 13 )], by = \"gene_ID\" ) %>% inner_join ( Functional_annotations , by = \"gene_ID\" ) #Write the table to file write.table ( DEG_Ti_counts_FA , file = \"DEG_Output_tables/Ti_Treatment_Control_DGE.txt\" , quote = FALSE , row.names = FALSE , sep = \"\\t\" ) #Display the first 10 rows of the table head ( DEG_Ti_counts_FA , n = 10 ) #Create the table with the DEGs, Raw counts(just for the Ti samples in this case, change to columns (2:7) for the S10 samples) and Functional annotations DEG_Ti_counts_FA < - inner_join(DEG_Ti_filtered[,c(1, 2, 6)], Read_counts[, c(1, 8:13)], by=\"gene_ID\") %>% inner_join(Functional_annotations, by=\"gene_ID\") #Write the table to file write.table(DEG_Ti_counts_FA, file = \"DEG_Output_tables/Ti_Treatment_Control_DGE.txt\", quote = FALSE, row.names = FALSE, sep = \"\\t\") #Display the first 10 rows of the table head(DEG_Ti_counts_FA, n=10) Example for filtering and counting the Up/Down genes. You can easily filter using the \"filter()\" function just by specifying the dataframe and a logical argument. In [ ]: Copied! DEG_Ti_up <- filter ( DEG_Ti_counts_FA , logFC > 0 ) print ( \"Nr of upregulated genes:\" ) print ( nrow ( DEG_Ti_up )) DEG_Ti_down <- filter ( DEG_Ti_counts_FA , logFC < 0 ) print ( \"Nr of downregulated genes:\" ) print ( nrow ( DEG_Ti_down )) DEG_Ti_up < - filter(DEG_Ti_counts_FA, logFC > 0) print(\"Nr of upregulated genes:\") print(nrow(DEG_Ti_up)) DEG_Ti_down < - filter(DEG_Ti_counts_FA, logFC < 0) print(\"Nr of downregulated genes:\") print(nrow(DEG_Ti_down)) You can use the \"inner_join\" and the \"anti_join\" functions from the dplyr package to select the common and unique genes for each genotype: example_file_joined <- inner_join(file1, file2, by=\"gene_ID\")","title":"Adding functional annotation to DEGs"},{"location":"nb/bulk.html#wrapping-up","text":"In this notebook, you have worked with RNA-seq results for two white clover genotypes exposed to one night of cold treatment, aiming to identify genes that change their expression in response to the cold treatment. You have learned to perform exploratory data analysis of raw and normalized RNA-seq read counts. You have also performed differential gene expression using edgeR .","title":"Wrapping up \ud83c\udf89 \ud83c\udf89 \ud83c\udf89"},{"location":"nb/vcf.html","text":"(function (global, factory) { typeof exports === 'object' && typeof module !== 'undefined' ? module.exports = factory() : typeof define === 'function' && define.amd ? define(factory) : (global = global || self, global.ClipboardCopyElement = factory()); }(this, function () { 'use strict'; function createNode(text) { const node = document.createElement('pre'); node.style.width = '1px'; node.style.height = '1px'; node.style.position = 'fixed'; node.style.top = '5px'; node.textContent = text; return node; } function copyNode(node) { if ('clipboard' in navigator) { // eslint-disable-next-line flowtype/no-flow-fix-me-comments // $FlowFixMe Clipboard is not defined in Flow yet. return navigator.clipboard.writeText(node.textContent); } const selection = getSelection(); if (selection == null) { return Promise.reject(new Error()); } selection.removeAllRanges(); 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We also subtract one pixel so that no scrollbar appears if we have just one single-line cell in the notebook. This padding is to enable a 'scroll past end' feature in a notebook. */ --jp-notebook-scroll-padding: calc( 100% - var(--jp-code-font-size) * var(--jp-code-line-height) - var(--jp-code-padding) - var(--jp-cell-padding) - 1px ); /* Rendermime styles */ --jp-rendermime-error-background: #fdd; --jp-rendermime-table-row-background: var(--md-grey-100); --jp-rendermime-table-row-hover-background: var(--md-light-blue-50); /* Dialog specific styles */ --jp-dialog-background: rgba(0, 0, 0, 0.25); /* Console specific styles */ --jp-console-padding: 10px; /* Toolbar specific styles */ --jp-toolbar-border-color: var(--jp-border-color1); --jp-toolbar-micro-height: 8px; --jp-toolbar-background: var(--jp-layout-color1); --jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, 0.24); --jp-toolbar-header-margin: 4px 4px 0px 4px; --jp-toolbar-active-background: var(--md-grey-300); /* Statusbar specific styles */ --jp-statusbar-height: 24px; /* Input field styles */ --jp-input-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-input-active-background: var(--jp-layout-color1); --jp-input-hover-background: var(--jp-layout-color1); --jp-input-background: var(--md-grey-100); --jp-input-border-color: var(--jp-border-color1); --jp-input-active-border-color: var(--jp-brand-color1); --jp-input-active-box-shadow-color: rgba(19, 124, 189, 0.3); /* General editor styles */ --jp-editor-selected-background: #d9d9d9; --jp-editor-selected-focused-background: #d7d4f0; --jp-editor-cursor-color: var(--jp-ui-font-color0); /* Code mirror specific styles */ --jp-mirror-editor-keyword-color: #008000; --jp-mirror-editor-atom-color: #88f; --jp-mirror-editor-number-color: #080; --jp-mirror-editor-def-color: #00f; --jp-mirror-editor-variable-color: var(--md-grey-900); --jp-mirror-editor-variable-2-color: #05a; --jp-mirror-editor-variable-3-color: #085; --jp-mirror-editor-punctuation-color: #05a; --jp-mirror-editor-property-color: #05a; --jp-mirror-editor-operator-color: #aa22ff; --jp-mirror-editor-comment-color: #408080; --jp-mirror-editor-string-color: #ba2121; --jp-mirror-editor-string-2-color: #708; --jp-mirror-editor-meta-color: #aa22ff; --jp-mirror-editor-qualifier-color: #555; --jp-mirror-editor-builtin-color: #008000; --jp-mirror-editor-bracket-color: #997; --jp-mirror-editor-tag-color: #170; --jp-mirror-editor-attribute-color: #00c; --jp-mirror-editor-header-color: blue; --jp-mirror-editor-quote-color: #090; --jp-mirror-editor-link-color: #00c; --jp-mirror-editor-error-color: #f00; --jp-mirror-editor-hr-color: #999; /* Vega extension styles */ --jp-vega-background: white; /* Sidebar-related styles */ --jp-sidebar-min-width: 250px; /* Search-related styles */ --jp-search-toggle-off-opacity: 0.5; --jp-search-toggle-hover-opacity: 0.8; --jp-search-toggle-on-opacity: 1; --jp-search-selected-match-background-color: rgb(245, 200, 0); --jp-search-selected-match-color: black; --jp-search-unselected-match-background-color: var( --jp-inverse-layout-color0 ); --jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0); /* Icon colors that work well with light or dark backgrounds */ --jp-icon-contrast-color0: var(--md-purple-600); --jp-icon-contrast-color1: var(--md-green-600); --jp-icon-contrast-color2: var(--md-pink-600); --jp-icon-contrast-color3: var(--md-blue-600); } [data-md-color-scheme=\"slate\"] .jupyter-wrapper { /* Elevation * * We style box-shadows using Material Design's idea of elevation. These particular numbers are taken from here: * * https://github.com/material-components/material-components-web * https://material-components-web.appspot.com/elevation.html */ /* The dark theme shadows need a bit of work, but this will probably also require work on the core layout * colors used in the theme as well. */ --jp-shadow-base-lightness: 32; --jp-shadow-umbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.2 ); --jp-shadow-penumbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.14 ); --jp-shadow-ambient-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.12 ); --jp-elevation-z0: none; --jp-elevation-z1: 0px 2px 1px -1px var(--jp-shadow-umbra-color), 0px 1px 1px 0px var(--jp-shadow-penumbra-color), 0px 1px 3px 0px var(--jp-shadow-ambient-color); --jp-elevation-z2: 0px 3px 1px -2px var(--jp-shadow-umbra-color), 0px 2px 2px 0px var(--jp-shadow-penumbra-color), 0px 1px 5px 0px var(--jp-shadow-ambient-color); --jp-elevation-z4: 0px 2px 4px -1px var(--jp-shadow-umbra-color), 0px 4px 5px 0px var(--jp-shadow-penumbra-color), 0px 1px 10px 0px var(--jp-shadow-ambient-color); --jp-elevation-z6: 0px 3px 5px -1px var(--jp-shadow-umbra-color), 0px 6px 10px 0px var(--jp-shadow-penumbra-color), 0px 1px 18px 0px var(--jp-shadow-ambient-color); --jp-elevation-z8: 0px 5px 5px -3px var(--jp-shadow-umbra-color), 0px 8px 10px 1px var(--jp-shadow-penumbra-color), 0px 3px 14px 2px var(--jp-shadow-ambient-color); --jp-elevation-z12: 0px 7px 8px -4px var(--jp-shadow-umbra-color), 0px 12px 17px 2px var(--jp-shadow-penumbra-color), 0px 5px 22px 4px var(--jp-shadow-ambient-color); --jp-elevation-z16: 0px 8px 10px -5px var(--jp-shadow-umbra-color), 0px 16px 24px 2px var(--jp-shadow-penumbra-color), 0px 6px 30px 5px var(--jp-shadow-ambient-color); --jp-elevation-z20: 0px 10px 13px -6px var(--jp-shadow-umbra-color), 0px 20px 31px 3px var(--jp-shadow-penumbra-color), 0px 8px 38px 7px var(--jp-shadow-ambient-color); --jp-elevation-z24: 0px 11px 15px -7px var(--jp-shadow-umbra-color), 0px 24px 38px 3px var(--jp-shadow-penumbra-color), 0px 9px 46px 8px var(--jp-shadow-ambient-color); /* Borders * * The following variables, specify the visual styling of borders in JupyterLab. */ --jp-border-width: 1px; --jp-border-color0: var(--md-grey-700); --jp-border-color1: var(--md-grey-700); --jp-border-color2: var(--md-grey-800); --jp-border-color3: var(--md-grey-900); --jp-border-radius: 2px; /* UI Fonts * * The UI font CSS variables are used for the typography all of the JupyterLab * user interface elements that are not directly user generated content. * * The font sizing here is done assuming that the body font size of --jp-ui-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-ui-font-scale-factor: 1.2; --jp-ui-font-size0: 0.83333em; --jp-ui-font-size1: 13px; /* Base font size */ --jp-ui-font-size2: 1.2em; --jp-ui-font-size3: 1.44em; --jp-ui-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Use these font colors against the corresponding main layout colors. * In a light theme, these go from dark to light. */ /* Defaults use Material Design specification */ --jp-ui-font-color0: rgba(255, 255, 255, 1); --jp-ui-font-color1: rgba(255, 255, 255, 0.87); --jp-ui-font-color2: rgba(255, 255, 255, 0.54); --jp-ui-font-color3: rgba(255, 255, 255, 0.38); /* * Use these against the brand/accent/warn/error colors. * These will typically go from light to darker, in both a dark and light theme. */ --jp-ui-inverse-font-color0: rgba(0, 0, 0, 1); --jp-ui-inverse-font-color1: rgba(0, 0, 0, 0.8); --jp-ui-inverse-font-color2: rgba(0, 0, 0, 0.5); --jp-ui-inverse-font-color3: rgba(0, 0, 0, 0.3); /* Content Fonts * * Content font variables are used for typography of user generated content. * * The font sizing here is done assuming that the body font size of --jp-content-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-content-line-height: 1.6; --jp-content-font-scale-factor: 1.2; --jp-content-font-size0: 0.83333em; --jp-content-font-size1: 14px; /* Base font size */ --jp-content-font-size2: 1.2em; --jp-content-font-size3: 1.44em; --jp-content-font-size4: 1.728em; --jp-content-font-size5: 2.0736em; /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-content-presentation-font-size1: 17px; --jp-content-heading-line-height: 1; --jp-content-heading-margin-top: 1.2em; --jp-content-heading-margin-bottom: 0.8em; --jp-content-heading-font-weight: 500; /* Defaults use Material Design specification */ --jp-content-font-color0: rgba(255, 255, 255, 1); --jp-content-font-color1: rgba(255, 255, 255, 1); --jp-content-font-color2: rgba(255, 255, 255, 0.7); --jp-content-font-color3: rgba(255, 255, 255, 0.5); --jp-content-link-color: var(--md-blue-300); --jp-content-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Code Fonts * * Code font variables are used for typography of code