+>
Replace Text
> 1. {% tool [Replace](toolshed.g2.bx.psu.edu/repos/bgruening/text_processing/tp_find_and_replace/1.1.4) %} with the following parameters:
> - {% icon param-file %} *"File to process"*: `report` (output of **ABRicate** {% icon tool %})
> - In *"Find and Replace"*:
diff --git a/topics/proteomics/tutorials/encyclopedia/tutorial.md b/topics/proteomics/tutorials/encyclopedia/tutorial.md
index c2414058e75b5b..04dfc2a7e96e37 100644
--- a/topics/proteomics/tutorials/encyclopedia/tutorial.md
+++ b/topics/proteomics/tutorials/encyclopedia/tutorial.md
@@ -102,7 +102,7 @@ In a typical DIA-MS experiment, the precursor scan usually ranges between 400-10
> https://zenodo.org/records/13505774/files/191023JAT06_P_1ug_595_705_4_20.raw
> https://zenodo.org/records/13505774/files/191023JAT07_P_1ug_695_805_4_20.raw
> https://zenodo.org/records/13505774/files/191023JAT08_P_1ug_795_905_4_20.raw
-> ttps://zenodo.org/records/13505774/files/191023JAT09_P_1ug_895_1005_4_20.raw
+> https://zenodo.org/records/13505774/files/191023JAT09_P_1ug_895_1005_4_20.raw
> https://zenodo.org/record/4926594/files/T4_Salmonella_Ecoli_Bacillus_BS_191102.fasta
> https://zenodo.org/record/4926594/files/T4_Salmonella_Ecoli_Bacillus_fasta_trypsin_z2_nce33_BS_191102.dlib
> ```
diff --git a/topics/single-cell/tutorials/GO-enrichment/tutorial.md b/topics/single-cell/tutorials/GO-enrichment/tutorial.md
index 94fa4ada897f6a..7fb0e6343736d9 100644
--- a/topics/single-cell/tutorials/GO-enrichment/tutorial.md
+++ b/topics/single-cell/tutorials/GO-enrichment/tutorial.md
@@ -37,6 +37,11 @@ contributors:
- MennaGamal
---
+In the tutorial [Filter, plot and explore single-cell RNA-seq data with Scanpy]({% link topics/single-cell/tutorials/scrna-case_basic-pipeline/tutorial.md %}), we took an important step in our single-cell RNA sequencing analysis by identifying marker genes for each of the clusters in our dataset. These marker genes are crucial, as they help us distinguish between different cell types and states, giving us a clearer picture of the cellular diversity within our samples.
+However, simply identifying marker genes is just the beginning. To truly understand what makes each cluster unique, we need to dive deeper into the biological functions these genes are involved in. This is where Gene Ontology (GO) enrichment analysis comes into play.
+We will perform GO enrichment analysis as a type of over-representation analysis (ORA), ORA is a statistical method that determines whether genes from pre-defined sets (e.g. genes belonging to a specific GO term) are expressed more than would be expected in a subset of your data. The most commonly used statistical tests are Fischer's exact test and hypergeometric test, more details about them are explained in the tutorial slides.
+
+
>
>
> In this tutorial, we will cover:
@@ -46,13 +51,6 @@ contributors:
>
{: .agenda}
-# Introduction
-
-In the tutorial [Filter, plot and explore single-cell RNA-seq data with Scanpy]({% link topics/single-cell/tutorials/scrna-case_basic-pipeline/tutorial.md %}), we took an important step in our single-cell RNA sequencing analysis by identifying marker genes for each of the clusters in our dataset. These marker genes are crucial, as they help us distinguish between different cell types and states, giving us a clearer picture of the cellular diversity within our samples.
-However, simply identifying marker genes is just the beginning. To truly understand what makes each cluster unique, we need to dive deeper into the biological functions these genes are involved in. This is where Gene Ontology (GO) enrichment analysis comes into play.
-We will perform GO enrichment analysis as a type of over-representation analysis (ORA), ORA is a statistical method that determines whether genes from pre-defined sets (e.g. genes belonging to a specific GO term) are expressed more than would be expected in a subset of your data. The most commonly used statistical tests are Fischer's exact test and hypergeometric test, more details about them are explained in the tutorial slides.
