Snakemake pipeline for Epicure analyses

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bigr_epicure_pipeline

Snakemake pipeline for Epicure analyses: Chip-Seq, Atac-Seq, Cut&Tag, Cut&Run, MeDIP-Seq, 8-OxoG-Seq

Summary

Usage

Installation (following Snakemake-Workflows guidelines)

note: This action has already been done for you if you work at Gustave Roussy. See at the end of this section

Install snakemake and snakedeploy with mamba package manager. Given that Mamba is installed, run:

mamba create -c conda-forge -c bioconda --name bigr_epicure_pipeline snakemake snakedeploy pandas

Ensure your conda environment is activated in your bash terminal:

conda shell.bash activate bigr_epicure_pipeline

Alternatively, if you work at Gustave Roussy, you can use our shared environment:

conda shell.bash activate /mnt/beegfs/pipelines/unofficial-snakemake-wrappers/shared_conda/bigr_epicure_pipeline

Deployment (following Snakemake-Workflows guidelines)

note: This action has been made easier for you if you work at Gustave Roussy. See at the end of this section

Given that Snakemake and Snakedeploy are installed and available (see Installation ), the workflow can be deployed as follows.

Go to your working directory:

cd path/to/my/project

Deploy workflow:

snakedeploy deploy-workflow https://github.com/tdayris/bigr_epicure_pipeline . --tag v0.4.0

Snakedeploy will create two folders workflow and config . The former contains the deployment of the chosen workflow as a Snakemake module , the latter contains configuration files which will be modified in the next step in order to configure the workflow to your needs. Later, when executing the workflow, Snakemake will automatically find the main Snakefile in the workflow subfolder.

Consider to put the exact version of the pipeline and all modifications you might want perform under version control. e.g. by managing it via a (private) Github repository

Configure workflow (following Snakemake-Workflows guidelines)

See dedicated config/README.md file for dedicated explanations of all options and consequences.

Run workflow (following Snakemake-Workflows guidelines)

note: This action has been made easier for you if you work at Gustave Roussy. See at the end of this section

Given that the workflow has been properly deployed and configured, it can be executed as follows.

Fow running the workflow while deploying any necessary software via conda (using the Mamba package manager by default), run Snakemake with

snakemake --cores all --use-conda

Alternatively, for users at Gustave Roussy, you may use:

bash workflow/scripts/misc/bigr_launcher.sh

Snakemake will automatically detect the main Snakefile in the workflow subfolder and execute the workflow module that has been defined by the deployment in step 2.

For further options, e.g. for cluster and cloud execution, see the docs .

Generate report (following Snakemake-Workflows guidelines)

After finalizing your data analysis, you can automatically generate an interactive visual HTML report for inspection of results together with parameters and code inside of the browser via

snakemake --report report.zip

The resulting report.zip file can be passed on to collaborators, provided as a supplementary file in publications, or uploaded to a service like Zenodo in order to obtain a citable DOI .

Open-on-demand Flamingo

This section describes the pipeline usage through Open-on-demand web-interface at Flamingo, Gustave Roussy.

Open-on-demand job composer

Log-in to Flamingo Open-On-Demand web-page using your institutional user name and password. On the main page, click on "Job", then, in the drop-down menu, hit "Job Composer"

hit-job-composer

Create a new template

On the top of the new page, in the grey bar menu, hit "Template". Then use the light blue button "+ New Template".

new-template

An empty template for appears:

empty-form

Fill the "Path" section with the following path: /mnt/beegfs/pipelines/bigr_epicure_pipeline/open-on-demand-templates . It is very important that you do not change this path. Fill the rest of the template as you like.

filled-form

Hit the white "save" button at the bottom left of the form.

Edit the template with your current work

Back to the "Template" page we saw earlier, a new line should be displayed with our new template. Click on the line and the right section of the page should display dedicated informations about this template.

select-pipeline

Name it as you like, but do not create a job now! Scroll down and hit the white button: "Open Dir"

open-dir

Edit `bigr_launcher.sh`

Click on the file bigr_launcher.sh and hit the white button: "Edit".

bigr_launcher

Follow comments in grey and edit the line 11 to 30 as you'd like. You can refer to this page to know the latest version of this pipeline. You should always be using the latest version of this pipeline when starting a new project.

lines-edit

Launch a new job

Back to the Job Composer window, you can now click "+ New Job", "From Template" and select the template we just made.

launch

Pipeline description

note: The following steps may not be perform in that exact order.

