chopchop

Description

• Analysis: Pipeline to use chopchop API using command shell. Design guides for CRISPR screnning.
• Date: 11/04/23
• Author: Agustín Sánchez Belmonte (asanchezb@cnio.es)
• Institution: Spanish National Research Cancer Centre (CNIO)

Table of contents

• Workflow
• Contents of the repository
• Pipeline
• Recomendations

Workflow

Contents of the repository

• The bash script chop_pipeline.sh that can be used for obtaining target sites for CRISPR.
• config_env.yml which is a file for create the working enviroment.

The output of this bash script includes:

• <gen>.txt which are target sites for CRISPR.

There are other outputs less important such as off-targets sequences.

Pipeline

Step 1: Clone the repo in your home

If you have not git package installed

conda install -c anaconda git

Then clone the entire repository in your local space

git clone https://bitbucket.org/valenlab/chopchop.git
cd chopchop

Step 2a: Update conda and create the environment

conda update --all
conda env create -f config_env.yml
conda activate chopchop

If this step doesn´t work, doing step 2b.

Step 2b: Create new enviroment

conda update --all
conda env create -n chopchop
conda activate chopchop
conda install -c anaconda biopython pandas numpy scipy argparse mysql-python scikit-learn=0.18.1

Step 3: Create table

chopchop.py will need a table to look up genomic coordinates if you want to supply names of the genes rather than coordinates. To get example genePred table:

• Select organism and assembly
• Select group: Genes and Gene Predictions
• Select track: RefSeq Genes or Ensemble Genes
• Select table: refGene or ensGene
• Select region: genome
• Select output format: all fields from selected table
• Fill name with extension ".gene_table' e.g. danRer10.gene_table
• Get output

mkdir genePred_folder

Save file.gene_table inside of genePred_folder.

Step 4: Download genome

Download *.2bit compressed genome:

• Select organism in complete annotation sets section
• Select Full data set
• download *.2bit file

mkdir 2bit_folder
wget -P 2bit_folder http://hgdownload.soe.ucsc.edu/goldenPath/hg38/bigZips/latest/hg38.2bit

Step 5: Download genome

Create fasta version of genome by running twoBitToFa on *.2bit file

./twoBitToFa 2bit_folder/hg38.2bit hg38.fasta

Step 6: Create bowtie version

Make bowtie compressed version of genome using your new *.fasta file

mkdir ebwt_folder
./bowtie/bowtie-build hg38.fasta ebwt_folder/hg38

Step 6: Create bowtie version

Change config.json file, replace paths with your own for .2bit genome files, bowtie (.ewbt) genome files and *.gene_table files

Observe config.json in order to see an example.

Step 7: Permissions

Make sure all these files and programs have proper access rights. You can use the chmod command in order to change permissions. Maybe some packages may require compilation for your operating system.

Step 8a: Run KO pipeline (single gen)

You must run this in your terminal shell and in gen must type the name of the interest gen (be carefull, you must write gene name correctly, some genes have several names, but it is only in one way).

./chopchop.py -G hg38 -o results -Target <gen> --scoringMethod DOENCH_2016 -consensusUnion -t CODING > results/<gen>.txt

• -G is the genome to search
• -o output folder
• -Target Target genes or regions
• -t Target the whole gene CODING/WHOLE/UTR5/UTR3/SPLICE
• -consensusUnion this option specifies union of isoforms

When the gene is very small, the design the guides will fail and -t WHOLE is recommended.

Step 8b: Run Activation pipeline (single gen)

You must run this in your terminal shell and in gen must type the name of the interest gen (be carefull, you must write gene name correctly, some genes have several names, but it is only in one way).

./chopchop.py -G hg38 -o results -Target <gen> --scoringMethod DOENCH_2016 -consensusUnion -t PROMOTER -TDP 0 -TUP 300 > results/<gen>.txt

• -t Promoter
• -TDP how many bp to target downstream of TSS
• -TUP how many bp to target upstream of TSS

Step 9: Run pipeline (several genes)

You must run this in your terminal shell and type interest genes separated by spaces.

bash chop_pipeline.sh <gen1> <gen2> <gen3> <gen4>

Recomendations

chopchop.py has a lot of funtionalities and arguments that you can change, it would be well for you observe this in the chochop link or doing this:

./chopchop.py --help