-
-
# Data description
In this tutorial will use the following datasets:
diff --git a/topics/single-cell/tutorials/scrna-case_alevin-combine-datasets/tutorial.md b/topics/single-cell/tutorials/scrna-case_alevin-combine-datasets/tutorial.md
index a7660669da7187..12ef70a8ac2a4d 100644
--- a/topics/single-cell/tutorials/scrna-case_alevin-combine-datasets/tutorial.md
+++ b/topics/single-cell/tutorials/scrna-case_alevin-combine-datasets/tutorial.md
@@ -169,11 +169,11 @@ You can access the data for this tutorial in multiple ways:
> - {% icon param-file %} *"Annotated data matrix"*: `N701-400k`
> - *"Function to manipulate the object"*: `Concatenate along the observations axis`
> - {% icon param-file %} *"Annotated data matrix to add"*: `Select all the other matrix files from bottom to top, N702 to N707`
->
->
-> >If you imported files from Zenodo instead of using the input history, yours might not be in the same order as ours. Since the files will be concatenated in the order that you click, it will be helpful if you click them in the same order, from N702 to N707. This will ensure your samples are given the same batch numbers as we got in this tutorial, which will help when we're adding in metadata later!
- {: .comment}
->
+>
+> >
+> > If you imported files from Zenodo instead of using the input history, yours might not be in the same order as ours. Since the files will be concatenated in the order that you click, it will be helpful if you click them in the same order, from N702 to N707. This will ensure your samples are given the same batch numbers as we got in this tutorial, which will help when we're adding in metadata later!
+> {: .comment}
+>
> >
Don't add N701!
> > You are adding files to N701, so do not add N701 to itself!
> {: .warning}
diff --git a/topics/transcriptomics/tutorials/differential-isoform-expression/tutorial.md b/topics/transcriptomics/tutorials/differential-isoform-expression/tutorial.md
index cc3344a78430a7..c3baebd597f86f 100644
--- a/topics/transcriptomics/tutorials/differential-isoform-expression/tutorial.md
+++ b/topics/transcriptomics/tutorials/differential-isoform-expression/tutorial.md
@@ -131,10 +131,12 @@ Next we will retrieve the remaining datasets.
>
{: .hands_on}
-
+{: .details}
+-->
# Quality assessment
diff --git a/topics/variant-analysis/tutorials/beacon_cnv_query/tutorial.md b/topics/variant-analysis/tutorials/beacon_cnv_query/tutorial.md
index 8e13fd30effed9..11113bb2ce83fb 100644
--- a/topics/variant-analysis/tutorials/beacon_cnv_query/tutorial.md
+++ b/topics/variant-analysis/tutorials/beacon_cnv_query/tutorial.md
@@ -86,30 +86,30 @@ Those parametars are, "CHROMOSOME", "Start", and "End".
> >
> > What types of information can be extracted from records?
> >
-> > > ```json
-> > >{'_id': ObjectId('66c466431ea6cb4184ee0f2f'),
-> > > 'assemblyId': 'GRCh38',
-> > > 'biosampleId': 'MP2PRT-PARNFH-TMP1-A, MP2PRT-PARNFH-NM1-A',
-> > > 'definitions': {'Location': {'chromosome': '17',
-> > > 'end': 43170245,
-> > > 'start': 43044295}},
-> > > 'diseaseType': 'acute lymphoblastic leukemia',
-> > > 'gene': 'BRCA1',
-> > > 'geneID': 'ENSG00000012048.23',
-> > > 'id': 'refvar-66c466431ea6cb4184ee0f2f',
-> > > 'info': {'caseID': 'MP2PRT-PARNFH, MP2PRT-PARNFH',
-> > > 'cnCount': 3,
-> > > 'fileName': 'f11b7fb7-a610-4978-b5c4-523450a0fd5f.wgs.ASCAT.gene_level.copy_number_variation.tsv',
-> > > 'legacyId': 'DUP:chr17:43044295-43170245',
-> > > 'projectID': 'MP2PRT-ALL',
-> > > 'sampleType': 'Blood Derived Cancer - Bone Marrow, Blood Derived '