and other monospaces content. */ --jp-code-font-size: 13px; --jp-code-line-height: 1.3077; /* 17px for 13px base */ --jp-code-padding: 5px; /* 5px for 13px base, codemirror highlighting needs integer px value */ --jp-code-font-family-default: Menlo, Consolas, \"DejaVu Sans Mono\", monospace; --jp-code-font-family: var(--jp-code-font-family-default); /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-code-presentation-font-size: 16px; /* may need to tweak cursor width if you change font size */ --jp-code-cursor-width0: 1.4px; --jp-code-cursor-width1: 2px; --jp-code-cursor-width2: 4px; /* Layout * * The following are the main layout colors use in JupyterLab. In a light * theme these would go from light to dark. */ --jp-layout-color0: #111111; --jp-layout-color1: var(--md-grey-900); --jp-layout-color2: var(--md-grey-800); --jp-layout-color3: var(--md-grey-700); --jp-layout-color4: var(--md-grey-600); /* Inverse Layout * * The following are the inverse layout colors use in JupyterLab. In a light * theme these would go from dark to light. */ --jp-inverse-layout-color0: white; --jp-inverse-layout-color1: white; --jp-inverse-layout-color2: var(--md-grey-200); --jp-inverse-layout-color3: var(--md-grey-400); --jp-inverse-layout-color4: var(--md-grey-600); /* Brand/accent */ --jp-brand-color0: var(--md-blue-700); --jp-brand-color1: var(--md-blue-500); --jp-brand-color2: var(--md-blue-300); --jp-brand-color3: var(--md-blue-100); --jp-brand-color4: var(--md-blue-50); --jp-accent-color0: var(--md-green-700); --jp-accent-color1: var(--md-green-500); --jp-accent-color2: var(--md-green-300); --jp-accent-color3: var(--md-green-100); /* State colors (warn, error, success, info) */ --jp-warn-color0: var(--md-orange-700); --jp-warn-color1: var(--md-orange-500); --jp-warn-color2: var(--md-orange-300); --jp-warn-color3: var(--md-orange-100); --jp-error-color0: var(--md-red-700); --jp-error-color1: var(--md-red-500); --jp-error-color2: var(--md-red-300); --jp-error-color3: var(--md-red-100); --jp-success-color0: var(--md-green-700); --jp-success-color1: var(--md-green-500); --jp-success-color2: var(--md-green-300); --jp-success-color3: var(--md-green-100); --jp-info-color0: var(--md-cyan-700); --jp-info-color1: var(--md-cyan-500); --jp-info-color2: var(--md-cyan-300); --jp-info-color3: var(--md-cyan-100); /* Cell specific styles */ --jp-cell-padding: 5px; --jp-cell-collapser-width: 8px; --jp-cell-collapser-min-height: 20px; --jp-cell-collapser-not-active-hover-opacity: 0.6; --jp-cell-editor-background: var(--jp-layout-color1); --jp-cell-editor-border-color: var(--md-grey-700); --jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-cell-editor-active-background: var(--jp-layout-color0); --jp-cell-editor-active-border-color: var(--jp-brand-color1); --jp-cell-prompt-width: 64px; --jp-cell-prompt-font-family: var(--jp-code-font-family-default); --jp-cell-prompt-letter-spacing: 0px; --jp-cell-prompt-opacity: 1; --jp-cell-prompt-not-active-opacity: 1; --jp-cell-prompt-not-active-font-color: var(--md-grey-300); /* A custom blend of MD grey and blue 600 * See https://meyerweb.com/eric/tools/color-blend/#546E7A:1E88E5:5:hex */ --jp-cell-inprompt-font-color: #307fc1; /* A custom blend of MD grey and orange 600 * https://meyerweb.com/eric/tools/color-blend/#546E7A:F4511E:5:hex */ --jp-cell-outprompt-font-color: #bf5b3d; /* Notebook specific styles */ --jp-notebook-padding: 10px; --jp-notebook-select-background: var(--jp-layout-color1); --jp-notebook-multiselected-color: rgba(33, 150, 243, 0.24); /* The scroll padding is calculated to fill enough space at the bottom of the notebook to show one single-line cell (with appropriate padding) at the top when the notebook is scrolled all the way to the bottom. We also subtract one pixel so that no scrollbar appears if we have just one single-line cell in the notebook. This padding is to enable a 'scroll past end' feature in a notebook. */ --jp-notebook-scroll-padding: calc( 100% - var(--jp-code-font-size) * var(--jp-code-line-height) - var(--jp-code-padding) - var(--jp-cell-padding) - 1px ); /* Rendermime styles */ --jp-rendermime-error-background: rgba(244, 67, 54, 0.28); --jp-rendermime-table-row-background: var(--md-grey-900); --jp-rendermime-table-row-hover-background: rgba(3, 169, 244, 0.2); /* Dialog specific styles */ --jp-dialog-background: rgba(0, 0, 0, 0.6); /* Console specific styles */ --jp-console-padding: 10px; /* Toolbar specific styles */ --jp-toolbar-border-color: var(--jp-border-color2); --jp-toolbar-micro-height: 8px; --jp-toolbar-background: var(--jp-layout-color1); --jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, 0.8); --jp-toolbar-header-margin: 4px 4px 0px 4px; --jp-toolbar-active-background: var(--jp-layout-color0); /* Statusbar specific styles */ --jp-statusbar-height: 24px; /* Input field styles */ --jp-input-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-input-active-background: var(--jp-layout-color0); --jp-input-hover-background: var(--jp-layout-color2); --jp-input-background: var(--md-grey-800); --jp-input-border-color: var(--jp-border-color1); --jp-input-active-border-color: var(--jp-brand-color1); --jp-input-active-box-shadow-color: rgba(19, 124, 189, 0.3); /* General editor styles */ --jp-editor-selected-background: var(--jp-layout-color2); --jp-editor-selected-focused-background: rgba(33, 150, 243, 0.24); --jp-editor-cursor-color: var(--jp-ui-font-color0); /* Code mirror specific styles */ --jp-mirror-editor-keyword-color: var(--md-green-500); --jp-mirror-editor-atom-color: var(--md-blue-300); --jp-mirror-editor-number-color: var(--md-green-400); --jp-mirror-editor-def-color: var(--md-blue-600); --jp-mirror-editor-variable-color: var(--md-grey-300); --jp-mirror-editor-variable-2-color: var(--md-blue-400); --jp-mirror-editor-variable-3-color: var(--md-green-600); --jp-mirror-editor-punctuation-color: var(--md-blue-400); --jp-mirror-editor-property-color: var(--md-blue-400); --jp-mirror-editor-operator-color: #aa22ff; --jp-mirror-editor-comment-color: #408080; --jp-mirror-editor-string-color: #ff7070; --jp-mirror-editor-string-2-color: var(--md-purple-300); --jp-mirror-editor-meta-color: #aa22ff; --jp-mirror-editor-qualifier-color: #555; --jp-mirror-editor-builtin-color: var(--md-green-600); --jp-mirror-editor-bracket-color: #997; --jp-mirror-editor-tag-color: var(--md-green-700); --jp-mirror-editor-attribute-color: var(--md-blue-700); --jp-mirror-editor-header-color: var(--md-blue-500); --jp-mirror-editor-quote-color: var(--md-green-300); --jp-mirror-editor-link-color: var(--md-blue-700); --jp-mirror-editor-error-color: #f00; --jp-mirror-editor-hr-color: #999; /* Vega extension styles */ --jp-vega-background: var(--md-grey-400); /* Sidebar-related styles */ --jp-sidebar-min-width: 250px; /* Search-related styles */ --jp-search-toggle-off-opacity: 0.6; --jp-search-toggle-hover-opacity: 0.8; --jp-search-toggle-on-opacity: 1; --jp-search-selected-match-background-color: rgb(255, 225, 0); --jp-search-selected-match-color: black; --jp-search-unselected-match-background-color: var( --jp-inverse-layout-color0 ); --jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0); /* scrollbar related styles. Supports every browser except Edge. */ /* colors based on JetBrain's Darcula theme */ --jp-scrollbar-background-color: #3f4244; --jp-scrollbar-thumb-color: 88, 96, 97; /* need to specify thumb color as an RGB triplet */ --jp-scrollbar-endpad: 3px; /* the minimum gap between the thumb and the ends of a scrollbar */ /* hacks for setting the thumb shape. These do nothing in Firefox */ --jp-scrollbar-thumb-margin: 3.5px; /* the space in between the sides of the thumb and the track */ --jp-scrollbar-thumb-radius: 9px; /* set to a large-ish value for rounded endcaps on the thumb */ /* Icon colors that work well with light or dark backgrounds */ --jp-icon-contrast-color0: var(--md-purple-600); --jp-icon-contrast-color1: var(--md-green-600); --jp-icon-contrast-color2: var(--md-pink-600); --jp-icon-contrast-color3: var(--md-blue-600); } :root{--md-red-50: #ffebee;--md-red-100: #ffcdd2;--md-red-200: #ef9a9a;--md-red-300: #e57373;--md-red-400: #ef5350;--md-red-500: #f44336;--md-red-600: #e53935;--md-red-700: #d32f2f;--md-red-800: #c62828;--md-red-900: #b71c1c;--md-red-A100: #ff8a80;--md-red-A200: #ff5252;--md-red-A400: #ff1744;--md-red-A700: #d50000;--md-pink-50: #fce4ec;--md-pink-100: #f8bbd0;--md-pink-200: #f48fb1;--md-pink-300: #f06292;--md-pink-400: #ec407a;--md-pink-500: #e91e63;--md-pink-600: #d81b60;--md-pink-700: #c2185b;--md-pink-800: #ad1457;--md-pink-900: #880e4f;--md-pink-A100: #ff80ab;--md-pink-A200: #ff4081;--md-pink-A400: #f50057;--md-pink-A700: #c51162;--md-purple-50: #f3e5f5;--md-purple-100: #e1bee7;--md-purple-200: #ce93d8;--md-purple-300: #ba68c8;--md-purple-400: #ab47bc;--md-purple-500: #9c27b0;--md-purple-600: #8e24aa;--md-purple-700: #7b1fa2;--md-purple-800: #6a1b9a;--md-purple-900: #4a148c;--md-purple-A100: #ea80fc;--md-purple-A200: #e040fb;--md-purple-A400: #d500f9;--md-purple-A700: #aa00ff;--md-deep-purple-50: #ede7f6;--md-deep-purple-100: #d1c4e9;--md-deep-purple-200: #b39ddb;--md-deep-purple-300: #9575cd;--md-deep-purple-400: #7e57c2;--md-deep-purple-500: #673ab7;--md-deep-purple-600: #5e35b1;--md-deep-purple-700: #512da8;--md-deep-purple-800: #4527a0;--md-deep-purple-900: #311b92;--md-deep-purple-A100: #b388ff;--md-deep-purple-A200: #7c4dff;--md-deep-purple-A400: #651fff;--md-deep-purple-A700: #6200ea;--md-indigo-50: #e8eaf6;--md-indigo-100: #c5cae9;--md-indigo-200: #9fa8da;--md-indigo-300: #7986cb;--md-indigo-400: #5c6bc0;--md-indigo-500: #3f51b5;--md-indigo-600: #3949ab;--md-indigo-700: #303f9f;--md-indigo-800: #283593;--md-indigo-900: #1a237e;--md-indigo-A100: #8c9eff;--md-indigo-A200: #536dfe;--md-indigo-A400: #3d5afe;--md-indigo-A700: #304ffe;--md-blue-50: #e3f2fd;--md-blue-100: #bbdefb;--md-blue-200: #90caf9;--md-blue-300: #64b5f6;--md-blue-400: #42a5f5;--md-blue-500: #2196f3;--md-blue-600: #1e88e5;--md-blue-700: #1976d2;--md-blue-800: #1565c0;--md-blue-900: #0d47a1;--md-blue-A100: #82b1ff;--md-blue-A200: #448aff;--md-blue-A400: #2979ff;--md-blue-A700: #2962ff;--md-light-blue-50: #e1f5fe;--md-light-blue-100: #b3e5fc;--md-light-blue-200: #81d4fa;--md-light-blue-300: #4fc3f7;--md-light-blue-400: #29b6f6;--md-light-blue-500: #03a9f4;--md-light-blue-600: #039be5;--md-light-blue-700: #0288d1;--md-light-blue-800: #0277bd;--md-light-blue-900: #01579b;--md-light-blue-A100: #80d8ff;--md-light-blue-A200: #40c4ff;--md-light-blue-A400: #00b0ff;--md-light-blue-A700: #0091ea;--md-cyan-50: #e0f7fa;--md-cyan-100: #b2ebf2;--md-cyan-200: #80deea;--md-cyan-300: #4dd0e1;--md-cyan-400: #26c6da;--md-cyan-500: #00bcd4;--md-cyan-600: #00acc1;--md-cyan-700: #0097a7;--md-cyan-800: #00838f;--md-cyan-900: #006064;--md-cyan-A100: #84ffff;--md-cyan-A200: #18ffff;--md-cyan-A400: #00e5ff;--md-cyan-A700: #00b8d4;--md-teal-50: #e0f2f1;--md-teal-100: #b2dfdb;--md-teal-200: #80cbc4;--md-teal-300: #4db6ac;--md-teal-400: #26a69a;--md-teal-500: #009688;--md-teal-600: #00897b;--md-teal-700: #00796b;--md-teal-800: #00695c;--md-teal-900: #004d40;--md-teal-A100: #a7ffeb;--md-teal-A200: #64ffda;--md-teal-A400: #1de9b6;--md-teal-A700: #00bfa5;--md-green-50: #e8f5e9;--md-green-100: #c8e6c9;--md-green-200: #a5d6a7;--md-green-300: #81c784;--md-green-400: #66bb6a;--md-green-500: #4caf50;--md-green-600: #43a047;--md-green-700: #388e3c;--md-green-800: #2e7d32;--md-green-900: #1b5e20;--md-green-A100: #b9f6ca;--md-green-A200: #69f0ae;--md-green-A400: #00e676;--md-green-A700: 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sourceMappingURL=mkdocs-jupyter.css.map*/ init_mathjax = function() { if (window.MathJax) { // MathJax loaded MathJax.Hub.Config({ TeX: { equationNumbers: { autoNumber: \"AMS\", useLabelIds: true } }, tex2jax: { inlineMath: [ ['$','$'], [\"\\\\(\",\"\\\\)\"] ], displayMath: [ ['$$','$$'], [\"\\\\[\",\"\\\\]\"] ], processEscapes: true, processEnvironments: true }, displayAlign: 'center', CommonHTML: { linebreaks: { automatic: true } } }); MathJax.Hub.Queue([\"Typeset\", MathJax.Hub]); } } init_mathjax(); Variant calling and VCF processing This tutorial will cover the steps for performing Variant calling and working on the resulting VCF file format. We will be using the Hifi and RNA-seq mapping data obtained from Galaxy. At the end of this tutorial you will be able to: call variants using alignment files extract data from VCF files using Python filter and count variants The present tutorial, like the rest of the course material, is available at our open-source github repository . To use this notebook, use the NGS (python) kernel that contains the packages. Choose it by selecting Kernel -> Change Kernel in the menu on top of the window. A few introductory points to run this notebook (click to show): To use this notebook, use the NGS (python) kernel that contains the packages. Choose it by selecting Kernel -> Change Kernel in the menu on top of the window. In this notebook you will use both bash and python commands On some computers, you might see the result of the commands once they are done running. This means you will wait some time while the computer is crunching, and only afterwards you will see the result of the command you have executed You can run the code in each cell by clicking on the run cell button, or by pressing Shift + Enter . When the code is done running, a small green check sign will appear on the left side You need to run the cells in sequential order, please do not run a cell until the one above finished running and do not skip any cells Each cell contains a short description of the code and the output you should get. Please try not to focus on understanding the code for each command in too much detail, but rather try to focus on the output You can create new code cells by pressing + in the Menu bar above. Import the necessary Python libraries: In [1]: Copied! import pandas import allel import numpy as np import matplotlib as mpl import matplotlib.pyplot as plt import seaborn as sns import pandas import allel import numpy as np import matplotlib as mpl import matplotlib.pyplot as plt import seaborn as sns Some of the commands used in the course are functions we implement to simplify reading the code of this course. Mostly, those are commands requiring lines of code that would not add anything to your learning curve (management of plots, trivial calculations, few management of the notebook layout). However, you are free to look at the code into the file Scripts/pythonScripts.py and to reuse our code in your own work (citing our course). In [2]: Copied! % run ../ Scripts / pythonScripts . py %run ../Scripts/pythonScripts.py Create a folder for the data necessary to VCF data analysis In [3]: Copied! %% bash mkdir - p Data_for_VCF_analysis %%bash mkdir -p Data_for_VCF_analysis Use the bam files generated from the alignment of raw reads \u00b6 We will use the data created during the alignment session of the course. You had two possibilities: using the interactive Galaxy platform as explained in the course webpage . In this case you will need to upload the some of the created data into the folder Data_for_VCF_analysis , renaming the files as follows: Hifi reads mapped to Contig1: HIFI_contig_1.bam Hifi reads mapped to Contig2: HIFI_contig_2.bam Hifi reads mapped to Contig 1 and 2: HIFI_contig_1_2_asm20.bam (you can of course choose the one with the map-pb alignment option from the alignment notebook) S10 RNAseq reads mapped to Contig 1 and 2 (After merging the bam files): RNA_S10_merged.bam Ti RNAseq reads mapped to Contig 1 and 2 (After merging the bam files): RNA_TI_merged.bam using the notebook 01_align.ipynb . In that case, you just need to run the command below, linking the data in the results folder into the folder Data_for_VCF_analysis . In [4]: Copied! %% bash ln - fs ` realpath results / HIFI_alignment / PacBio_clover_alignment_1 . sort . bam ` Data_for_VCF_analysis / HIFI_contig_1 . bam ln - fs ` realpath results / HIFI_alignment / PacBio_clover_alignment_2 . sort . bam ` Data_for_VCF_analysis / HIFI_contig_2 . bam ln - fs ` realpath results / HIFI_alignment / PacBio_clover_alignment_1_2_asm20 . sort . bam ` Data_for_VCF_analysis / HIFI_contig_1_2 . bam ln - fs ` realpath results / STAR_output / S10_align_contigs_1_2_merge / S10 . sorted . bam ` Data_for_VCF_analysis / RNA_S10_merged . bam ln - fs ` realpath results / STAR_output / TI_align_contigs_1_2_merge / TI . sorted . bam ` Data_for_VCF_analysis / RNA_TI_merged . bam %%bash ln -fs `realpath results/HIFI_alignment/PacBio_clover_alignment_1.sort.bam` Data_for_VCF_analysis/HIFI_contig_1.bam ln -fs `realpath results/HIFI_alignment/PacBio_clover_alignment_2.sort.bam` Data_for_VCF_analysis/HIFI_contig_2.bam ln -fs `realpath results/HIFI_alignment/PacBio_clover_alignment_1_2_asm20.sort.bam` Data_for_VCF_analysis/HIFI_contig_1_2.bam ln -fs `realpath results/STAR_output/S10_align_contigs_1_2_merge/S10.sorted.bam` Data_for_VCF_analysis/RNA_S10_merged.bam ln -fs `realpath results/STAR_output/TI_align_contigs_1_2_merge/TI.sorted.bam` Data_for_VCF_analysis/RNA_TI_merged.bam Variant calling \u00b6 We will use a tool named bcftools to find variants in the alignment files ( .bam ) relative to the reference genome, in this case, the file DNA_Contig1_2.fasta . In this way we can identify positions in the genome where the sequence of the aligned reads differ from the reference genome they were mapped to. There are multiple types of genetic variation , e.g. SNPs (single nucleotide polymorphism), indels (insertions or deletions) and structural variations (such as chromosomal rearrangements). Here we will only be working with SNPs, thus we will use the terms variants and SNPs interchangeably. The called variants will be stored in a specific format file, the Variant Call Format ( VCF ). When looking at an alignment files in IGV you should easily recognize the SNP positions. You can see in the figure below that at the marked positions all the mapped reads have a mismatch compared to the reference. You will be calling two types of SNPs in this notebook. First, we will find subgenome SNPs using the Hifi reads mapped to white clover Contig1 and Contig2. These are positions in the contigs that are different between the two subgenomes of white clover. Secondly, we will find SNPs that differentiate between the sequenced white clover S10 (the reference genome) and Ti genotype, using RNA-seq read mapping. Create a folder that will store all the files generated in this notebook. In [5]: Copied! ! mkdir - p results / VCF_Files !mkdir -p results/VCF_Files Call subgenome SNPs using the Hifi alignments and the reference genome \u00b6 We will use the bcftools software to call SNPs from alignment files. bcftools is a toolkit for variant calling and manipulating VCF files. If you are interested, you can find all the functionalities here http://samtools.github.io/bcftools/bcftools.html#call . We mainly need two commands for this step, first bcftools mpileup which takes as input the alignment and the genome reference files, followed by bcftools call to produce VCF files. The cell below will generate 3 VCF files stored in the folder results/VCF_Files using the Hifi alignment files uploaded from Galaxy. In [6]: Copied! ! bcftools mpileup -- threads 4 - Ou -- skip - indels - f ../ Data / Clover_Data / DNA_Contig1_2 . fasta Data_for_VCF_analysis / HIFI_contig_1 . bam | bcftools call - mv - Ov - o results / VCF_Files / HIFI_Contig_1 . vcf ! bcftools mpileup -- threads 4 - Ou -- skip - indels - f ../ Data / Clover_Data / DNA_Contig1_2 . fasta Data_for_VCF_analysis / HIFI_contig_2 . bam | bcftools call - mv - Ov - o results / VCF_Files / HIFI_Contig_2 . vcf ! bcftools mpileup -- threads 4 - Ou -- skip - indels - f ../ Data / Clover_Data / DNA_Contig1_2 . fasta Data_for_VCF_analysis / HIFI_contig_1_2 . bam | bcftools call - mv - Ov - o results / VCF_Files / HIFI_Contig_1_2 . vcf !bcftools mpileup --threads 4 -Ou --skip-indels -f ../Data/Clover_Data/DNA_Contig1_2.fasta Data_for_VCF_analysis/HIFI_contig_1.bam | bcftools call -mv -Ov -o results/VCF_Files/HIFI_Contig_1.vcf !bcftools mpileup --threads 4 -Ou --skip-indels -f ../Data/Clover_Data/DNA_Contig1_2.fasta Data_for_VCF_analysis/HIFI_contig_2.bam | bcftools call -mv -Ov -o results/VCF_Files/HIFI_Contig_2.vcf !bcftools mpileup --threads 4 -Ou --skip-indels -f ../Data/Clover_Data/DNA_Contig1_2.fasta Data_for_VCF_analysis/HIFI_contig_1_2.bam | bcftools call -mv -Ov -o results/VCF_Files/HIFI_Contig_1_2.vcf Note: none of --samples-file, --ploidy or --ploidy-file given, assuming all sites are diploid [mpileup] 1 samples in 1 input files [mpileup] maximum number of reads per input file set to -d 250 Note: none of --samples-file, --ploidy or --ploidy-file given, assuming all sites are diploid [mpileup] 1 samples in 1 input files [mpileup] maximum number of reads per input file set to -d 250 Note: none of --samples-file, --ploidy or --ploidy-file given, assuming all sites are diploid [mpileup] 1 samples in 1 input files [mpileup] maximum number of reads per input file set to -d 250 TASK Inspect the VCF files using IGV, comparing them to the specific alignment BAM files. (You can download the VCF files to your computer from the results/VCF_Files folder) Are there any problematic positions that may not represent true SNPs? How can you use the HiFi reads aligned to Contigs1+2 identify potential problems? Hint: Try scrolling to the ends of the contigs. Call SNPs using the two RNA-seq genotypes, S10 and Ti \u00b6 We will repeat the same step as above this time using the RNA-seq alignment files for the two white clover genotypes (S10 and Ti). These commands will produce another two VCF files, stored in the same folder. In [7]: Copied! ! bcftools mpileup -- threads 4 - Ou -- skip - indels - f reference_data / DNA_Contig1_2 . fasta Data_for_VCF_analysis / RNA_S10_merged . bam | bcftools call - mv - Ov - o results / VCF_Files / RNA_S10_merged . vcf ! bcftools mpileup -- threads 4 - Ou -- skip - indels - f reference_data / DNA_Contig1_2 . fasta Data_for_VCF_analysis / RNA_TI_merged . bam | bcftools call - mv - Ov - o results / VCF_Files / RNA_TI_merged . vcf !bcftools mpileup --threads 4 -Ou --skip-indels -f reference_data/DNA_Contig1_2.fasta Data_for_VCF_analysis/RNA_S10_merged.bam | bcftools call -mv -Ov -o results/VCF_Files/RNA_S10_merged.vcf !bcftools mpileup --threads 4 -Ou --skip-indels -f reference_data/DNA_Contig1_2.fasta Data_for_VCF_analysis/RNA_TI_merged.bam | bcftools call -mv -Ov -o results/VCF_Files/RNA_TI_merged.vcf [mpileup] 1 samples in 1 input files Note: none of --samples-file, --ploidy or --ploidy-file given, assuming all sites are diploid [mpileup] maximum number of reads per input file set to -d 250 Note: none of --samples-file, --ploidy or --ploidy-file given, assuming all sites are diploid [mpileup] 1 samples in 1 input files [mpileup] maximum number of reads per input file set to -d 250 TASK Inspect one of the VCFs and the corresponding alignment files in IGV. Is it relevant to filter for false positives in this case, and what parameters would you look further into? VCF