Pre-pocessing

Step	Tool	Documentation	Reason
Download genome sequence	curl	Snakemake-Wrapper: download-sequence	Ensure genome sequence are consistent in Epicure analyses
Download genome annotation	curl	Snakemake-Wrapper: download-annotation	Ensure genome annotation are consistent in Epicure analyses
Download blacklised regions	wget		Ensure blacklist regions are consistent in Epicure analyses
Trimming + QC	Fastp	Snakemake-Wrapper: fastp	Perform read quality check and corrections, UMI, adapter removal, QC before and after trimming
Quality Control	FastqScreen	Snakemake-Wrapper: fastq-screen	Perform library quality check
Download fastq screen indexes	wget		Ensure fastq_screen reports are the same among Epicure analyses

Read mapping

Step	Tool	Documentation	Reason
Indexation	Bowtie2	Snakemake-Wrapper: bowtie2-build	Index genome for up-coming read mapping
Mapping	Bowtie2	Snakemake-Wrapper: bowtie2-align	Align sequenced reads on the genome
Filtering	Sambamba	Snakemake-Wrapper: sambamba-sort	Sort alignment over chromosome position, this reduce up-coming required computing resources, and reduce alignment-file size.
Filtering	Sambamba	Snakemake-Wrapper: sambamba-view	Remove non-canonical chromosomes and mapping belonging to mitochondrial chromosome. Remove low quality mapping.
Filtering	Sambamba	Snakemake-Wrapper: sambamba-markdup	Remove sequencing duplicates.
Filtering	DeepTools	Snakemake-Wrapper: sambamba-sort	For Atac-seq only. Reads on the positive strand should be shifted 4 bp to the right and reads on the negative strand should be shifted 5 bp to the left as in Buenrostro et al. 2013 .
Archive	Sambamba	Snakemake-Wrapper: sambamba-view	Compress alignment fil in CRAM format in order to reduce archive size.
Quality Control	Picard	Snakemake-Wrapper: picard-collect-multiple-metrics	Summarize alignments, GC bias, insert size metrics, and quality score distribution.
Quality Control	Samtools	Snakemake-Wrapper: samtools-stats	Summarize alignment metrics. Performed before and after mapping-post-processing in order to highlight possible bias.
Quality Control	DeepTools	Snakemake-Wrapper: deeptools-fingerprint	Control imuno precipitation signal specificity.
GC-bias correction	DeepTools	Official Documentation	Filter regions with abnormal GC-Bias as decribed in Benjamini & Speed, 2012 . OxiDIP-Seq only.

Coverage

Step	Tool	Documentation	Reason
Coverage	DeepTools	Snakemake-Wrapper: deeptools-bamcoverage	Compute genome coverage, normalized to 1M reads
Coverage	MEDIPS	Official documentation	Compute genome coverage with CpG density correction using MEDIPS (MeDIP-Seq only)
Scaled-Coverage	DeepTools	Snakemake-Wrapper: deeptools-computematrix	Calculate scores per genomic regions. Used for heatmaps and profile coverage plots.
Depth	DeepTools	Snakemake-Wrapper: deeptools-plotheatmap	Assess the sequencing depth of given samples
Coverage	CSAW	Official documentation	Count and filter reads over sliding windows.
Filter	CSAW	Official documentation	Filter reads over background signal.
Quality Control	DeepTools	Snakemake-Wrapper: deeptools-plotprofile	Plot profile scores over genomic regions
Quality Control	DeepTools	Official Documentation	Plot principal component analysis (PCA) over bigwig coverage to assess sample dissimilarity.
Quality Control	DeepTools	Official Documentation	Plot sample correlation based on the coverage analysis.
Coverage	BedTools	Snakemake-Wrapper: bedtools-genomecov	Estimate raw coverage over the genome

Peak-Calling

Step	Tool	Documentation	Reason
Peak-Calling	Mac2	Snakemake-Wrapper: macs2-callpeak	Search for significant peaks
Heatmap	DeepTools	Snakemake-Wrapper: deeptools-plotheatmap	Plot heatmap and peak coverage among samples
FRiP score	Manually assessed		Compute Fragment of Reads in Peaks to assess signal/noise ratio.
Peak-Calling	SEACR	Official Documentation	Search for significant peaks in Cut&Tag or Cut&Run

Differential Peak Calling

Step	Tool	Documentation	Reason
Peak-Calling	MEDIPS	Official documentation	Search for significant variation in peak coverage with EdgeR (MeDIP-Seq only)
Normalization	CSAW	Official documentation	Correct composition bias and variable library sizes.
Differential Binding	CSAW-EdgeR	Official documentation	Call for differentially covered windows

Peak annotation

Step	Tool	Documentation	Reason
Annotation	MEDIPS	Official Documentation	Annotate peaks with Ensembl-mart through MEDIPS (MeDIP-Seq only)
Annotation	Homer	Snakemake-Wrapper: homer-annotatepeaks	Performing peak annotation to associate peaks with nearby genes.
Annotation	CHiP seeker	Official Documentation	Performing peak annotation to associate peaks with nearby genes.
Quality Control	CHiP Seeker	Official Documentation	Perform region-wise barplot graph.
Quality Control	CHiP Seeker	Official Documentation	Perform region-wise upset-plot.
Quality Control	CHiP Seeker	Official Documentation	Visualize distribution of TF-binding loci relative to TSS

Motifs

Step	Tool	Documentation	Reason
De-novo	Homer	Official Documentation	Perform de novo motifs discovery.

Material and Methods

ChIP-Seq

Chip-Seq rulegraph

Reads are trimmed using fastp version 0.23.2 , using default parameters. Trimmed reads are mapped over the genome of interest defined in the configuration file, using bowtie2 version 2.5.1 , using --very-sensitive parameter to increase mapping specificity.

