Snakemake workflow: J2Seq

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This is the template for a new Snakemake workflow. Replace this text with a comprehensive description covering the purpose and domain. Insert your code into the respective folders, i.e. scripts , rules , and envs . Define the entry point of the workflow in the Snakefile and the main configuration in the config.yaml file.

Authors

  • Chao Di (@dic)

Usage

If you use this workflow in a paper, don't forget to give credits to the authors by citing the URL of this (original) repository and, if available, its DOI (see above).

Step 1: Obtain a copy of this workflow

  1. Create a new github repository using this workflow as a template .

  2. Clone the newly created repository to your local system, into the place where you want to perform the data analysis.

Step 2: Configure workflow

Configure the workflow according to your needs via editing the files in the config/ folder. Adjust config.yaml to configure the workflow execution, and samples.tsv to specify your sample setup.

Step 3: Install Snakemake

Install Snakemake using conda : 

conda create -c bioconda -c conda-forge -n snakemake snakemake

For installation details, see the instructions in the Snakemake documentation .

Step 4: Execute workflow

Activate the conda environment: 

conda activate snakemake

Test your configuration by performing a dry-run via 

snakemake --use-conda -n

Execute the workflow locally via 

snakemake --use-conda --cores $N

using $N cores or run it in a cluster environment via 

snakemake --use-conda --cluster qsub --jobs 100

or 

snakemake --use-conda --drmaa --jobs 100

If you not only want to fix the software stack but also the underlying OS, use 

snakemake --use-conda --use-singularity

in combination with any of the modes above. See the Snakemake documentation for further details.

Step 5: Investigate results

After successful execution, you can create a self-contained interactive HTML report with all results via: 

snakemake --report report.html

This report can, e.g., be forwarded to your collaborators. An example (using some trivial test data) can be seen here .

Step 6: Commit changes

Whenever you change something, don't forget to commit the changes back to your github copy of the repository: 

git commit -a
git push

Step 7: Obtain updates from upstream

Whenever you want to synchronize your workflow copy with new developments from upstream, do the following.

  1. Once, register the upstream repository in your local copy: git remote add -f upstream git@github.com:snakemake-workflows/J2seq.git or git remote add -f upstream https://github.com/snakemake-workflows/J2seq.git if you do not have setup ssh keys.

  2. Update the upstream version: git fetch upstream .

  3. Create a diff with the current version: git diff HEAD upstream/master workflow > upstream-changes.diff .

  4. Investigate the changes: vim upstream-changes.diff .

  5. Apply the modified diff via: git apply upstream-changes.diff .

  6. Carefully check whether you need to update the config files: git diff HEAD upstream/master config . If so, do it manually, and only where necessary, since you would otherwise likely overwrite your settings and samples.

Step 8: Contribute back

In case you have also changed or added steps, please consider contributing them back to the original repository:

  1. Fork the original repo to a personal or lab account.

  2. Clone the fork to your local system, to a different place than where you ran your analysis.

  3. Copy the modified files from your analysis to the clone of your fork, e.g., cp -r workflow path/to/fork . Make sure to not accidentally copy config file contents or sample sheets. Instead, manually update the example config files if necessary.

  4. Commit and push your changes to your fork.

  5. Create a pull request against the original repository.

Testing

Test cases are in the subfolder .test . They are automatically executed via continuous integration with Github Actions .