files processing and filtering \u00b6 Take a look at what one of the previously generated VCF files contains by running the cell below. A VCF file begins with the meta-information lines starting with ## followed by one header line, starting with # . Next, there are the data lines, each describing a genetic variant for a certain position in the genome. Each data line contains multiple tab-delimited columns with information about the variant. There are 9 fixed columns, labelled \u201cCHROM\u201d, \u201cPOS\u201d, \u201cID\u201d, \u201cREF\u201d, \u201cALT\u201d, \u201cQUAL\u201d, \u201cFILTER\u201d, \u201cINFO\u201d and \u201cFORMAT\u201d. Find more about the content of those columns in this wikipedia page . In the sample column you can find the most likely genotype of the sample. For diploid organisms, there will be a 0 value for reference allele and 1 for the alternate allele. Figure: possible genotypes in the data. For example, here we read the first 50 rows from the RNAseq_S10_merged.vcf file. You can see that the file format follows the same pattern as explained above. In [8]: Copied! ! cat results / VCF_Files / RNA_S10_merged . vcf | head - 50 !cat results/VCF_Files/RNA_S10_merged.vcf | head -50 ##fileformat=VCFv4.2 ##FILTER= ##bcftoolsVersion=1.16+htslib-1.17 ##bcftoolsCommand=mpileup --threads 4 -Ou --skip-indels -f reference_data/DNA_Contig1_2.fasta Data_for_VCF_analysis/RNA_S10_merged.bam ##reference=file://reference_data/DNA_Contig1_2.fasta ##contig= ##contig= ##ALT= ##INFO= ##INFO= ##INFO= ##INFO= ##INFO= ##INFO= ##INFO= ##INFO= ##INFO= ##INFO= ##INFO= ##INFO= ##INFO= ##INFO= ##FORMAT= ##FORMAT= ##INFO= ##INFO= ##INFO= ##INFO= ##bcftools_callVersion=1.16+htslib-1.17 ##bcftools_callCommand=call -mv -Ov -o results/VCF_Files/RNA_S10_merged.vcf; Date=Mon Jun 19 10:48:17 2023 #CHROM POS ID REF ALT QUAL FILTER INFO FORMAT Data_for_VCF_analysis/RNA_S10_merged.bam 1 33218 . G A 65.3262 . DP=88;VDB=0.0923504;SGB=-0.692914;RPBZ=3.99376;MQBZ=-1.86933;MQSBZ=-0.901828;BQBZ=-1.8368;SCBZ=2.6559;FS=0;MQ0F=0;AC=1;AN=2;DP4=15,13,16,9;MQ=19 GT:PL 0/1:98,0,138 1 33222 . C G 69.1967 . DP=56;VDB=4.89382e-06;SGB=-0.692352;RPBZ=3.47069;MQBZ=-1.73535;MQSBZ=-0.222384;BQBZ=-1.36705;SCBZ=2.16882;FS=0;MQ0F=0;AC=1;AN=2;DP4=18,13,12,9;MQ=19 GT:PL 0/1:102,0,138 1 33223 . T G 61.1405 . DP=56;VDB=2.99077e-05;SGB=-0.692067;RPBZ=3.40644;MQBZ=-1.80665;MQSBZ=-0.222384;BQBZ=0.830878;SCBZ=2.63256;FS=0;MQ0F=0;AC=1;AN=2;DP4=18,14,12,8;MQ=19 GT:PL 0/1:94,0,132 1 33224 . T G 65.1016 . DP=58;VDB=4.85907e-06;SGB=-0.692352;RPBZ=3.67489;MQBZ=-1.78978;MQSBZ=-0.159627;BQBZ=-1.43699;SCBZ=2.60016;FS=0;MQ0F=0;AC=1;AN=2;DP4=18,15,12,9;MQ=19 GT:PL 0/1:98,0,144 1 33225 . G T 64.0746 . DP=58;VDB=4.36523e-06;SGB=-0.692352;RPBZ=3.67489;MQBZ=-1.78978;MQSBZ=-0.159627;BQBZ=-2.60059;SCBZ=2.60016;FS=0;MQ0F=0;AC=1;AN=2;DP4=18,15,12,9;MQ=19 GT:PL 0/1:97,0,144 1 33227 . T A 64.067 . DP=59;VDB=4.09235e-06;SGB=-0.692352;RPBZ=4.13393;MQBZ=-1.81637;MQSBZ=-0.18317;BQBZ=-1.48941;SCBZ=2.64866;FS=0;MQ0F=0;AC=1;AN=2;DP4=19,15,12,9;MQ=19 GT:PL 0/1:97,0,144 1 49149 . C T 30.4183 . DP=4;VDB=0.0058656;SGB=-0.556411;FS=0;MQ0F=0;AC=2;AN=2;DP4=0,0,0,4;MQ=20 GT:PL 1/1:60,12,0 1 83968 . C G 78.0347 . DP=10;VDB=5.52906e-06;SGB=-0.662043;RPBZ=-3;MQBZ=0;MQSBZ=0;BQBZ=-0.333333;SCBZ=1.77187;FS=0;MQ0F=0;AC=2;AN=2;DP4=1,0,2,7;MQ=20 GT:PL 1/1:105,10,0 1 83970 . T C 78.0347 . DP=10;VDB=8.25997e-06;SGB=-0.662043;RPBZ=-1.79284;MQBZ=0;MQSBZ=0;BQBZ=0;SCBZ=1.77187;FS=0;MQ0F=0;AC=2;AN=2;DP4=1,0,2,7;MQ=20 GT:PL 1/1:105,10,0 1 83972 . G T 78.0347 . DP=10;VDB=8.25997e-06;SGB=-0.662043;RPBZ=-1.79284;MQBZ=0;MQSBZ=0;BQBZ=0;SCBZ=1.57628;FS=0;MQ0F=0;AC=2;AN=2;DP4=1,0,2,7;MQ=20 GT:PL 1/1:105,10,0 1 83975 . G A 78.0347 . DP=10;VDB=1.22485e-05;SGB=-0.662043;RPBZ=-1.79284;MQBZ=0;MQSBZ=0;BQBZ=0;SCBZ=1.57628;FS=0;MQ0F=0;AC=2;AN=2;DP4=1,0,2,7;MQ=20 GT:PL 1/1:105,10,0 1 174956 . C T 10.7923 . DP=2;VDB=0.14;SGB=-0.453602;MQSBZ=0;FS=0;MQ0F=0;AC=2;AN=2;DP4=0,0,1,1;MQ=20 GT:PL 1/1:40,6,0 1 175456 . A G 7.30814 . DP=2;VDB=0.02;SGB=-0.453602;FS=0;MQ0F=0;AC=2;AN=2;DP4=0,0,2,0;MQ=20 GT:PL 1/1:36,6,0 1 211292 . A T 16.7427 . DP=5;VDB=0.0058656;SGB=-0.556411;RPBZ=-2;MQBZ=0;MQSBZ=0;BQBZ=1.22474;SCBZ=2;FS=0;MQ0F=0;AC=1;AN=2;DP4=0,1,4,0;MQ=20 GT:PL 0/1:47,0,5 1 321659 . G A 13.5525 . DP=7;VDB=0.107235;SGB=-0.511536;RPBZ=1.34164;MQBZ=0;MQSBZ=0;BQBZ=1.73205;SCBZ=0.942809;FS=0;MQ0F=0;AC=1;AN=2;DP4=1,0,1,2;MQ=20 GT:PL 0/1:45,0,8 1 321661 . A C 13.5525 . DP=7;VDB=0.107235;SGB=-0.511536;RPBZ=1.34164;MQBZ=0;MQSBZ=0;BQBZ=1.73205;SCBZ=0.942809;FS=0;MQ0F=0;AC=1;AN=2;DP4=1,0,1,2;MQ=20 GT:PL 0/1:45,0,8 1 323166 . G A 7.30814 . DP=4;VDB=0.1;SGB=-0.453602;FS=0;MQ0F=0;AC=2;AN=2;DP4=0,0,0,2;MQ=20 GT:PL 1/1:36,6,0 1 331785 . A T 7.30814 . DP=2;VDB=0.02;SGB=-0.453602;FS=0;MQ0F=0;AC=2;AN=2;DP4=0,0,2,0;MQ=20 GT:PL 1/1:36,6,0 1 331890 . C T 39.4149 . DP=6;VDB=0.100421;SGB=-0.556411;MQSBZ=0;FS=0;MQ0F=0;AC=2;AN=2;DP4=0,0,3,1;MQ=20 GT:PL 1/1:69,12,0 Next, in order to generate plots of the metrics stored in the VCF files, we will first import the VCF files in Python using the scikit-allel package. This package contains a series of function helpful for processing variant calling files. For now, we will import only the VCF files generated using the Hifi data alignments. You can ignore the warning. In [9]: Copied! #Import the VCF files Hifi_Contig_1_vcf = allel . read_vcf ( 'results/VCF_Files/HIFI_Contig_1.vcf' , fields = '*' ) Hifi_Contig_2_vcf = allel . read_vcf ( 'results/VCF_Files/HIFI_Contig_2.vcf' , fields = '*' ) Hifi_Contig_1_2_vcf = allel . read_vcf ( 'results/VCF_Files/HIFI_Contig_1_2.vcf' , fields = '*' ) #Import the VCF files Hifi_Contig_1_vcf = allel.read_vcf('results/VCF_Files/HIFI_Contig_1.vcf', fields='*') Hifi_Contig_2_vcf = allel.read_vcf('results/VCF_Files/HIFI_Contig_2.vcf', fields='*') Hifi_Contig_1_2_vcf = allel.read_vcf('results/VCF_Files/HIFI_Contig_1_2.vcf', fields='*') /work/Course_Env/lib/python3.10/site-packages/allel/io/vcf_read.py:1732: UserWarning: invalid INFO header: '##INFO=\\n' warnings.warn('invalid INFO header: %r' % header) Let's check the number of SNPs in each of the three VCF files. The first two VCF files contain subgenome SNPs. You can see that we identified a have a high number of polymorphisms between the two subgenomes. On the other hand the Hifi_Contig1_2_vcf file contains what we would call false positives, as we called SNPs using the same Hifi reads that were used for assembling the reference genome. As expected the number of SNPs in this file is much smaller. In [10]: Copied! print ( \"Number of SNPs in Hifi Contig 1 vcf file:\" , Hifi_Contig_1_vcf [ 'variants/POS' ] . size ) print ( \"Number of SNPs in Hifi Contig 2 vcf file:\" , Hifi_Contig_2_vcf [ 'variants/POS' ] . size ) print ( \"Number of SNPs in Hifi Contig 1 and 2 vcf file:\" , Hifi_Contig_1_2_vcf [ 'variants/POS' ] . size ) print(\"Number of SNPs in Hifi Contig 1 vcf file:\", Hifi_Contig_1_vcf['variants/POS'].size) print(\"Number of SNPs in Hifi Contig 2 vcf file:\", Hifi_Contig_2_vcf['variants/POS'].size) print(\"Number of SNPs in Hifi Contig 1 and 2 vcf file:\", Hifi_Contig_1_2_vcf['variants/POS'].size) Number of SNPs in Hifi Contig 1 vcf file: 31981 Number of SNPs in Hifi Contig 2 vcf file: 30823 Number of SNPs in Hifi Contig 1 and 2 vcf file: 1309 As an overview of the distribution of SNPs, we can plot the density of SNPs across the contigs. We have defined a function called plot_variant_density in the file ../Scripts/pythonScripts.sh that does exactly that. We use that to plot the SNP density across the contigs in the three VCF files in bins of 5000 bp. We can approximate that a SNP density of 0.1 SNPs/bp repesents one polymorphic site at every ten nucleotides. You can see that for the two contigs there are regions more conserved between the two subgenomes, which show a low density of SNPs and more variable regions, which have a higher SNP density. In the third plot we can mainly observe SNPs at the beging of the contigs, as you have probably also oberved when inspecting the files in IGV. Taking a look at the scale of the y-axis you can oberve that the SNP density for Hifi_Contig1_2_vcf is much smaller compared to the other two files. Note: For easier plotting of the files that contain SNPs from both Contig 1 and 2, the SNP density is plotted as an overlay of both contigs. In [11]: Copied! #Plot SNP density plot_variant_density ( Hifi_Contig_1_vcf , window_size = 5000 , title = 'Contig 1 Raw SNP density' ) plot_variant_density ( Hifi_Contig_2_vcf , window_size = 5000 , title = 'Contig 2 Raw SNP density' ) plot_variant_density ( Hifi_Contig_1_2_vcf , window_size = 5000 , title = 'Contig 1 and 2 Raw SNP density' ) #Plot SNP density plot_variant_density(Hifi_Contig_1_vcf, window_size=5000, title='Contig 1 Raw SNP density') plot_variant_density(Hifi_Contig_2_vcf, window_size=5000, title='Contig 2 Raw SNP density') plot_variant_density(Hifi_Contig_1_2_vcf, window_size=5000, title='Contig 1 and 2 Raw SNP density') We can also plot some of the most relevant parameters from the VCF files, such as the read depth and the quality, to have an overview of the quality of the SNPs in these samples. We defined for that the function plot_variant_hist in the file ../Scripts/pythonScripts.sh . We can plot the metrics \"DP\" (Read depth), \"QUAL\" (Quality score), and \"MQ\" (Mapping quality for Hifi_Contig1_vcf . The x-axis represents the chosen metric, while the y-axis represents the number of SNPs. You can observe that the majority of the SNPs from this file have a high quality. In [12]: Copied! plot_variant_hist ( Hifi_Contig_1_vcf , 'DP' , \"mediumblue\" ) plot_variant_hist ( Hifi_Contig_1_vcf , 'QUAL' , \"forestgreen\" ) plot_variant_hist ( Hifi_Contig_1_vcf , 'MQ' , \"darkred\" ) plot_variant_hist(Hifi_Contig_1_vcf,'DP', \"mediumblue\") plot_variant_hist(Hifi_Contig_1_vcf, 'QUAL', \"forestgreen\") plot_variant_hist(Hifi_Contig_1_vcf, 'MQ', \"darkred\") TASK In the two cells below generate the same three plots as above using the VCF files for Contig 2 and Contig 1+2. You will just need to copy the three command lines from the cell above and replace the name of the VCF files. In [ ]: Copied! #Plot the metrics for Contig 2 #Plot the metrics for Contig 2 In [ ]: Copied! #Plot the metrics for Contig 1 + 2 #Plot the metrics for Contig 1 + 2 As you can observe in the plots above, there are some positions with very low read depth and low quality. Those might represent false-positive variants. Thus, we will filter the VCF files in order to eliminate those positions. For this purpose, we will use another bcftools function bcftools filter . We can pass thresholds for all the info metrics to this function using the \"-i\" flag. Below is just an example of the thresholds, you can change the values to suit your files. Note that this command will generate a new filtered VCF file in the \"VCF_Files\" Folder. In [ ]: Copied! ! bcftools filter - i '(QUAL>150) & (DP > 20) & (MQ > 40)' results / VCF_Files / HIFI_Contig_1 . vcf - O v > results / VCF_Files / HIFI_Contig_1_filtered . vcf !bcftools filter -i '(QUAL>150) & (DP > 20) & (MQ > 40)' results/VCF_Files/HIFI_Contig_1.vcf -O v > results/VCF_Files/HIFI_Contig_1_filtered.vcf In [ ]: Copied! ! bcftools filter - i '(QUAL>150) & (DP > 20) & (MQ > 40)' results / VCF_Files / HIFI_Contig_2 . vcf - O v > results / VCF_Files / HIFI_Contig_2_filtered . vcf !bcftools filter -i '(QUAL>150) & (DP > 20) & (MQ > 40)' results/VCF_Files/HIFI_Contig_2.vcf -O v > results/VCF_Files/HIFI_Contig_2_filtered.vcf In [ ]: Copied! ! bcftools filter - i '(QUAL>150) & (DP > 20) & (MQ > 