Mapped reads with a quality lower than 30 are dropped out using Sambamba version 1.0 with parameter --filter 'mapping_quality >= 30' . In case of pair-ended library, orphan reads a dropped out using --filter 'not (unmapped or mate_is_unmapped)' in Sambamba version 1.0 . Remaining reads are filtered over canonical chromosomes, using Samtools version 1.17 . Mitochondrial chromosome is not considered as a canonical chromosome, that being so, mitochondrial reads are dropped out. In case regions of interest are defined over the genome, BedTools version 2.31.0 with -wa -sorted as optional parameter to keep mapped reads that overlap the regions of interest by, at least, one base. Duplicated reads were filtered out, using Sambamba version 1.0 using the optional parameter --remove-duplicates --overflow-list-size 600000 .

A quality control is made both before and after these filters to ensure no valuable data is lost. These quality controls are made using Picard version 3.0.0 , and Samtools version 1.17 .

Genome coverage was assessed with DeepTools version 3.5.2 . Chromosome X and Y were ignored in the RPKM normalization in order to reduce noise the final result. using the following optional parameters --normalizeUsing RPKM --binSize 5 --skipNonCoveredRegions --ignoreForNormalization chrX chrM chrY . The same tool was used to plot both quality controls and peak coverage heatmaps.

The peak annotation was done using ChIPSeeker version 1.34.0 , using Org.eg.db annotations version 3.16.0 .

De novo motif discovery was made with Homer version 4.11 over called peaks, using the following optional parameters -size 200 -homer2 over the genome identified in the configuration file (GRCh38 or mm10). The motifs were used for further annotation of peaks with Homer.

Differential binding analysis was performed using CSAW version 1.32.0 . Input signal (if available) was used to find signal of interest over background noise. Or else, a large binning was defined over mappable genome regions to define a background noise track to use as an input signal. In any case, a log2 of 1.1 between the background and the sample of interest was used as a threshold value to identify regions of interest. Data were normalized over efficiency biases as described in CSAW documentation , since libraries of the same size can still have composition bias. Differential binded sites were called using EdgeR over the statistical formulas described in the configuration file. The prior N was defined over 20 degrees of freedom in the GLM test. FDR values from EdgeR were corrected using a region merge accross gene annotations to account for related binding sites. Differentially bounded regions were annotated as if they were peaks, using the same methods, and the same parameters.

Additional quality controls were made using FastqScreen version 0.15.3 . Complete quality reports were built using MultiQC aggregation tool and in-house scripts.

The whole pipeline was powered by Snakemake version 7.26.0 and the Snakemake-Wrappers version v2.0.0 .

MNase-seq

rulegraph-mnase

A quality control is made both before and after these filters to ensure no valuable data is lost. These quality controls are made using Picard version 3.0.0 , and Samtools version 1.17 .

Genome coverage was assessed with DeepTools version 3.5.2 . Chromosome X and Y were ignored in the RPKM normalization in order to reduce noise the final result. MNase specificity was also taken into account using the following optional parameters --normalizeUsing RPKM --binSize 5 --skipNonCoveredRegions --ignoreForNormalization chrX chrM chrY --MNase . The same tool was used to plot both quality controls and peak coverage heatmaps.

The peak annotation was done using ChIPSeeker version 1.34.0 , using Org.eg.db annotations version 3.16.0 .

Additional quality controls were made using FastqScreen version 0.15.3 . Complete quality reports were built using MultiQC aggregation tool and in-house scripts.

The whole pipeline was powered by Snakemake version 7.26.0 and the Snakemake-Wrappers version v2.0.0 .

Gro-seq

rulegraph-groseq

A quality control is made both before and after these filters to ensure no valuable data is lost. These quality controls are made using Picard version 3.0.0 , and Samtools version 1.17 .

Genome coverage was assessed with DeepTools version 3.5.2 . Chromosome X and Y were ignored in the RPKM normalization in order to reduce noise the final result. Gro-seq specificity was also taken into account using the following optional parameters --normalizeUsing RPKM --binSize 5 --skipNonCoveredRegions --ignoreForNormalization chrX chrM chrY --Offset 12 . The same tool was used to plot both quality controls and peak coverage heatmaps.

The peak annotation was done using ChIPSeeker version 1.34.0 , using Org.eg.db annotations version 3.16.0 .

Additional quality controls were made using FastqScreen version 0.15.3 . Complete quality reports were built using MultiQC aggregation tool and in-house scripts.

The whole pipeline was powered by Snakemake version 7.26.0 and the Snakemake-Wrappers version v2.0.0 .

Ribo-seq

rulegraph-riboseq

A quality control is made both before and after these filters to ensure no valuable data is lost. These quality controls are made using Picard version 3.0.0 , and Samtools version 1.17 .

Genome coverage was assessed with DeepTools version 3.5.2 . Chromosome X and Y were ignored in the RPKM normalization in order to reduce noise the final result. Ribo-seq specificity was also taken into account using the following optional parameters --normalizeUsing RPKM --binSize 5 --skipNonCoveredRegions --ignoreForNormalization chrX chrM chrY --Offset 15 . The same tool was used to plot both quality controls and peak coverage heatmaps.

The peak annotation was done using ChIPSeeker version 1.34.0 , using Org.eg.db annotations version 3.16.0 .