-> > > 'Cancer - Bone Marrow, Post-treatment'},
-> > > 'primarySite': 'hematopoietic and reticuloendothelial systems',
-> > > 'updated': '2024-08-19T21:23:09.374531',
-> > > 'variantInternalId': '17:43044295-43170245:EFO:0030071',
-> > > 'variantState': {'id': 'EFO:0030071', 'label': 'low-level gain'},
-> > > 'variantType': 'DUP'}
-> > > ```
+> > ```json
+> > {'_id': ObjectId('66c466431ea6cb4184ee0f2f'),
+> > 'assemblyId': 'GRCh38',
+> > 'biosampleId': 'MP2PRT-PARNFH-TMP1-A, MP2PRT-PARNFH-NM1-A',
+> > 'definitions': {'Location': {'chromosome': '17',
+> > 'end': 43170245,
+> > 'start': 43044295}},
+> > 'diseaseType': 'acute lymphoblastic leukemia',
+> > 'gene': 'BRCA1',
+> > 'geneID': 'ENSG00000012048.23',
+> > 'id': 'refvar-66c466431ea6cb4184ee0f2f',
+> > 'info': {'caseID': 'MP2PRT-PARNFH, MP2PRT-PARNFH',
+> > 'cnCount': 3,
+> > 'fileName': 'f11b7fb7-a610-4978-b5c4-523450a0fd5f.wgs.ASCAT.gene_level.copy_number_variation.tsv',
+> > 'legacyId': 'DUP:chr17:43044295-43170245',
+> > 'projectID': 'MP2PRT-ALL',
+> > 'sampleType': 'Blood Derived Cancer - Bone Marrow, Blood Derived '
+> > 'Cancer - Bone Marrow, Post-treatment'},
+> > 'primarySite': 'hematopoietic and reticuloendothelial systems',
+> > 'updated': '2024-08-19T21:23:09.374531',
+> > 'variantInternalId': '17:43044295-43170245:EFO:0030071',
+> > 'variantState': {'id': 'EFO:0030071', 'label': 'low-level gain'},
+> > 'variantType': 'DUP'}
+> > ```
> >
> > >
> > > 1. Identifiers and IDs:
diff --git a/topics/variant-analysis/tutorials/beaconise_1000hg/tutorial.md b/topics/variant-analysis/tutorials/beaconise_1000hg/tutorial.md
index 58a383a6c9f5cb..bc8f7449ad4e7a 100644
--- a/topics/variant-analysis/tutorials/beaconise_1000hg/tutorial.md
+++ b/topics/variant-analysis/tutorials/beaconise_1000hg/tutorial.md
@@ -109,45 +109,45 @@ We will use docker and docker-compose for this step. If you don't have it instal
> nano docker-compose.yaml
> ```
> 4. Copy the text below into the `docker-compose.yaml` file
-> >```yaml
-> > version: '3.6'
-> > services:
-> >
-> > mongo-client:
-> > image: mongo:3.6
-> > restart: unless-stopped
-> > volumes:
-> > - ./mongo/db:/data/db
-> > - ./mongo-init:/docker-entrypoint-initdb.d
-> > ports:
-> > - "27017:27017"
-> > environment:
-> > MONGO_INITDB_ROOT_USERNAME: root
-> > MONGO_INITDB_ROOT_PASSWORD: example
-> >
-> > mongo-express:
-> > image: mongo-express
-> > restart: unless-stopped
-> > environment:
-> > - ME_CONFIG_MONGODB_SERVER=mongo-client
-> > - ME_CONFIG_MONGODB_PORT=27017
-> > - ME_CONFIG_BASICAUTH_USERNAME=root
-> > - ME_CONFIG_BASICAUTH_PASSWORD=example
-> > ports:
-> > - "8081:8081"
-> >
-> > mongo-init:
-> > image: mongo:3.6
-> > restart: "no"
-> > depends_on:
-> > - mongo-client
-> > environment:
-> > - MONGO_INITDB_DATABASE=admin
-> > - MONGO_INITDB_ROOT_USERNAME=root
-> > - MONGO_INITDB_ROOT_PASSWORD=example
-> > volumes:
-> > - ./mongo-init:/docker-entrypoint-initdb.d
-> > ```
+> ```yaml
+> version: '3.6'
+> services:
+>
+> mongo-client:
+> image: mongo:3.6
+> restart: unless-stopped
+> volumes:
+> - ./mongo/db:/data/db
+> - ./mongo-init:/docker-entrypoint-initdb.d
+> ports:
+> - "27017:27017"
+> environment:
+> MONGO_INITDB_ROOT_USERNAME: root
+> MONGO_INITDB_ROOT_PASSWORD: example
+>
+> mongo-express:
+> image: mongo-express
+> restart: unless-stopped
+> environment:
+> - ME_CONFIG_MONGODB_SERVER=mongo-client
+> - ME_CONFIG_MONGODB_PORT=27017
+> - ME_CONFIG_BASICAUTH_USERNAME=root
+> - ME_CONFIG_BASICAUTH_PASSWORD=example
+> ports:
+> - "8081:8081"
+>
+> mongo-init:
+> image: mongo:3.6