40)' results / VCF_Files / HIFI_Contig_1_2 . vcf - O v > results / VCF_Files / HIFI_Contig_1_2_filtered . vcf !bcftools filter -i '(QUAL>150) & (DP > 20) & (MQ > 40)' results/VCF_Files/HIFI_Contig_1_2.vcf -O v > results/VCF_Files/HIFI_Contig_1_2_filtered.vcf We read the filtered VCF file for Contig1 in python and generate the same plots as before. We can read the VCF file and generate all the plots in one cell this time. We check how many positions have been eliminated by the filter and compare the plots with the ones for the unfiltered VCFs. If you consider that there are still positions with with low quality you can change the filter and repeat the process. In [ ]: Copied! Hifi_Contig1_filtered = allel . read_vcf ( 'results/VCF_Files/HIFI_Contig_1_filtered.vcf' , fields = '*' ) print ( \"Number of SNPs in Contig 1 VCF filtered file:\" , Hifi_Contig1_filtered [ 'variants/POS' ] . size ) #Plot SNP density plot_variant_density ( Hifi_Contig1_filtered , window_size = 5000 , title = 'Contig 1 Filtered variant density' ) #Plot metrics plot_variant_hist ( Hifi_Contig1_filtered , 'DP' , \"mediumblue\" ) plot_variant_hist ( Hifi_Contig1_filtered , 'QUAL' , \"forestgreen\" ) plot_variant_hist ( Hifi_Contig1_filtered , 'MQ' , \"darkred\" ) Hifi_Contig1_filtered = allel.read_vcf('results/VCF_Files/HIFI_Contig_1_filtered.vcf', fields='*') print(\"Number of SNPs in Contig 1 VCF filtered file:\", Hifi_Contig1_filtered['variants/POS'].size) #Plot SNP density plot_variant_density(Hifi_Contig1_filtered, window_size=5000, title='Contig 1 Filtered variant density') #Plot metrics plot_variant_hist(Hifi_Contig1_filtered,'DP', \"mediumblue\") plot_variant_hist(Hifi_Contig1_filtered, 'QUAL', \"forestgreen\") plot_variant_hist(Hifi_Contig1_filtered, 'MQ', \"darkred\") TASK This exercise covers the rest of the notebook and contains explanations as well . The code will work only if you complete correctly the empty cells. Some cells are filled in to help. Compare the VCF files before and after filtering in IGV. In the two cells below generate the same plots as above using the filtered VCF files for Contig 2 and Contig 1 + 2. You will just need to copy the three command lines from the cell above and replace the name of the VCF files. In [ ]: Copied! #Figures for Contig 2 Filtered VCF #Figures for Contig 2 Filtered VCF In [ ]: Copied! #Figures for Contig 1 + 2 Filtered VCF #Figures for Contig 1 + 2 Filtered VCF Repeat the quality check and filtering as described above for the two RNA-seq VCF files: Read the VCF files in Python: In [ ]: Copied! vcf_S10_RNA = allel . read_vcf ( 'results/VCF_Files/RNA_S10_merged.vcf' , fields = '*' ) vcf_Ti_RNA = allel . read_vcf ( 'results/VCF_Files/RNA_TI_merged.vcf' , fields = '*' ) vcf_S10_RNA = allel.read_vcf('results/VCF_Files/RNA_S10_merged.vcf', fields='*') vcf_Ti_RNA = allel.read_vcf('results/VCF_Files/RNA_TI_merged.vcf', fields='*') Print the number of SNPs in the two RNA-seq VCF files. Note that the number of SNPs is much smaller than for the Hifi VCF files. Also, there is a difference in the number of SNPs for the two genotypes. As S10 RNA-seq data originates from the same genotype as the referece genome, one would expect a small number of SNPs to be identified in this case. In [ ]: Copied! print ( \"Number of SNPs in S10 RNA-seq VCF file:\" , vcf_S10_RNA [ 'variants/POS' ] . size ) print ( \"Number of SNPs in Ti RNA-seq VCF file:\" , vcf_Ti_RNA [ 'variants/POS' ] . size ) print(\"Number of SNPs in S10 RNA-seq VCF file:\", vcf_S10_RNA['variants/POS'].size) print(\"Number of SNPs in Ti RNA-seq VCF file:\", vcf_Ti_RNA['variants/POS'].size) Generate SNP density plots using the plot_variant_density() function: In [ ]: Copied! In [ ]: Copied! Generate info histograms using the plot_variant_hist() function for the same fields. You should observe that the metrics for the RNA-seq vcf files are quite different from the ones using Hifi reads. In [ ]: Copied! In [ ]: Copied! Filter the VCF files using bcftools and your selected tresholds after inspecting the VCF files. Add the filter thresholds before running the command. In [ ]: Copied! ! bcftools filter - i 'add filter thresholds here' results / VCF_Files / RNA_S10_merged . vcf - O v > results / VCF_Files / RNA_S10_merged_filtered . vcf ! bcftools filter - i 'add filter thresholds here' results / VCF_Files / RNA_TI_merged . vcf - O v > results / VCF_Files / RNA_TI_merged_filtered . vcf !bcftools filter -i 'add filter thresholds here' results/VCF_Files/RNA_S10_merged.vcf -O v > results/VCF_Files/RNA_S10_merged_filtered.vcf !bcftools filter -i 'add filter thresholds here' results/VCF_Files/RNA_TI_merged.vcf -O v > results/VCF_Files/RNA_TI_merged_filtered.vcf Read the filtered VCFs in python: In [ ]: Copied! vcf_S10_RNA_filtered = allel . read_vcf ( 'results/VCF_Files/RNA_S10_merged_filtered.vcf' , fields = '*' ) vcf_Ti_RNA_filtered = allel . read_vcf ( 'results/VCF_Files/RNA_TI_merged_filtered.vcf' , fields = '*' ) vcf_S10_RNA_filtered = allel.read_vcf('results/VCF_Files/RNA_S10_merged_filtered.vcf', fields='*') vcf_Ti_RNA_filtered = allel.read_vcf('results/VCF_Files/RNA_TI_merged_filtered.vcf', fields='*') Check the number of SNPs in the filtered VCF files: In [ ]: Copied! print ( \"Number of SNPs in the filtered S10 RNA-seq VCF file:\" , vcf_S10_RNA_filtered [ 'variants/POS' ] . size ) print ( \"Number of SNPs in the filtered Ti RNA-seq VCF file:\" , vcf_Ti_RNA_filtered [ 'variants/POS' ] . size ) print(\"Number of SNPs in the filtered S10 RNA-seq VCF file:\", vcf_S10_RNA_filtered['variants/POS'].size) print(\"Number of SNPs in the filtered Ti RNA-seq VCF file:\", vcf_Ti_RNA_filtered['variants/POS'].size) Plot same histograms for the filtered VCF files. In [ ]: Copied! In [ ]: Copied! Question: Is it relevant to filter for false positives in this case, and what parameters would you look further into? Genotypes counting \u00b6 We want to count the different genotypes in each of the five filtered VCF files generated previously. To make this task easier we can take advantage of the counting functions in the scikit-allel package. We have used those to implement a little function called position_count in the file ../Scripts/pythonScripts.sh . You can then use the function with each of the 5 filtered VCF files to count the number of positions. You need to fill the names of the VCF files imported in Python. In [ ]: Copied! #Contig 1 filtered VCF position_count ( Hifi_Contig1_filtered ) #Contig 1 filtered VCF position_count(Hifi_Contig1_filtered) In [ ]: Copied! #Contig 2 filtered VCF position_count () #Contig 2 filtered VCF position_count() In [ ]: Copied! #Contig 1 and 2 filtered VCF position_count () #Contig 1 and 2 filtered VCF position_count() In [ ]: Copied! #RNAseq S10 filtered VCF position_count () #RNAseq S10 filtered VCF position_count() In [ ]: Copied! #RNAseq Ti filtered VCF position_count () #RNAseq Ti filtered VCF position_count() Comparison of VCF files \u00b6 Compare the VCF files with subgenome variants and the VCF files with S10/Tienshan genotype specific variants obtained from RNA-seq mapping We want now to find overlapping position between the Contig 1 and Contig 2 Hifi VCF files and the Ti RNAseq VCF file. To ease this task we will first create two merged VCF files containing: Hifi Contig 1 VCF + Ti RNAseq VCF Hifi Contig 2 VCF + Ti RNAseq VCF Once again, we will use bcftools for this task, using the merge function. However, the bcftools merge function works only with compressed and indexed VCF files, so first we will do this. In [ ]: Copied! ! bcftools view results / VCF_Files / HIFI_Contig_1_filtered . vcf - Oz - o results / VCF_Files / HIFI_Contig_1_filtered . vcf . gz ! bcftools index results / VCF_Files / HIFI_Contig_1_filtered . vcf . gz - f - o results / VCF_Files / HIFI_Contig_1_filtered . vcf . gz . csi ! bcftools view results / VCF_Files / HIFI_Contig_2_filtered . vcf - Oz - o results / VCF_Files / HIFI_Contig_2_filtered . vcf . gz ! bcftools index results / VCF_Files / HIFI_Contig_2_filtered . vcf . gz - f - o results / VCF_Files / HIFI_Contig_2_filtered . vcf . gz . csi ! bcftools view results / VCF_Files / RNA_TI_merged_filtered . vcf - Oz - o results / VCF_Files / RNA_TI_merged_filtered . vcf . gz ! bcftools index results / VCF_Files / RNA_TI_merged_filtered . vcf . gz - f - o results / VCF_Files / RNA_TI_merged_filtered . vcf . gz . csi !bcftools view results/VCF_Files/HIFI_Contig_1_filtered.vcf -Oz -o results/VCF_Files/HIFI_Contig_1_filtered.vcf.gz !bcftools index results/VCF_Files/HIFI_Contig_1_filtered.vcf.gz -f -o results/VCF_Files/HIFI_Contig_1_filtered.vcf.gz.csi !bcftools view results/VCF_Files/HIFI_Contig_2_filtered.vcf -Oz -o results/VCF_Files/HIFI_Contig_2_filtered.vcf.gz !bcftools index results/VCF_Files/HIFI_Contig_2_filtered.vcf.gz -f -o results/VCF_Files/HIFI_Contig_2_filtered.vcf.gz.csi !bcftools view results/VCF_Files/RNA_TI_merged_filtered.vcf -Oz -o results/VCF_Files/RNA_TI_merged_filtered.vcf.gz !bcftools index results/VCF_Files/RNA_TI_merged_filtered.vcf.gz -f -o results/VCF_Files/RNA_TI_merged_filtered.vcf.gz.csi Now we can merge Contig 1 VCF with the Ti RNA VCF and keep only positions that match between the two files by filtering out any positions with missing genotypes. In [ ]: Copied! ! bcftools merge results / VCF_Files / HIFI_Contig_1_filtered . vcf . gz results / VCF_Files / RNA_TI_merged_filtered . vcf . gz | bcftools filter - e 'GT=\"./.\"' > results / VCF_Files / TI_Contig_1_merged . vcf !bcftools merge results/VCF_Files/HIFI_Contig_1_filtered.vcf.gz results/VCF_Files/RNA_TI_merged_filtered.vcf.gz | bcftools filter -e 'GT=\"./.\"' > results/VCF_Files/TI_Contig_1_merged.vcf Merge Contig 2 VCF with the Ti RNA VCF and keep only positions that match between the two files. In [ ]: Copied! ! bcftools merge results / VCF_Files / HIFI_Contig_2_filtered . vcf . gz results / VCF_Files / RNA_TI_merged_filtered . vcf . gz | bcftools filter - e 'GT=\"./.\"' > results / VCF_Files / TI_Contig_2_merged . vcf !bcftools merge results/VCF_Files/HIFI_Contig_2_filtered.vcf.gz results/VCF_Files/RNA_TI_merged_filtered.vcf.gz | bcftools filter -e 'GT=\"./.