Additional quality controls were made using FastqScreen version 0.15.3 . Complete quality reports were built using MultiQC aggregation tool and in-house scripts.

The whole pipeline was powered by Snakemake version 7.26.0 and the Snakemake-Wrappers version v2.0.0 .

Atac-seq

Atac-Seq rulegraph

A quality control is made both before and after these filters to ensure no valuable data is lost. These quality controls are made using Picard version 3.0.0 , and Samtools version 1.17 .

The peak annotation was done using ChIPSeeker version 1.34.0 , using Org.eg.db annotations version 3.16.0 .

Differential binding analysis was performed using CSAW version 1.32.0 . A large binning was defined over mappable genome regions to define a background noise track to use as an input signal. In any case, a log2 of 1.1 between the background and the sample of interest was used as a threshold value to identify regions of interest. Data were normalized over efficiency biases as described in CSAW documentation , since libraries of the same size can still have composition bias. Differential binded sites were called using EdgeR over the statistical formulas described in the configuration file. The prior N was defined over 20 degrees of freedom in the GLM test. FDR values from EdgeR were corrected using a region merge accross gene annotations to account for related binding sites. Differentially bounded regions were annotated as if they were peaks, using the same methods, and the same parameters.

Additional quality controls were made using FastqScreen version 0.15.3 . Complete quality reports were built using MultiQC aggregation tool and in-house scripts.

The whole pipeline was powered by Snakemake version 7.26.0 and the Snakemake-Wrappers version v2.0.0 .

Cut&Tag

Cut&Tag rulegraph

Mapped reads with a quality lower than 30 are dropped out using Sambamba version 1.0 with parameter --filter 'mapping_quality >= 30' . In case of pair-ended library, orphan reads a dropped out using --filter 'not (unmapped or mate_is_unmapped)' in Sambamba version 1.0 . Remaining reads are filtered over canonical chromosomes, using Samtools version 1.17 . Mitochondrial chromosome is not considered as a canonical chromosome, that being so, mitochondrial reads are dropped out. In case regions of interest are defined over the genome, BedTools version 2.31.0 with -wa -sorted as optional parameter to keep mapped reads that overlap the regions of interest by, at least, one base. Duplicated reads were not filtered out, but marked as duplicates, using Sambamba version 1.0 using the optional parameter --overflow-list-size 600000 .

A quality control is made both before and after these filters to ensure no valuable data is lost. These quality controls are made using Picard version 3.0.0 , and Samtools version 1.17 .

Genome coverage was assessed with DeepTools version 3.5.2 . Chromosome X and Y were ignored in the RPKM normalization in order to reduce noise the final result. using the following optional parameters --normalizeUsing RPKM --binSize 5 --skipNonCoveredRegions --ignoreForNormalization chrX chrM chrY --ignoreDuplicates . Reads marked as duplicates were treated as normal reads. The same tool was used to plot both quality controls and peak coverage heatmaps.

Single-sample peak calling was performed by Macs2 version 2.2.7.1 using both broad and narrow options. Pair-ended libraries recieved the --format BAMPE parameters, while single-ended libraries used estimated fragment size provided in input. Alongside with Macs2, SEACR version 1.3 was used to perform single-sample peak-calling using parameters hinted in the original publication of SEACR.

The peak annotation was done using ChIPSeeker version 1.34.0 , using Org.eg.db annotations version 3.16.0 .

Differential binding analysis was performed using CSAW version 1.32.0 . Input signal (if available) was used to find signal of interest over background noise. Or else, a large binning was defined over mappable genome regions to define a background noise track to use as an input signal. In any case, a log2 of 1.1 between the background and the sample of interest was used as a threshold value to identify regions of interest. Data were normalized over efficiency biases as described in CSAW documentation , since libraries of the same size can still have composition bias. Differential binded sites were called using EdgeR over the statistical formulas described in the configuration file. The prior N was defined over 20 degrees of freedom in the GLM test. FDR values from EdgeR were corrected using a region merge accross gene annotations to account for related binding sites. Through the whole process, reads flagged as duplicates are treated as any other read. Differentially bounded regions were annotated as if they were peaks, using the same methods, and the same parameters.

Additional quality controls were made using FastqScreen version 0.15.3 . Complete quality reports were built using MultiQC aggregation tool and in-house scripts.

The whole pipeline was powered by Snakemake version 7.26.0 and the Snakemake-Wrappers version v2.0.0 .

Cut&Run

Cut&Run rulegraph

Mapped reads with a quality lower than 30 are dropped out using Sambamba version 1.0 with parameter --filter 'mapping_quality >= 30' . In case of pair-ended library, orphan reads a dropped out using --filter 'not (unmapped or mate_is_unmapped)' in Sambamba version 1.0 . Remaining reads are filtered over canonical chromosomes, using Samtools version 1.17 . Mitochondrial chromosome is not considered as a canonical chromosome, that being so, mitochondrial reads are dropped out. In case regions of interest are defined over the genome, BedTools version 2.31.0 with -wa -sorted as optional parameter to keep mapped reads that overlap the regions of interest by, at least, one base. Duplicated reads were not filtered out, but marked as duplicates, using Sambamba version 1.0 using the optional parameter --overflow-list-size 600000 .