+> restart: "no"
+> depends_on:
+> - mongo-client
+> environment:
+> - MONGO_INITDB_DATABASE=admin
+> - MONGO_INITDB_ROOT_USERNAME=root
+> - MONGO_INITDB_ROOT_PASSWORD=example
+> volumes:
+> - ./mongo-init:/docker-entrypoint-initdb.d
+> ```
> 5. Create the path `mongo/db` in your directory using `$mkdir` tool
> ```bash
> mkdir mongo
@@ -168,23 +168,23 @@ We will use docker and docker-compose for this step. If you don't have it instal
> nano create-user.js
> ```
> 9. Copy the text below into the `create-user.js` file
-> >```js
-> > // create_user.js
-> >
-> > // Connect to the admin database
-> > var adminDB = db.getSiblingDB("admin");
-> >
-> > // Create a new user with read-only access to all databases
-> > adminDB.createUser({
-> > user: "query_user",
-> > pwd: "querypassword",
-> > roles: [
-> > { role: "read", db: "admin" },
-> > { role: "read", db: "Beacon" }, // Adjust this for your needs
-> > // Add additional read roles as needed
-> > ]
-> >});
-> > ```
+> ```js
+> // create_user.js
+>
+> // Connect to the admin database
+> var adminDB = db.getSiblingDB("admin");
+>
+> // Create a new user with read-only access to all databases
+> adminDB.createUser({
+> user: "query_user",
+> pwd: "querypassword",
+> roles: [
+> { role: "read", db: "admin" },
+> { role: "read", db: "Beacon" }, // Adjust this for your needs
+> // Add additional read roles as needed
+> ]
+> });
+> ```
> This will add a user (user name: `query_user` and password:`querypassword`) account with read-only permission to the Beacon database. This is important to avoid unwanted modifications to the Beacon database.
> To know more about MongoDB, please read the [MongoDB documentation](https://www.mongodb.com/docs/).
> 10. Run the command `$docker-compose` in the directory containing the `docker-compose.yaml` file with the specified parameters.
@@ -456,21 +456,21 @@ We are looking to see if there is a deletion mutation in the gene **located** in
> - *"END"*: `243620819`
> - *"VARIANT STATE ID"*: `EFO:0030068`
> The srarch function will queiry the Beacon database and print out the resutls that matches our quiery specifications. In this case it will print something like this.
-> > ```json
-> > {'_id': ObjectId('6690160a3a936e8e0a7828e2'),
-> > 'assemblyId': 'GRCh38',
-> > 'biosampleId': 'HG00096',
-> > 'definitions': {'Location': {'chromosome': '1',
-> > 'end': 243620819,
-> > 'start': 243618689}},
-> > 'id': 'refvar-6690160a3a936e8e0a7828e2',
-> > 'info': {'cnCount': 1,
-> > 'cnValue': 0.422353,
-> > 'legacyId': 'DRAGEN:LOSS:chr1:243618690-243620819'},
-> > 'updated': '2024-07-11T17:26:27.265115',
-> > 'variantInternalId': 'chr1:243618689-243620819:EFO:0030068',
-> > 'variantState': {'id': 'EFO:0030068', 'label': 'low-level loss'}}
-> > ```
+> ```json
+> {'_id': ObjectId('6690160a3a936e8e0a7828e2'),
+> 'assemblyId': 'GRCh38',
+> 'biosampleId': 'HG00096',
+> 'definitions': {'Location': {'chromosome': '1',
+> 'end': 243620819,
+> 'start': 243618689}},
+> 'id': 'refvar-6690160a3a936e8e0a7828e2',
+> 'info': {'cnCount': 1,
+> 'cnValue': 0.422353,
+> 'legacyId': 'DRAGEN:LOSS:chr1:243618690-243620819'},
+> 'updated': '2024-07-11T17:26:27.265115',
+> 'variantInternalId': 'chr1:243618689-243620819:EFO:0030068',
+> 'variantState': {'id': 'EFO:0030068', 'label': 'low-level loss'}}
+> ```
>
> When sharing a Beacon protocol, it is important to provide users with read-only access to query the Beacon database. Creating read-only users for Beacon-providing institutions helps prevent unwanted data overwrites that can occur by mistake.