\"' > results/VCF_Files/TI_Contig_2_merged.vcf Take a look at the merged VCF file. You can see that compared with the VCF files for just one sample, there is now a new column in the file, with the corresponding genotype for the second sample that we merged. In [ ]: Copied! ! cat results / VCF_Files / TI_Contig_1_merged . vcf | head - 50 !cat results/VCF_Files/TI_Contig_1_merged.vcf | head -50 In order to count the genotypes for the matching positions we can use once again bcftools filter command. Only that this time we will filter based on the genotype columns for the two samples (GT[0] and GT[1]). GT[0] repesents the genotype for the first sample in the merge command and GT[1] is the genotype for the second sample in the merge command. AR - heterozygous AA - heterozygous alternate For example, the filter GT[0]=\"AR\" && GT[1]=\"AR\" will keep only position in which both samples have a heterozygous genotype. A quick way to count how many positions are in the VCF file is by suing the simple bash command grep . This will count all the lines in the VCF files that do not start with a # , thus all the data lines. In [ ]: Copied! ! echo \"AR + AR:\" ! bcftools filter - i 'GT[0]=\"AR\" && GT[1]=\"AR\"' results / VCF_Files / TI_Contig_1_merged . vcf | grep - c - v \"^#\" ! echo \"AR + AA:\" ! bcftools filter - i 'GT[0]=\"AR\" && GT[1]=\"AA\"' results / VCF_Files / TI_Contig_1_merged . vcf | grep - c - v \"^#\" ! echo \"AA + AR:\" ! bcftools filter - i 'GT[0]=\"AA\" && GT[1]=\"AR\"' results / VCF_Files / TI_Contig_1_merged . vcf | grep - c - v \"^#\" ! echo \"AA + AA:\" ! bcftools filter - i 'GT[0]=\"AA\" && GT[1]=\"AA\"' results / VCF_Files / TI_Contig_1_merged . vcf | grep - c - v \"^#\" !echo \"AR + AR:\" !bcftools filter -i 'GT[0]=\"AR\" && GT[1]=\"AR\"' results/VCF_Files/TI_Contig_1_merged.vcf | grep -c -v \"^#\" !echo \"AR + AA:\" !bcftools filter -i 'GT[0]=\"AR\" && GT[1]=\"AA\"' results/VCF_Files/TI_Contig_1_merged.vcf | grep -c -v \"^#\" !echo \"AA + AR:\" !bcftools filter -i 'GT[0]=\"AA\" && GT[1]=\"AR\"' results/VCF_Files/TI_Contig_1_merged.vcf | grep -c -v \"^#\" !echo \"AA + AA:\" !bcftools filter -i 'GT[0]=\"AA\" && GT[1]=\"AA\"' results/VCF_Files/TI_Contig_1_merged.vcf | grep -c -v \"^#\" Use the same commands as above to count the positions marching the filters in the Contig2_Ti_merged.vcf. In [ ]: Copied! Wrapping up \ud83c\udf89 \ud83c\udf89 \ud83c\udf89 \u00b6 In this exercise, you identified polymorphisms between the two white clover subgenomes using Hifi reads alignment files and SNPs specific for the white clover Tienshan genotype using RNA-seq alignment files. You learned how to import VCF files in Python to extract data and created plots. You also filtered low-quality variants from the VCF files and counted genotypes.","title":"3.2.Variant calling analysis"},{"location":"nb/vcf.html#use-the-bam-files-generated-from-the-alignment-of-raw-reads","text":"We will use the data created during the alignment session of the course. You had two possibilities: using the interactive Galaxy platform as explained in the course webpage . In this case you will need to upload the some of the created data into the folder Data_for_VCF_analysis , renaming the files as follows: Hifi reads mapped to Contig1: HIFI_contig_1.bam Hifi reads mapped to Contig2: HIFI_contig_2.bam Hifi reads mapped to Contig 1 and 2: HIFI_contig_1_2_asm20.bam (you can of course choose the one with the map-pb alignment option from the alignment notebook) S10 RNAseq reads mapped to Contig 1 and 2 (After merging the bam files): RNA_S10_merged.bam Ti RNAseq reads mapped to Contig 1 and 2 (After merging the bam files): RNA_TI_merged.bam using the notebook 01_align.ipynb . In that case, you just need to run the command below, linking the data in the results folder into the folder Data_for_VCF_analysis . In [4]: Copied! %% bash ln - fs ` realpath results / HIFI_alignment / PacBio_clover_alignment_1 . sort . bam ` Data_for_VCF_analysis / HIFI_contig_1 . bam ln - fs ` realpath results / HIFI_alignment / PacBio_clover_alignment_2 . sort . bam ` Data_for_VCF_analysis / HIFI_contig_2 . bam ln - fs ` realpath results / HIFI_alignment / PacBio_clover_alignment_1_2_asm20 . sort . bam ` Data_for_VCF_analysis / HIFI_contig_1_2 . bam ln - fs ` realpath results / STAR_output / S10_align_contigs_1_2_merge / S10 . sorted . bam ` Data_for_VCF_analysis / RNA_S10_merged . bam ln - fs ` realpath results / STAR_output / TI_align_contigs_1_2_merge / TI . sorted . bam ` Data_for_VCF_analysis / RNA_TI_merged . bam %%bash ln -fs `realpath results/HIFI_alignment/PacBio_clover_alignment_1.sort.bam` Data_for_VCF_analysis/HIFI_contig_1.bam ln -fs `realpath results/HIFI_alignment/PacBio_clover_alignment_2.sort.bam` Data_for_VCF_analysis/HIFI_contig_2.bam ln -fs `realpath results/HIFI_alignment/PacBio_clover_alignment_1_2_asm20.sort.bam` Data_for_VCF_analysis/HIFI_contig_1_2.bam ln -fs `realpath results/STAR_output/S10_align_contigs_1_2_merge/S10.sorted.bam` Data_for_VCF_analysis/RNA_S10_merged.bam ln -fs `realpath results/STAR_output/TI_align_contigs_1_2_merge/TI.sorted.bam` Data_for_VCF_analysis/RNA_TI_merged.bam","title":"Use the bam files generated from the alignment of raw reads"},{"location":"nb/vcf.html#variant-calling","text":"We will use a tool named bcftools to find variants in the alignment files ( .bam ) relative to the reference genome, in this case, the file DNA_Contig1_2.fasta . In this way we can identify positions in the genome where the sequence of the aligned reads differ from the reference genome they were mapped to. There are multiple types of genetic variation , e.g. SNPs (single nucleotide polymorphism), indels (insertions or deletions) and structural variations (such as chromosomal rearrangements). Here we will only be working with SNPs, thus we will use the terms variants and SNPs interchangeably. The called variants will be stored in a specific format file, the Variant Call Format ( VCF ). When looking at an alignment files in IGV you should easily recognize the SNP positions. You can see in the figure below that at the marked positions all the mapped reads have a mismatch compared to the reference. You will be calling two types of SNPs in this notebook. First, we will find subgenome SNPs using the Hifi reads mapped to white clover Contig1 and Contig2. These are positions in the contigs that are different between the two subgenomes of white clover. Secondly, we will find SNPs that differentiate between the sequenced white clover S10 (the reference genome) and Ti genotype, using RNA-seq read mapping. Create a folder that will store all the files generated in this notebook. In [5]: Copied! ! mkdir - p results / VCF_Files !mkdir -p results/VCF_Files","title":"Variant calling"},{"location":"nb/vcf.html#call-subgenome-snps-using-the-hifi-alignments-and-the-reference-genome","text":"We will use the bcftools software to call SNPs from alignment files. bcftools is a toolkit for variant calling and manipulating VCF files. If you are interested, you can find all the functionalities here http://samtools.github.io/bcftools/bcftools.html#call . We mainly need two commands for this step, first bcftools mpileup which takes as input the alignment and the genome reference files, followed by bcftools call to produce VCF files. The cell below will generate 3 VCF files stored in the folder results/VCF_Files using the Hifi alignment files uploaded from Galaxy. In [6]: Copied! ! bcftools mpileup -- threads 4 - Ou -- skip - indels - f ../ Data / Clover_Data / DNA_Contig1_2 . fasta Data_for_VCF_analysis / HIFI_contig_1 . bam | bcftools call - mv - Ov - o results / VCF_Files / HIFI_Contig_1 . vcf ! bcftools mpileup -- threads 4 - Ou -- skip - indels - f ../ Data / Clover_Data / DNA_Contig1_2 . fasta Data_for_VCF_analysis / HIFI_contig_2 . bam | bcftools call - mv - Ov - o results / VCF_Files / HIFI_Contig_2 . vcf ! bcftools mpileup -- threads 4 - Ou -- skip - indels - f ../ Data / Clover_Data / DNA_Contig1_2 . fasta Data_for_VCF_analysis / HIFI_contig_1_2 . bam | bcftools call - mv - Ov - o results / VCF_Files / HIFI_Contig_1_2 . vcf !bcftools mpileup --threads 4 -Ou --skip-indels -f ../Data/Clover_Data/DNA_Contig1_2.fasta Data_for_VCF_analysis/HIFI_contig_1.bam | bcftools call -mv -Ov -o results/VCF_Files/HIFI_Contig_1.vcf !bcftools mpileup --threads 4 -Ou --skip-indels -f ../Data/Clover_Data/DNA_Contig1_2.fasta Data_for_VCF_analysis/HIFI_contig_2.bam | bcftools call -mv -Ov -o results/VCF_Files/HIFI_Contig_2.vcf !bcftools mpileup --threads 4 -Ou --skip-indels -f ../Data/Clover_Data/DNA_Contig1_2.fasta Data_for_VCF_analysis/HIFI_contig_1_2.bam | bcftools call -mv -Ov -o results/VCF_Files/HIFI_Contig_1_2.vcf Note: none of --samples-file, --ploidy or --ploidy-file given, assuming all sites are diploid [mpileup] 1 samples in 1 input files [mpileup] maximum number of reads per input file set to -d 250 Note: none of --samples-file, --ploidy or --ploidy-file given, assuming all sites are diploid [mpileup] 1 samples in 1 input files [mpileup] maximum number of reads per input file set to -d 250 Note: none of --samples-file, --ploidy or --ploidy-file given, assuming all sites are diploid [mpileup] 1 samples in 1 input files [mpileup] maximum number of reads per input file set to -d 250 TASK Inspect the VCF files using IGV, comparing them to the specific alignment BAM files. (You can download the VCF files to your computer from the results/VCF_Files folder) Are there any problematic positions that may not represent true SNPs? How can you use the HiFi reads aligned to Contigs1+2 identify potential problems? Hint: Try scrolling to the ends of the contigs.","title":"Call subgenome SNPs using the Hifi alignments and the reference genome"},{"location":"nb/vcf.html#call-snps-using-the-two-rna-seq-genotypes-s10-and-ti","text":"We will repeat the same step as above this time using the RNA-seq alignment files for the two white clover genotypes (S10 and Ti). These commands will produce another two VCF files, stored in the same folder. In [7]: Copied! ! bcftools mpileup -- threads 4 - Ou -- skip - indels - f reference_data / DNA_Contig1_2 . fasta Data_for_VCF_analysis / RNA_S10_merged . bam | bcftools call - mv - Ov - o results / VCF_Files / RNA_S10_merged . vcf ! bcftools mpileup -- threads 4 - Ou -- skip - indels - f reference_data / DNA_Contig1_2 . fasta Data_for_VCF_analysis / RNA_TI_merged . bam | bcftools call - mv - Ov - o results / VCF_Files / RNA_TI_merged . vcf !bcftools mpileup --threads 4 -Ou --skip-indels -f reference_data/DNA_Contig1_2.fasta Data_for_VCF_analysis/RNA_S10_merged.bam | bcftools call -mv -Ov -o results/VCF_Files/RNA_S10_merged.vcf !bcftools mpileup --threads 4 -Ou --skip-indels -f reference_data/DNA_Contig1_2.fasta Data_for_VCF_analysis/RNA_TI_merged.bam | bcftools call -mv -Ov -o results/VCF_Files/RNA_TI_merged.vcf [mpileup] 1 samples in 1 input files Note: none of --samples-file, --ploidy or --ploidy-file given, assuming all sites are diploid [mpileup] maximum number of reads per input file set to -d 250 Note: none of --samples-file, --ploidy or --ploidy-file given, assuming all sites are diploid [mpileup] 1 samples in 1 input files [mpileup] maximum number of reads per input file set to -d 250 TASK Inspect one of the VCFs and the corresponding alignment files in IGV. Is it relevant to filter for false positives in this case, and what parameters would you look further into?","title":"Call SNPs using the two RNA-seq genotypes, S10 and Ti"},{"location":"nb/vcf.html#vcf-files-processing-and-filtering","text":"Take a look at what one of the previously generated VCF files contains by running the cell below. A VCF file begins with the meta-information lines starting with ## followed by one header line, starting with # . Next, there are the data lines, each describing a genetic variant for a certain position in the genome. Each data line contains multiple tab-delimited columns with information about the variant. There are 9 fixed columns, labelled \u201cCHROM\u201d, \u201cPOS\u201d, \u201cID\u201d, \u201cREF\u201d, \u201cALT\u201d, \u201cQUAL\u201d, \u201cFILTER\u201d, \u201cINFO\u201d and \u201cFORMAT\u201d. Find more about the content of those columns in this wikipedia page . In the sample column you can find the most likely genotype of the sample. For diploid organisms, there will be a 0 value for reference allele and 1 for the alternate allele. Figure: possible genotypes in the data. For example, here we read the first 50 rows from the RNAseq_S10_merged.vcf file. You can see that the file format follows the same pattern as explained above. In [8]: Copied! ! cat results / VCF_Files / RNA_S10_merged . vcf | head - 50 !cat results/VCF_Files/RNA_S10_merged.vcf | head -50 ##fileformat=VCFv4.2 ##FILTER= ##bcftoolsVersion=1.16+htslib-1.17 ##bcftoolsCommand=mpileup --threads 4 -Ou --skip-indels -f reference_data/DNA_Contig1_2.fasta Data_for_VCF_analysis/RNA_S10_merged.bam ##reference=file://reference_data/DNA_Contig1_2.fasta ##contig= ##contig= ##ALT= ##INFO= ##INFO= ##INFO= ##INFO= ##INFO= ##INFO= ##INFO= ##INFO= ##INFO= ##INFO= ##INFO= ##INFO= ##INFO= ##INFO= ##FORMAT= ##FORMAT= ##INFO= ##INFO= ##INFO= ##INFO= ##bcftools_callVersion=1.16+htslib-1.17 ##bcftools_callCommand=call -mv -Ov -o results/VCF_Files/RNA_S10_merged.vcf; Date=Mon Jun 19 10:48:17 2023 #CHROM POS ID REF ALT QUAL FILTER INFO FORMAT Data_for_VCF_analysis/RNA_S10_merged.bam 1 33218 . G A 65.3262 . DP=88;VDB=0.0923504;SGB=-0.692914;RPBZ=3.99376;MQBZ=-1.86933;MQSBZ=-0.901828;BQBZ=-1.8368;SCBZ=2.6559;FS=0;MQ0F=0;AC=1;AN=2;DP4=15,13,16,9;MQ=19 GT:PL 0/1:98,0,138 1 33222 . C G 69.1967 . DP=56;VDB=4.89382e-06;SGB=-0.692352;RPBZ=3.47069;MQBZ=-1.73535;MQSBZ=-0.222384;BQBZ=-1.36705;SCBZ=2.16882;FS=0;MQ0F=0;AC=1;AN=2;DP4=18,13,12,9;MQ=19 GT:PL 0/1:102,0,138 1 33223 . T G 61.1405 . DP=56;VDB=2.99077e-05;SGB=-0.692067;RPBZ=3.40644;MQBZ=-1.80665;MQSBZ=-0.222384;BQBZ=0.830878;SCBZ=2.63256;FS=0;MQ0F=0;AC=1;AN=2;DP4=18,14,12,8;MQ=19 GT:PL 0/1:94,0,132 1 33224 . T G 65.1016 . DP=58;VDB=4.85907e-06;SGB=-0.692352;RPBZ=3.67489;MQBZ=-1.78978;MQSBZ=-0.159627;BQBZ=-1.43699;SCBZ=2.60016;FS=0;MQ0F=0;AC=1;AN=2;DP4=18,15,12,9;MQ=19 GT:PL 0/1:98,0,144 1 33225 . G T 64.0746 . DP=58;VDB=4.36523e-06;SGB=-0.692352;RPBZ=3.67489;MQBZ=-1.78978;MQSBZ=-0.159627;BQBZ=-2.60059;SCBZ=2.60016;FS=0;MQ0F=0;AC=1;AN=2;DP4=18,15,12,9;MQ=19 GT:PL 0/1:97,0,144 1 33227 . T A 64.067 . DP=59;VDB=4.09235e-06;SGB=-0.692352;RPBZ=4.13393;MQBZ=-1.81637;MQSBZ=-0.18317;BQBZ=-1.48941;SCBZ=2.64866;FS=0;MQ0F=0;AC=1;AN=2;DP4=19,15,12,9;MQ=19 GT:PL 0/1:97,0,144 1 49149 . C T 30.4183 . DP=4;VDB=0.0058656;SGB=-0.556411;FS=0;MQ0F=0;AC=2;AN=2;DP4=0,0,0,4;MQ=20 GT:PL 1/1:60,12,0 1 83968 . C G 78.0347 . DP=10;VDB=5.52906e-06;SGB=-0.662043;RPBZ=-3;MQBZ=0;MQSBZ=0;BQBZ=-0.333333;SCBZ=1.77187;FS=0;MQ0F=0;AC=2;AN=2;DP4=1,0,2,7;MQ=20 GT:PL 1/1:105,10,0 1 83970 . T C 78.0347 . DP=10;VDB=8.25997e-06;SGB=-0.662043;RPBZ=-1.79284;MQBZ=0;MQSBZ=0;BQBZ=0;SCBZ=1.77187;FS=0;MQ0F=0;AC=2;AN=2;DP4=1,0,2,7;MQ=20 GT:PL 1/1:105,10,0 1 83972 . G T 78.0347 . DP=10;VDB=8.25997e-06;SGB=-0.662043;RPBZ=-1.79284;MQBZ=0;MQSBZ=0;BQBZ=0;SCBZ=1.57628;FS=0;MQ0F=0;AC=2;AN=2;DP4=1,0,2,7;MQ=20 GT:PL 1/1:105,10,0 1 83975 . G A 78.0347 . DP=10;VDB=1.22485e-05;SGB=-0.662043;RPBZ=-1.79284;MQBZ=0;MQSBZ=0;BQBZ=0;SCBZ=1.57628;FS=0;MQ0F=0;AC=2;AN=2;DP4=1,0,2,7;MQ=20 GT:PL 1/1:105,10,0 1 174956 . C T 10.7923 . DP=2;VDB=0.14;SGB=-0.453602;MQSBZ=0;FS=0;MQ0F=0;AC=2;AN=2;DP4=0,0,1,1;MQ=20 GT:PL 1/1:40,6,0 1 175456 . A G 7.30814 . DP=2;VDB=0.02;SGB=-0.453602;FS=0;MQ0F=0;AC=2;AN=2;DP4=0,0,2,0;MQ=20 GT:PL 1/1:36,6,0 1 211292 . A T 16.7427 . DP=5;VDB=0.0058656;SGB=-0.556411;RPBZ=-2;MQBZ=0;MQSBZ=0;BQBZ=1.22474;SCBZ=2;FS=0;MQ0F=0;AC=1;AN=2;DP4=0,1,4,0;MQ=20 GT:PL 0/1:47,0,5 1 321659 . G A 13.5525 . DP=7;VDB=0.107235;SGB=-0.511536;RPBZ=1.34164;MQBZ=0;MQSBZ=0;BQBZ=1.73205;SCBZ=0.942809;FS=0;MQ0F=0;AC=1;AN=2;DP4=1,0,1,2;MQ=20 GT:PL 0/1:45,0,8 1 321661 . A C 13.5525 . DP=7;VDB=0.107235;SGB=-0.511536;RPBZ=1.34164;MQBZ=0;MQSBZ=0;BQBZ=1.73205;SCBZ=0.942809;FS=0;MQ0F=0;AC=1;AN=2;DP4=1,0,1,2;MQ=20 GT:PL 0/1:45,0,8 1 323166 . G A 7.30814 . DP=4;VDB=0.1;SGB=-0.453602;FS=0;MQ0F=0;AC=2;AN=2;DP4=0,0,0,2;MQ=20 GT:PL 1/1:36,6,0 1 331785 . A T 7.30814 . DP=2;VDB=0.02;SGB=-0.453602;FS=0;MQ0F=0;AC=2;AN=2;DP4=0,0,2,0;MQ=20 GT:PL 1/1:36,6,0 1 331890 . C T 39.4149 . DP=6;VDB=0.100421;SGB=-0.556411;MQSBZ=0;FS=0;MQ0F=0;AC=2;AN=2;DP4=0,0,3,1;MQ=20 GT:PL 1/1:69,12,0 Next, in order to generate plots of the metrics stored in the VCF files, we will first import the VCF files in Python using the scikit-allel package. This package contains a series of function helpful for processing variant calling files. For now, we will import only the VCF files generated using the Hifi data alignments. You can ignore the warning. In [9]: Copied! #Import the VCF files Hifi_Contig_1_vcf = allel . read_vcf ( 'results/VCF_Files/HIFI_Contig_1.vcf' , fields = '*' ) Hifi_Contig_2_vcf = allel . read_vcf ( 'results/VCF_Files/HIFI_Contig_2.vcf' , fields = '*' ) Hifi_Contig_1_2_vcf = allel . read_vcf ( 'results/VCF_Files/HIFI_Contig_1_2.vcf' , fields = '*' ) #Import the VCF files Hifi_Contig_1_vcf = allel.read_vcf('results/VCF_Files/HIFI_Contig_1.vcf', fields='*') Hifi_Contig_2_vcf = allel.read_vcf('results/VCF_Files/HIFI_Contig_2.vcf', fields='*') Hifi_Contig_1_2_vcf = allel.read_vcf('results/VCF_Files/HIFI_Contig_1_2.vcf', fields='*') /work/Course_Env/lib/python3.10/site-packages/allel/io/vcf_read.py:1732: UserWarning: invalid INFO header: '##INFO=\\n' warnings.warn('invalid INFO header: %r' % header) Let's check the number of SNPs in each of the three VCF files. The first two VCF files contain subgenome SNPs. You can see that we identified a have a high number of polymorphisms between the two subgenomes. On the other hand the Hifi_Contig1_2_vcf file contains what we would call false positives, as we called SNPs using the same Hifi reads that were used for assembling the reference genome. As expected the number of SNPs in this file is much smaller. In [10]: Copied! print ( \"Number of SNPs in Hifi Contig 1 vcf file:\" , Hifi_Contig_1_vcf [ 'variants/POS' ] . size ) print ( \"Number of SNPs in Hifi Contig 2 vcf file:\" , Hifi_Contig_2_vcf [ 'variants/POS' ] . size ) print ( \"Number of SNPs in Hifi Contig 1 and 2 vcf file:\" , Hifi_Contig_1_2_vcf [ 'variants/POS' ] . size ) print(\"Number of SNPs in Hifi Contig 1 vcf file:\", Hifi_Contig_1_vcf['variants/POS'].size) print(\"Number of SNPs in Hifi Contig 2 vcf file:\", Hifi_Contig_2_vcf['variants/POS'].size) print(\"Number of SNPs in Hifi Contig 1 and 2 vcf file:\", Hifi_Contig_1_2_vcf['variants/POS'].size) Number of SNPs in Hifi Contig 1 vcf file: 31981 Number of SNPs in Hifi Contig 2 vcf file: 30823 Number of SNPs in Hifi Contig 1 and 2 vcf file: 1309 As an overview of the distribution of SNPs, we can plot the density of SNPs across the contigs. We have defined a function called plot_variant_density in the file ../Scripts/pythonScripts.sh that does exactly that. We use that to plot the SNP density across the contigs in the three VCF files in bins of 5000 bp. We can approximate that a SNP density of 0.1 SNPs/bp repesents one polymorphic site at every ten nucleotides. You can see that for the two contigs there are regions more conserved between the two subgenomes, which show a low density of SNPs and more variable regions, which have a higher SNP density. In the third plot we can mainly observe SNPs at the beging of the contigs, as you have probably also oberved when inspecting the files in IGV. Taking a look at the scale of the y-axis you can oberve that the SNP density for Hifi_Contig1_2_vcf is much smaller compared to the other two files. Note: For easier plotting of the files that contain SNPs from both Contig 1 and 2, the SNP density is plotted as an overlay of both contigs. In [11]: Copied! #Plot SNP density plot_variant_density ( Hifi_Contig_1_vcf , window_size = 5000 , title = 'Contig 1 Raw SNP density' ) plot_variant_density ( Hifi_Contig_2_vcf , window_size = 5000 , title = 'Contig 2 Raw SNP density' ) plot_variant_density ( Hifi_Contig_1_2_vcf , window_size = 5000 , title = 'Contig 1 and 2 Raw SNP density' ) #Plot SNP density plot_variant_density(Hifi_Contig_1_vcf, window_size=5000, title='Contig 1 Raw SNP density') plot_variant_density(Hifi_Contig_2_vcf, window_size=5000, title='Contig 2 Raw SNP density') plot_variant_density(Hifi_Contig_1_2_vcf, window_size=5000, title='Contig 1 and 2 Raw SNP density') We can also plot some of the most relevant parameters from the VCF files, such as the read depth and the quality, to have an overview of the quality of the SNPs in these samples. We defined for that the function plot_variant_hist in the file ../Scripts/pythonScripts.sh . We can plot the metrics \"DP\" (Read depth), \"QUAL\" (Quality score), and \"MQ\" (Mapping quality for Hifi_Contig1_vcf . The x-axis represents the chosen metric, while the y-axis represents the number of SNPs. You can observe that the majority of the SNPs from this file have a high quality. In [12]: Copied! plot_variant_hist ( Hifi_Contig_1_vcf , 'DP' , \"mediumblue\" ) plot_variant_hist ( Hifi_Contig_1_vcf , 'QUAL' , \"forestgreen\" ) plot_variant_hist ( Hifi_Contig_1_vcf , 'MQ' , \"darkred\" ) plot_variant_hist(Hifi_Contig_1_vcf,'DP', \"mediumblue\") plot_variant_hist(Hifi_Contig_1_vcf, 'QUAL', \"forestgreen\") plot_variant_hist(Hifi_Contig_1_vcf, 'MQ', \"darkred\") TASK In the two cells below generate the same three plots as above using the VCF files for Contig 2 and Contig 1+2. You will just need to copy the three command lines from the cell above and replace the name of the VCF files. In [ ]: Copied! #Plot the metrics for Contig 2 #Plot the metrics for Contig 2 In [ ]: Copied! #Plot the metrics for Contig 1 + 2 #Plot the metrics for Contig 1 + 2 As you can observe in the plots above, there are some positions with very low read depth and low quality. Those might represent false-positive variants. Thus, we will filter the VCF files in order to eliminate those positions. For this purpose, we will use another bcftools function bcftools filter . We can pass thresholds for all the info metrics to this function using the \"-i\" flag. Below is just an example of the thresholds, you can change the values to suit your files. Note that this command will generate a new filtered VCF file in the \"VCF_Files\" Folder. In [ ]: Copied! ! bcftools filter - i '(QUAL>150) & (DP > 20) & (MQ > 40)' results / VCF_Files / HIFI_Contig_1 . vcf - O v > results / VCF_Files / HIFI_Contig_1_filtered . vcf !bcftools filter -i '(QUAL>150) & (DP > 20) & (MQ > 40)' results/VCF_Files/HIFI_Contig_1.vcf -O v > results/VCF_Files/HIFI_Contig_1_filtered.vcf In [ ]: Copied! ! bcftools filter - i '(QUAL>150) & (DP > 20) & (MQ > 40)' results / VCF_Files / HIFI_Contig_2 . vcf - O v > results / VCF_Files / HIFI_Contig_2_filtered . vcf !bcftools filter -i '(QUAL>150) & (DP > 20) & (MQ > 40)' results/VCF_Files/HIFI_Contig_2.vcf -O v > results/VCF_Files/HIFI_Contig_2_filtered.vcf In [ ]: Copied! ! bcftools filter - i '(QUAL>150) & (DP > 20) & (MQ > 40)' results / VCF_Files / HIFI_Contig_1_2 . vcf - O v > results / VCF_Files / HIFI_Contig_1_2_filtered . vcf !bcftools filter -i '(QUAL>150) & (DP > 20) & (MQ > 40)' results/VCF_Files/HIFI_Contig_1_2.vcf -O v > results/VCF_Files/HIFI_Contig_1_2_filtered.vcf We read the filtered VCF file for Contig1 in python and generate the same plots as before. We can read the VCF file and generate all the plots in one cell this time. We check how many positions have been eliminated by the filter and compare the plots with the ones for the unfiltered VCFs. If you consider that there are still positions with with low quality you can change the filter and repeat the process. In [ ]: Copied! Hifi_Contig1_filtered = allel . read_vcf ( 'results/VCF_Files/HIFI_Contig_1_filtered.vcf' , fields = '*' ) print ( \"Number of SNPs in Contig 1 VCF filtered file:\" , Hifi_Contig1_filtered [ 'variants/POS' ] . size ) #Plot SNP density plot_variant_density ( Hifi_Contig1_filtered , window_size = 5000 , title = 'Contig 1 Filtered variant density' ) #Plot metrics plot_variant_hist ( Hifi_Contig1_filtered , 'DP' , \"mediumblue\" ) plot_variant_hist ( Hifi_Contig1_filtered , 'QUAL' , \"forestgreen\" ) plot_variant_hist ( Hifi_Contig1_filtered , 'MQ' , \"darkred\" ) Hifi_Contig1_filtered = allel.read_vcf('results/VCF_Files/HIFI_Contig_1_filtered.vcf', fields='*') print(\"Number of SNPs in Contig 1 VCF filtered file:\", Hifi_Contig1_filtered['variants/POS'].size) #Plot SNP density