A quality control is made both before and after these filters to ensure no valuable data is lost. These quality controls are made using Picard version 3.0.0 , and Samtools version 1.17 .

Genome coverage was assessed with DeepTools version 3.5.2 . Chromosome X and Y were ignored in the RPKM normalization in order to reduce noise the final result. using the following optional parameters --normalizeUsing RPKM --binSize 5 --skipNonCoveredRegions --ignoreForNormalization chrX chrM chrY --ignoreDuplicates . Reads marked as duplicates were treated as normal reads. The same tool was used to plot both quality controls and peak coverage heatmaps.

Single-sample peak calling was performed by Macs2 version 2.2.7.1 using both broad and narrow options. Pair-ended libraries recieved the --format BAMPE parameters, while single-ended libraries used estimated fragment size provided in input.Alongside with Macs2, SEACR version 1.3 was used to perform single-sample peak-calling using parameters hinted in the original publication of SEACR.

The peak annotation was done using ChIPSeeker version 1.34.0 , using Org.eg.db annotations version 3.16.0 .

Differential binding analysis was performed using CSAW version 1.32.0 . Input signal (if available) was used to find signal of interest over background noise. Or else, a large binning was defined over mappable genome regions to define a background noise track to use as an input signal. In any case, a log2 of 1.1 between the background and the sample of interest was used as a threshold value to identify regions of interest. Data were normalized over efficiency biases as described in CSAW documentation , since libraries of the same size can still have composition bias. Differential binded sites were called using EdgeR over the statistical formulas described in the configuration file. The prior N was defined over 20 degrees of freedom in the GLM test. FDR values from EdgeR were corrected using a region merge accross gene annotations to account for related binding sites. Through the whole process, reads flagged as duplicates are treated as any other read. Differentially bounded regions were annotated as if they were peaks, using the same methods, and the same parameters.

Additional quality controls were made using FastqScreen version 0.15.3 . Complete quality reports were built using MultiQC aggregation tool and in-house scripts.

The whole pipeline was powered by Snakemake version 7.26.0 and the Snakemake-Wrappers version v2.0.0 .

MeDIP-Seq

MeDIP-Seq rulegraph

A quality control is made both before and after these filters to ensure no valuable data is lost. These quality controls are made using Picard version 3.0.0 , and Samtools version 1.17 .

The peak annotation was done using ChIPSeeker version 1.34.0 , using Org.eg.db annotations version 3.16.0 .

Differential binding analysis was performed with MEDIPS version 1.50.0 . If available, input signals were used to search signal of interest over the background noise. An adjusted p-value threshold of 0.1 was chosen to find significant signal over noise. A distance of 1 base was used to merge neighboring significant windows. Differentially binded sites were called using EdgeR and FDR were corrected using MEDIPS internal methods as defined in the official documentation. Differentially bounded regions were annotated as if they were peaks, using the same methods, and the same parameters.

Additional quality controls were made using FastqScreen version 0.15.3 . Complete quality reports were built using MultiQC aggregation tool and in-house scripts.

The whole pipeline was powered by Snakemake version 7.26.0 and the Snakemake-Wrappers version v2.0.0 .

OxiDIP-Seq (OG-Seq)

OxiDIP-Seq rulegraph

Reads are trimmed using fastp version 0.23.2 , using the following non-default parameter: --poly_g_min_len 25 since we expect a higher number of base modifications. Trimmed reads are mapped over the genome of interest defined in the configuration file, using bowtie2 version 2.5.1 , using default parameters.

A quality control is made both before and after these filters to ensure no valuable data is lost. These quality controls are made using Picard version 3.0.0 , and Samtools version 1.17 .

The peak annotation was done using ChIPSeeker version 1.34.0 , using Org.eg.db annotations version 3.16.0 .

Differential binding analysis was performed using CSAW version 1.32.0 . Input signal (if available) was used to find signal of interest over background noise. Or else, a large binning was defined over mappable genome regions to define a background noise track to use as an input signal. In any case, a log2 of 1.1 between the background and the sample of interest was used as a threshold value to identify regions of interest. Data were normalized over efficiency biases as described in CSAW documentation , since libraries of the same size can still have composition bias, and 8oxoG changes the base composition. Differential binded sites were called using EdgeR over the statistical formulas described in the configuration file. The prior N was defined over 20 degrees of freedom in the GLM test. FDR values from EdgeR were corrected using a region merge accross gene annotations to account for related binding sites. Through the whole process, reads flagged as duplicates are treated as any other read. Differentially bounded regions were annotated as if they were peaks, using the same methods, and the same parameters.

Additional quality controls were made using FastqScreen version 0.15.3 . Complete quality reports were built using MultiQC aggregation tool and in-house scripts.

The whole pipeline was powered by Snakemake version 7.26.0 and the Snakemake-Wrappers version v2.0.0 .