Code Snippets

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shell:
    '''
        samtools view -h {input} Ad5 -b > {output} 2> {log}
    '''
SnakeMake SAMtools From line 12 of rules/extract_ad5.smk 
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shell:
    '''
    samtools index {input} {output.bai}
    '''
SnakeMake SAMtools From line 26 of rules/extract_ad5.smk 
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shell:
    '''
    multiqc ../results/fastq_screen_output -o {params.outdir} &> {log}
    '''
SnakeMake MultiQC From line 28 of rules/fastq_screen.smk 
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script:
    "../scripts/featureCount.R"
SnakeMake From line 16 of rules/featureCount.smk 
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script:
    "../scripts/featureCount.R"
SnakeMake From line 34 of rules/featureCount.smk 
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script:
    "../scripts/featureCount.R"
SnakeMake From line 52 of rules/featureCount.smk 
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script:
    "../scripts/featureCount_segments.R"
SnakeMake From line 68 of rules/featureCount.smk 
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shell:
    "samtools index {input} {output}"
SnakeMake SAMtools From line 34 of rules/merge_unmapped_bam.smk 
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shell:
    "samtools index {input} {output}"
SnakeMake SAMtools From line 31 of rules/star_map.smk 
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shell:
    '''
    rm -r ../results/multiqc/star/
    multiqc STAR_align -o {params.outdir} &> {log}
    '''
SnakeMake MultiQC From line 42 of rules/star_map.smk 
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shell:
    '''
    TEcount --sortByPos --format BAM --mode multi -b {input} \\
    --GTF {params.gene} --TE {params.TE} \\
    --stranded forward --project "../results/TEcount/TEcount.{wildcards.sample}"
    '''
SnakeMake tecount From line 17 of rules/TEtranscripts.smk 
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shell:
    '''
    rm -f {output};
    cut -f1  ../results/TEcount/TEcount.Ad5input1.cntTable | sed 's/"//g;s/gene\/TE/gene_TE/g' > {output};
    for i in {input}; do
        cut -f2 $i | sed 's/merged_bam\///g;s/_merged_dedup_sorted.bam//g' > foo;
        paste {output} foo > foo1;
        mv foo1 {output};
    done
    rm -f foo foo1
    '''
SnakeMake From line 32 of rules/TEtranscripts.smk 
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library(Rsubread)
library(dplyr)
library(mgsub)

log <- file(snakemake@log[[1]], open="wt")
sink(log)
sink(log, type="message")

## Count RPFs (normalized in RPKM) on CDS for each gene, using `featureCounts`
## run all bams together
samples <- read.table(snakemake@input[["samples"]], header=T)
bamfiles <- paste0("./merged_bam/", as.vector(samples$sample),"_merged_dedup_sorted.bam")

## run one bam file
# bamfiles <- snakemake@input[["bamfile"]]
RPFcounts <- featureCounts(files=bamfiles, annot.ext=snakemake@input[['gtf']],
    isGTFAnnotationFile=TRUE, GTF.featureType=snakemake@params[["featureType"]], GTF.attrType="gene_name",
    strandSpecific=snakemake@params[["strand"]], countMultiMappingReads=FALSE, juncCounts=TRUE, nthreads=snakemake@threads[[1]])

write.table(RPFcounts$counts, file=snakemake@output[[1]], sep="\t", quote=F, row.names = TRUE, col.names = NA)
R dplyr FeatureCounts Rsubread mgsub From line 2 of scripts/featureCount.R 
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library(Rsubread)
library(dplyr)
library(mgsub)

log <- file(snakemake@log[[1]], open="wt")
sink(log)
sink(log, type="message")

## Count RPFs (normalized in RPKM) on CDS for each gene, using `featureCounts`
## run all bams together
samples <- read.table(snakemake@input[["samples"]], header=T)
bamfiles <- paste0("./merged_bam/", as.vector(samples$sample),"_merged_dedup_sorted.bam")

## run one bam file
# bamfiles <- snakemake@input[["bamfile"]]
RPFcounts <- featureCounts(files=bamfiles, annot.ext=snakemake@input[['saf']],
    isGTFAnnotationFile=FALSE, fracOverlap=1,
    strandSpecific=snakemake@params[["strand"]], countMultiMappingReads=FALSE, juncCounts=TRUE, 
    nthreads=snakemake@threads[[1]])

write.table(RPFcounts$counts, file=snakemake@output[[1]], sep="\t", quote=F, row.names = TRUE, col.names = NA)
R dplyr FeatureCounts Rsubread mgsub From line 2 of scripts/featureCount_segments.R 
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__author__ = "Julian de Ruiter"
__copyright__ = "Copyright 2017, Julian de Ruiter"
__email__ = "julianderuiter@gmail.com"
__license__ = "MIT"


from os import path

from snakemake.shell import shell


input_dirs = set(path.dirname(fp) for fp in snakemake.input)
output_dir = path.dirname(snakemake.output[0])
output_name = path.basename(snakemake.output[0])
log = snakemake.log_fmt_shell(stdout=True, stderr=True)

shell(
    "multiqc"
    " {snakemake.params}"
    " --force"
    " -o {output_dir}"
    " -n {output_name}"
    " {input_dirs}"
    " {log}")
Python Snakemake MultiQC From line 3 of multiqc/wrapper.py
ShowHide 10 more snippets with no or duplicated tags.

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Created: 1yr ago
Updated: 1yr ago
Maitainers: public
URL: https://github.com/chaodi51/J2seq
Name: j2seq
Version: 1
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Copyright: Public Domain
License: MIT License
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