>
diff --git a/topics/variant-analysis/tutorials/sars-cov-2-variant-discovery/tutorial.md b/topics/variant-analysis/tutorials/sars-cov-2-variant-discovery/tutorial.md
index 62e8aa9d7bc89d..c95849077f4a38 100644
--- a/topics/variant-analysis/tutorials/sars-cov-2-variant-discovery/tutorial.md
+++ b/topics/variant-analysis/tutorials/sars-cov-2-variant-discovery/tutorial.md
@@ -4,7 +4,6 @@ layout: tutorial_hands_on
title: Mutation calling, viral genome reconstruction and lineage/clade assignment from SARS-CoV-2 sequencing data
subtopic: one-health
level: Intermediate
-zenodo_link: "https://zenodo.org/record/5036687"
questions:
- How can a complete analysis, including viral consensus sequence reconstruction and lineage assignment be performed?
- How can such an analysis be kept manageable for lots of samples, yet flexible enough to handle different types of input data?
@@ -242,7 +241,7 @@ For the suggested batch of early Omicron data we suggest downloading it via URLs
>
> - Option 2: Import from a shared data library
>
-> {% snippet faqs/galaxy/datasets_import_from_data_library.md astype="as a Collection" collection_type="List of Pairs" collection_name="Sequencing data" tohistory="the history you created for this tutorial" path="GTN - Material / Variant analysis / Mutation calling, viral genome reconstruction and lineage/clade assignment from SARS-CoV-2 sequencing data / DOI: 10.5281/zenodo.5036686" box_type="none" %}
+> {% snippet faqs/galaxy/datasets_import_from_data_library.md astype="as a Collection" collection_type="List of Pairs" collection_name="Sequencing data" tohistory="the history you created for this tutorial" path="GTN - Material / Variant analysis / Mutation calling, viral genome reconstruction and lineage/clade assignment from SARS-CoV-2 sequencing data / Omicron_batch_analysis" box_type="none" %}
>
{: .hands_on}
@@ -266,7 +265,7 @@ Besides the sequenced reads data, we need at least two additional datasets for c
> 1. {% tool [Upload](upload1) %} the reference to your history via the link above and make sure the dataset format is set to `fasta`.
>
> {% snippet faqs/galaxy/datasets_import_via_link.md format="fasta" %}
-> 2. {% tool [Replace Text in entire line](toolshed.g2.bx.psu.edu/repos/bgruening/text_processing/tp_replace_in_line/1.1.2) in entire line %} to simplify the reference sequence name
+> 2. {% tool [Replace Text in entire line](toolshed.g2.bx.psu.edu/repos/bgruening/text_processing/tp_replace_in_line/9.3+galaxy1) in entire line %} to simplify the reference sequence name
> - {% icon param-file %} *"File to process"*: the uploaded reference sequence from the ENA
> - In {% icon param-repeat %} *"1. Replacement"*:
> - *"Find pattern"*: `^>.+`
@@ -866,7 +865,7 @@ Pangolin (Phylogenetic Assignment of Named Global Outbreak LINeages) can be used
>
From consensus sequences to clade assignments using Pangolin
>
-> 1. {% tool [Pangolin](toolshed.g2.bx.psu.edu/repos/iuc/pangolin/pangolin/4.2+galaxy0) %} with the following parameters:
+> 1. {% tool [Pangolin](toolshed.g2.bx.psu.edu/repos/iuc/pangolin/pangolin/4.3+galaxy2) %} with the following parameters:
> - {% icon param-file %} *"Input FASTA File(s)"*: `Multisample consensus FASTA`
> - *"Include header line in output file"*: `Yes`
>