Citations

Fastp: Chen, Shifu, et al. "fastp: an ultra-fast all-in-one FASTQ preprocessor." Bioinformatics 34.17 (2018): i884-i890.
Bowtie2: Langmead, Ben, and Steven L. Salzberg. "Fast gapped-read alignment with Bowtie 2." Nature methods 9.4 (2012): 357-359.
Sambamba: Tarasov, Artem, et al. "Sambamba: fast processing of NGS alignment formats." Bioinformatics 31.12 (2015): 2032-2034.
Ensembl: Martin, Fergal J., et al. "Ensembl 2023." Nucleic Acids Research 51.D1 (2023): D933-D941.
Snakemake: Köster, Johannes, and Sven Rahmann. "Snakemake—a scalable bioinformatics workflow engine." Bioinformatics 28.19 (2012): 2520-2522.
Atac-Seq: Buenrostro, Jason D., et al. "Transposition of native chromatin for fast and sensitive epigenomic profiling of open chromatin, DNA-binding proteins and nucleosome position." Nature methods 10.12 (2013): 1213-1218.
MeDIPS: Lienhard, Matthias, et al. "MEDIPS: genome-wide differential coverage analysis of sequencing data derived from DNA enrichment experiments." Bioinformatics 30.2 (2014): 284-286.
DeepTools: Ramírez, Fidel, et al. "deepTools: a flexible platform for exploring deep-sequencing data." Nucleic acids research 42.W1 (2014): W187-W191.
UCSC: Nassar LR, Barber GP, Benet-Pagès A, Casper J, Clawson H, Diekhans M, Fischer C, Gonzalez JN, Hinrichs AS, Lee BT, Lee CM, Muthuraman P, Nguy B, Pereira T, Nejad P, Perez G, Raney BJ, Schmelter D, Speir ML, Wick BD, Zweig AS, Haussler D, Kuhn RM, Haeussler M, Kent WJ. The UCSC Genome Browser database: 2023 update. Nucleic Acids Res. 2022 Nov 24;. PMID: 36420891
ChIPSeeker: Yu, Guangchuang, Li-Gen Wang, and Qing-Yu He. "ChIPseeker: an R/Bioconductor package for ChIP peak annotation, comparison and visualization." Bioinformatics 31.14 (2015): 2382-2383.
CSAW: Lun, Aaron TL, and Gordon K. Smyth. "csaw: a Bioconductor package for differential binding analysis of ChIP-seq data using sliding windows." Nucleic acids research 44.5 (2016): e45-e45.
Macs2: Gaspar, John M. "Improved peak-calling with MACS2." BioRxiv (2018): 496521.
Homer: Heinz S, Benner C, Spann N, Bertolino E et al. Simple Combinations of Lineage-Determining Transcription Factors Prime cis-Regulatory Elements Required for Macrophage and B Cell Identities. Mol Cell 2010 May 28;38(4):576-589. PMID: 20513432
Seacr: Meers, Michael P., Dan Tenenbaum, and Steven Henikoff. "Peak calling by Sparse Enrichment Analysis for CUT&RUN chromatin profiling."Epigenetics & chromatin 12 (2019): 1-11.
BedTools: Quinlan, Aaron R., and Ira M. Hall. "BEDTools: a flexible suite of utilities for comparing genomic features." Bioinformatics 26.6 (2010): 841-842.
Snakemake-Wrappers version v2.0.0
Snakemake-Workflows
BiGR Epicure

Roadmap

Coverage: PBC
Peak-annotation: CentriMo
Differential Peak Calling: DiffBind
IGV: screen-shot, igv-reports

Based on Snakemake-Wrappers version v2.0.0

Code Snippets

__author__ = "Thibault Dayris"
__copyright__ = "Copyright 2023, Thibault Dayris"
__email__ = "[email protected]"
__license__ = "MIT"

from snakemake.shell import shell


log = snakemake.log_fmt_shell(stdout=True, stderr=True)
extra = snakemake.params.get("extra", "")

blacklist = snakemake.input.get("blacklist", "")
if blacklist:
    extra += f" --blackListFileName {blacklist} "

out_file = snakemake.output[0]
if out_file.endswith(".bed"):
    extra += " --BED "

shell(
    "alignmentSieve "
    "{extra} "
    "--numberOfProcessors {snakemake.threads} "
    "--bam {snakemake.input.aln} "
    "--outFile {out_file} "
    "{log} "
)

Python Snakemake DeepTools From line 1 of alignmentsieve/wrapper.py

__author__ = "Jan Forster, Felix Mölder"
__copyright__ = "Copyright 2019, Jan Forster"
__email__ = "[email protected], [email protected]"
__license__ = "MIT"

from snakemake.shell import shell

## Extract arguments
extra = snakemake.params.get("extra", "")

log = snakemake.log_fmt_shell(stdout=True, stderr=True)
if len(snakemake.input) > 1:
    if all(f.endswith(".gz") for f in snakemake.input):
        cat = "zcat"
    elif all(not f.endswith(".gz") for f in snakemake.input):
        cat = "cat"
    else:
        raise ValueError("Input files must be all compressed or uncompressed.")
    shell(
        "({cat} {snakemake.input} | "
        "sort -k1,1 -k2,2n | "
        "bedtools merge {extra} "
        "-i stdin > {snakemake.output}) "
        " {log}"
    )
else:
    shell(
        "( bedtools merge"
        " {extra}"
        " -i {snakemake.input}"
        " > {snakemake.output})"
        " {log}"
    )