plot_variant_density(Hifi_Contig1_filtered, window_size=5000, title='Contig 1 Filtered variant density') #Plot metrics plot_variant_hist(Hifi_Contig1_filtered,'DP', \"mediumblue\") plot_variant_hist(Hifi_Contig1_filtered, 'QUAL', \"forestgreen\") plot_variant_hist(Hifi_Contig1_filtered, 'MQ', \"darkred\") TASK This exercise covers the rest of the notebook and contains explanations as well . The code will work only if you complete correctly the empty cells. Some cells are filled in to help. Compare the VCF files before and after filtering in IGV. In the two cells below generate the same plots as above using the filtered VCF files for Contig 2 and Contig 1 + 2. You will just need to copy the three command lines from the cell above and replace the name of the VCF files. In [ ]: Copied! #Figures for Contig 2 Filtered VCF #Figures for Contig 2 Filtered VCF In [ ]: Copied! #Figures for Contig 1 + 2 Filtered VCF #Figures for Contig 1 + 2 Filtered VCF Repeat the quality check and filtering as described above for the two RNA-seq VCF files: Read the VCF files in Python: In [ ]: Copied! vcf_S10_RNA = allel . read_vcf ( 'results/VCF_Files/RNA_S10_merged.vcf' , fields = '*' ) vcf_Ti_RNA = allel . read_vcf ( 'results/VCF_Files/RNA_TI_merged.vcf' , fields = '*' ) vcf_S10_RNA = allel.read_vcf('results/VCF_Files/RNA_S10_merged.vcf', fields='*') vcf_Ti_RNA = allel.read_vcf('results/VCF_Files/RNA_TI_merged.vcf', fields='*') Print the number of SNPs in the two RNA-seq VCF files. Note that the number of SNPs is much smaller than for the Hifi VCF files. Also, there is a difference in the number of SNPs for the two genotypes. As S10 RNA-seq data originates from the same genotype as the referece genome, one would expect a small number of SNPs to be identified in this case. In [ ]: Copied! print ( \"Number of SNPs in S10 RNA-seq VCF file:\" , vcf_S10_RNA [ 'variants/POS' ] . size ) print ( \"Number of SNPs in Ti RNA-seq VCF file:\" , vcf_Ti_RNA [ 'variants/POS' ] . size ) print(\"Number of SNPs in S10 RNA-seq VCF file:\", vcf_S10_RNA['variants/POS'].size) print(\"Number of SNPs in Ti RNA-seq VCF file:\", vcf_Ti_RNA['variants/POS'].size) Generate SNP density plots using the plot_variant_density() function: In [ ]: Copied! In [ ]: Copied! Generate info histograms using the plot_variant_hist() function for the same fields. You should observe that the metrics for the RNA-seq vcf files are quite different from the ones using Hifi reads. In [ ]: Copied! In [ ]: Copied! Filter the VCF files using bcftools and your selected tresholds after inspecting the VCF files. Add the filter thresholds before running the command. In [ ]: Copied! ! bcftools filter - i 'add filter thresholds here' results / VCF_Files / RNA_S10_merged . vcf - O v > results / VCF_Files / RNA_S10_merged_filtered . vcf ! bcftools filter - i 'add filter thresholds here' results / VCF_Files / RNA_TI_merged . vcf - O v > results / VCF_Files / RNA_TI_merged_filtered . vcf !bcftools filter -i 'add filter thresholds here' results/VCF_Files/RNA_S10_merged.vcf -O v > results/VCF_Files/RNA_S10_merged_filtered.vcf !bcftools filter -i 'add filter thresholds here' results/VCF_Files/RNA_TI_merged.vcf -O v > results/VCF_Files/RNA_TI_merged_filtered.vcf Read the filtered VCFs in python: In [ ]: Copied! vcf_S10_RNA_filtered = allel . read_vcf ( 'results/VCF_Files/RNA_S10_merged_filtered.vcf' , fields = '*' ) vcf_Ti_RNA_filtered = allel . read_vcf ( 'results/VCF_Files/RNA_TI_merged_filtered.vcf' , fields = '*' ) vcf_S10_RNA_filtered = allel.read_vcf('results/VCF_Files/RNA_S10_merged_filtered.vcf', fields='*') vcf_Ti_RNA_filtered = allel.read_vcf('results/VCF_Files/RNA_TI_merged_filtered.vcf', fields='*') Check the number of SNPs in the filtered VCF files: In [ ]: Copied! print ( \"Number of SNPs in the filtered S10 RNA-seq VCF file:\" , vcf_S10_RNA_filtered [ 'variants/POS' ] . size ) print ( \"Number of SNPs in the filtered Ti RNA-seq VCF file:\" , vcf_Ti_RNA_filtered [ 'variants/POS' ] . size ) print(\"Number of SNPs in the filtered S10 RNA-seq VCF file:\", vcf_S10_RNA_filtered['variants/POS'].size) print(\"Number of SNPs in the filtered Ti RNA-seq VCF file:\", vcf_Ti_RNA_filtered['variants/POS'].size) Plot same histograms for the filtered VCF files. In [ ]: Copied! In [ ]: Copied! Question: Is it relevant to filter for false positives in this case, and what parameters would you look further into?","title":"VCF files processing and filtering"},{"location":"nb/vcf.html#genotypes-counting","text":"We want to count the different genotypes in each of the five filtered VCF files generated previously. To make this task easier we can take advantage of the counting functions in the scikit-allel package. We have used those to implement a little function called position_count in the file ../Scripts/pythonScripts.sh . You can then use the function with each of the 5 filtered VCF files to count the number of positions. You need to fill the names of the VCF files imported in Python. In [ ]: Copied! #Contig 1 filtered VCF position_count ( Hifi_Contig1_filtered ) #Contig 1 filtered VCF position_count(Hifi_Contig1_filtered) In [ ]: Copied! #Contig 2 filtered VCF position_count () #Contig 2 filtered VCF position_count() In [ ]: Copied! #Contig 1 and 2 filtered VCF position_count () #Contig 1 and 2 filtered VCF position_count() In [ ]: Copied! #RNAseq S10 filtered VCF position_count () #RNAseq S10 filtered VCF position_count() In [ ]: Copied! #RNAseq Ti filtered VCF position_count () #RNAseq Ti filtered VCF position_count()","title":"Genotypes counting"},{"location":"nb/vcf.html#comparison-of-vcf-files","text":"Compare the VCF files with subgenome variants and the VCF files with S10/Tienshan genotype specific variants obtained from RNA-seq mapping We want now to find overlapping position between the Contig 1 and Contig 2 Hifi VCF files and the Ti RNAseq VCF file. To ease this task we will first create two merged VCF files containing: Hifi Contig 1 VCF + Ti RNAseq VCF Hifi Contig 2 VCF + Ti RNAseq VCF Once again, we will use bcftools for this task, using the merge function. However, the bcftools merge function works only with compressed and indexed VCF files, so first we will do this. In [ ]: Copied! ! bcftools view results / VCF_Files / HIFI_Contig_1_filtered . vcf - Oz - o results / VCF_Files / HIFI_Contig_1_filtered . vcf . gz ! bcftools index results / VCF_Files / HIFI_Contig_1_filtered . vcf . gz - f - o results / VCF_Files / HIFI_Contig_1_filtered . vcf . gz . csi ! bcftools view results / VCF_Files / HIFI_Contig_2_filtered . vcf - Oz - o results / VCF_Files / HIFI_Contig_2_filtered . vcf . gz ! bcftools index results / VCF_Files / HIFI_Contig_2_filtered . vcf . gz - f - o results / VCF_Files / HIFI_Contig_2_filtered . vcf . gz . csi ! bcftools view results / VCF_Files / RNA_TI_merged_filtered . vcf - Oz - o results / VCF_Files / RNA_TI_merged_filtered . vcf . gz ! bcftools index results / VCF_Files / RNA_TI_merged_filtered . vcf . gz - f - o results / VCF_Files / RNA_TI_merged_filtered . vcf . gz . csi !bcftools view results/VCF_Files/HIFI_Contig_1_filtered.vcf -Oz -o results/VCF_Files/HIFI_Contig_1_filtered.vcf.gz !bcftools index results/VCF_Files/HIFI_Contig_1_filtered.vcf.gz -f -o results/VCF_Files/HIFI_Contig_1_filtered.vcf.gz.csi !bcftools view results/VCF_Files/HIFI_Contig_2_filtered.vcf -Oz -o results/VCF_Files/HIFI_Contig_2_filtered.vcf.gz !bcftools index results/VCF_Files/HIFI_Contig_2_filtered.vcf.gz -f -o results/VCF_Files/HIFI_Contig_2_filtered.vcf.gz.csi !bcftools view results/VCF_Files/RNA_TI_merged_filtered.vcf -Oz -o results/VCF_Files/RNA_TI_merged_filtered.vcf.gz !bcftools index results/VCF_Files/RNA_TI_merged_filtered.vcf.gz -f -o results/VCF_Files/RNA_TI_merged_filtered.vcf.gz.csi Now we can merge Contig 1 VCF with the Ti RNA VCF and keep only positions that match between the two files by filtering out any positions with missing genotypes. In [ ]: Copied! ! bcftools merge results / VCF_Files / HIFI_Contig_1_filtered . vcf . gz results / VCF_Files / RNA_TI_merged_filtered . vcf . gz | bcftools filter - e 'GT=\"./.\"' > results / VCF_Files / TI_Contig_1_merged . vcf !bcftools merge results/VCF_Files/HIFI_Contig_1_filtered.vcf.gz results/VCF_Files/RNA_TI_merged_filtered.vcf.gz | bcftools filter -e 'GT=\"./.\"' > results/VCF_Files/TI_Contig_1_merged.vcf Merge Contig 2 VCF with the Ti RNA VCF and keep only positions that match between the two files. In [ ]: Copied! ! bcftools merge results / VCF_Files / HIFI_Contig_2_filtered . vcf . gz results / VCF_Files / RNA_TI_merged_filtered . vcf . gz | bcftools filter - e 'GT=\"./.\"' > results / VCF_Files / TI_Contig_2_merged . vcf !bcftools merge results/VCF_Files/HIFI_Contig_2_filtered.vcf.gz results/VCF_Files/RNA_TI_merged_filtered.vcf.gz | bcftools filter -e 'GT=\"./.\"' > results/VCF_Files/TI_Contig_2_merged.vcf Take a look at the merged VCF file. You can see that compared with the VCF files for just one sample, there is now a new column in the file, with the corresponding genotype for the second sample that we merged. In [ ]: Copied! ! cat results / VCF_Files / TI_Contig_1_merged . vcf | head - 50 !cat results/VCF_Files/TI_Contig_1_merged.vcf | head -50 In order to count the genotypes for the matching positions we can use once again bcftools filter command. Only that this time we will filter based on the genotype columns for the two samples (GT[0] and GT[1]). GT[0] repesents the genotype for the first sample in the merge command and GT[1] is the genotype for the second sample in the merge command. AR - heterozygous AA - heterozygous alternate For example, the filter GT[0]=\"AR\" && GT[1]=\"AR\" will keep only position in which both samples have a heterozygous genotype. A quick way to count how many positions are in the VCF file is by suing the simple bash command grep . This will count all the lines in the VCF files that do not start with a # , thus all the data lines. In [ ]: Copied! ! echo \"AR + AR:\" ! bcftools filter - i 'GT[0]=\"AR\" && GT[1]=\"AR\"' results / VCF_Files / TI_Contig_1_merged . vcf | grep - c - v \"^#\" ! echo \"AR + AA:\" ! bcftools filter - i 'GT[0]=\"AR\" && GT[1]=\"AA\"' results / VCF_Files / TI_Contig_1_merged . vcf | grep - c - v \"^#\" ! echo \"AA + AR:\" ! bcftools filter - i 'GT[0]=\"AA\" && GT[1]=\"AR\"' results / VCF_Files / TI_Contig_1_merged . vcf | grep - c - v \"^#\" ! echo \"AA + AA:\" ! bcftools filter - i 'GT[0]=\"AA\" && GT[1]=\"AA\"' results / VCF_Files / TI_Contig_1_merged . vcf | grep - c - v \"^#\" !echo \"AR + AR:\" !bcftools filter -i 'GT[0]=\"AR\" && GT[1]=\"AR\"' results/VCF_Files/TI_Contig_1_merged.vcf | grep -c -v \"^#\" !echo \"AR + AA:\" !bcftools filter -i 'GT[0]=\"AR\" && GT[1]=\"AA\"' results/VCF_Files/TI_Contig_1_merged.vcf | grep -c -v \"^#\" !echo \"AA + AR:\" !bcftools filter -i 'GT[0]=\"AA\" && GT[1]=\"AR\"' results/VCF_Files/TI_Contig_1_merged.vcf | grep -c -v \"^#\" !echo \"AA + AA:\" !bcftools filter -i 'GT[0]=\"AA\" && GT[1]=\"AA\"' results/VCF_Files/TI_Contig_1_merged.vcf | grep -c -v \"^#\" Use the same commands as above to count the positions marching the filters in the Contig2_Ti_merged.vcf. In [ ]: Copied!","title":"Comparison of VCF files"},{"location":"nb/vcf.html#wrapping-up","text":"In this exercise, you identified polymorphisms between the two white clover subgenomes using Hifi reads alignment files and SNPs specific for the white clover Tienshan genotype using RNA-seq alignment files. You learned how to import VCF files in Python to extract data and created plots. 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