Python Snakemake BEDTools From line 1 of merge/wrapper.py

__author__ = "Jan Forster"
__copyright__ = "Copyright 2021, Jan Forster"
__email__ = "[email protected]"
__license__ = "MIT"


import os
from snakemake.shell import shell

log = snakemake.log_fmt_shell(stdout=True, stderr=True)

shell(
    "sambamba index {snakemake.params.extra} -t {snakemake.threads} "
    "{snakemake.input[0]} {snakemake.output[0]} "
    "{log}"
)

Python Snakemake Sambamba From line 1 of index/wrapper.py

__author__ = "Jan Forster"
__copyright__ = "Copyright 2021, Jan Forster"
__email__ = "[email protected]"
__license__ = "MIT"


import os
from snakemake.shell import shell

log = snakemake.log_fmt_shell(stdout=True, stderr=True)

shell(
    "sambamba merge {snakemake.params.extra} -t {snakemake.threads} "
    "{snakemake.output[0]} {snakemake.input} "
    "{log}"
)

Python Snakemake Sambamba From line 1 of merge/wrapper.py

__author__ = "Jan Forster"
__copyright__ = "Copyright 2021, Jan Forster"
__email__ = "[email protected]"
__license__ = "MIT"


import os

from snakemake.shell import shell

in_file = snakemake.input[0]
extra = snakemake.params.get("extra", "")
log = snakemake.log_fmt_shell(stdout=False, stderr=True)

if in_file.endswith(".sam") and ("-S" not in extra or "--sam-input" not in extra):
    extra += " --sam-input"

shell(
    "sambamba view {extra} -t {snakemake.threads} "
    "{snakemake.input[0]} > {snakemake.output[0]} "
    "{log}"
)

Python Snakemake Sambamba From line 1 of view/wrapper.py

script:
    "../../scripts/chipseeker/chipseeker_plot_gene_body.R"

SnakeMake From line 18 of annotation/chipseeker_genebody.smk

script:
    "../../scripts/chipseeker/chipseeker_plot_gene_body.R"

SnakeMake From line 39 of annotation/chipseeker_genebody.smk

script:
    "../../scripts/chipseeker/chipseeker_covplot.R"

SnakeMake From line 17 of annotation/chipseeker_genomecov.smk

script:
    "../../scripts/chipseeker/chipseeker_covplot.R"

SnakeMake From line 37 of annotation/chipseeker_genomecov.smk

script:
    "../../scripts/chipseeker/chipseeker_annotate.R"

SnakeMake From line 18 of annotation/chipseeker_peak_annotate.smk

script:
    "../../scripts/chipseeker/chipseeker_annotate.R"

SnakeMake From line 44 of annotation/chipseeker_peak_annotate.smk

script:
    "../../scripts/chipseeker/chipseeker_plot_annobar.R"

SnakeMake From line 22 of annotation/chipseeker_plot_annobar.smk

script:
    "../../scripts/chipseeker/chipseeker_plot_annobar.R"

SnakeMake From line 42 of annotation/chipseeker_plot_annobar.smk

script:
    "../../scripts/chipseeker/chipseeker_plot_annobar_lsit.R"

SnakeMake From line 66 of annotation/chipseeker_plot_annobar.smk

script:
    "../../scripts/chipseeker/chipseeker_plot_distance_tss.R"

SnakeMake From line 22 of annotation/chipseeker_plot_distance_to_tss.smk

script:
    "../../scripts/chipseeker/chipseeker_plot_distance_tss.R"

SnakeMake From line 42 of annotation/chipseeker_plot_distance_to_tss.smk

script:
    "../../scripts/chipseeker/chipseeker_plot_distance_tss_list.R"

SnakeMake From line 66 of annotation/chipseeker_plot_distance_to_tss.smk

script:
    "../../scripts/chipseeker/chipseeker_plot_upsetvenn.R"

SnakeMake From line 17 of annotation/chipseeker_plot_upsetvenn.smk

script:
    "../../scripts/chipseeker/chipseeker_plot_upsetvenn.R"

SnakeMake From line 37 of annotation/chipseeker_plot_upsetvenn.smk

script:
    "../../scripts/chipseeker/chipseeker_make_tagmatrix.R"

SnakeMake From line 17 of annotation/chipseeker_tagmatrix.smk

script:
    "../../scripts/chipseeker/chipseeker_make_tagmatrix.R"

SnakeMake From line 37 of annotation/chipseeker_tagmatrix.smk

shell:
    "annotatePeaks.pl "
    "{input.peak} "
    "{params.genome} "
    "-cpu {threads} "
    "-wig {input.wig} "
    "{params.extra} "
    "> {output.annotations} "
    "2> {log} "

SnakeMake homer From line 19 of annotation/homer_annotate_peaks.smk

script:
    "../../scripts/csaw/csaw_fregment_length.R"

SnakeMake From line 17 of coverage/csaw_count.smk

script:
    "../../scripts/csaw/csaw_count.R"

SnakeMake From line 37 of coverage/csaw_count.smk

script:
    "../../scripts/csaw/csaw_filter.R"

SnakeMake From line 63 of coverage/csaw_count.smk

shell:
    "multiBigwigSummary bins {params.extra} "
    "--bwfiles {input.bw} "
    "--outFileName {output.bw} "
    "--blackListFileName {input.blacklist} "
    "--numberOfProcessors {threads} "
    "> {log} 2>&1 "

SnakeMake From line 41 of coverage/deeptools_bamcoverage.smk

script:
    "../../scripts/medip-seq/medips_meth.R"

SnakeMake From line 17 of coverage/medips_meth.smk

shell:
    "plotCorrelation --corData {input.bw} "
    "--plotFile {output.png} "
    "--outFileCorMatrix {output.stats} "
    "{params.extra} > {log} 2>&1 "

SnakeMake From line 18 of coverage_qc/deeptools_plot_correlation.smk

shell:
    "plotPCA {params.extra} --corData {input.bw} "
    "--plotFile {output.png} "
    "--outFileNameData {output.stats} "
    "> {log} 2>&1 "

SnakeMake PCAtools From line 18 of coverage_qc/deeptools_plot_pca.smk

script:
    "../../scripts/csaw/csaw_normalize.R"

SnakeMake From line 20 of differential_binding/csaw_db.smk

script:
    "../../scripts/csaw/csaw_edger.R"

SnakeMake From line 46 of differential_binding/csaw_db.smk

script:
    "../../scripts/medip-seq/medips_edger.R"

SnakeMake From line 17 of differential_binding/medips_edger.smk

wrapper:
    "v2.0.0/bio/sambamba/merge"

SnakeMake From line 15 of differential_binding/pull_samples_coverage.smk

shell:
    "cat {params} {input} > {output} 2> {log}"

SnakeMake From line 19 of differential_binding/pull_samples_peaks.smk

shell:
    "sort {params} {input} > {output} 2> {log}"

SnakeMake From line 41 of differential_binding/pull_samples_peaks.smk

wrapper:
    "v2.0.0/bio/bedtools/merge"

SnakeMake From line 59 of differential_binding/pull_samples_peaks.smk

script:
    "../../scripts/factorfootprints/factor_footprints.R"

SnakeMake From line 22 of footprints/plot_footprints.smk

script:
    "../../scripts/factorfootprints/factor_footprints.R"

SnakeMake From line 47 of footprints/plot_footprints.smk

script:
    "../../scripts/csaw/csaw_readparam.R"

SnakeMake From line 18 of indexing/csaw_preprocess.smk

script:
    "../../scripts/misc/rename.py"

SnakeMake From line 15 of indexing/rename.smk

script:
    "../../scripts/misc/rename.py"

SnakeMake From line 33 of indexing/rename.smk

shell:
    "xenome index {params.extra} "
    "--num-threads {threads} "
    "--max-memory {resources.mem_mb} "
    "--prefix {params.prefix} "
    "--graft {input.human} "
    "--host {input.mouse} "
    "--log-file {log.tool} "
    "--tmp-dir {resources.tmpdir} "
    "> {log.general} 2>&1 "

SnakeMake From line 31 of indexing/xenome_index.smk

shell:
    "cut {params.cut} {input} | sort {params.sort} > {output} 2> {log}"

SnakeMake From line 19 of mapping/bedtools_filter_roi.smk

shell:
    "bedtools intersect "
    "{params.extra} "
    "-g {input.genome} "
    "-abam {input.left} "
    "-b {input.right} "
    "> {output} 2> {log}"

SnakeMake BEDTools From line 39 of mapping/bedtools_filter_roi.smk

shell:
    "computeGCBias "
    "--bamfile {input.bam} "
    "--genome {input.genome} "
    "--GCbiasFrequenciesFile {output.freq} "
    "--effectiveGenomeSize {params.genome_size} "
    "--blackListFileName {input.blacklist} "
    "--numberOfProcessors {threads} "
    "--biasPlot {output.plot} "
    "{params.extra} "
    "> {log} 2>&1 "

SnakeMake From line 22 of mapping/deeptools_gc_bias.smk

shell:
    "correctGCBias "
    "--bamfile {input.bam} "
    "--genome {input.genome} "
    "--GCbiasFrequenciesFile {input.freq} "
    "--correctedFile {output} "
    "--effectiveGenomeSize {params.genome_size} "
    "--numberOfProcessors {threads} "
    "{params.extra} "
    "> {log} 2>&1 "

SnakeMake From line 55 of mapping/deeptools_gc_bias.smk

wrapper:
    "master/bio/deeptools/alignmentsieve"

SnakeMake From line 17 of mapping/deeptools_shift.smk

wrapper:
    "master/bio/deeptools/alignmentsieve"

SnakeMake From line 73 of mapping/deeptools_shift.smk

wrapper:
    "v2.0.0/bio/sambamba/view"

SnakeMake From line 17 of mapping/ensembl_to_ucsc.smk

shell:
    "sed {params} {input} > {output} 2> {log}"

SnakeMake From line 39 of mapping/ensembl_to_ucsc.smk

wrapper:
    "v2.0.0/bio/sambamba/view"

SnakeMake From line 59 of mapping/ensembl_to_ucsc.smk

shell:
    "awk --file {params.awk} {input} > {output} 2> {log}"

SnakeMake From line 81 of mapping/ensembl_to_ucsc.smk

shell:
    "sed {params} {input} > {output} 2> {log}"

SnakeMake From line 103 of mapping/ensembl_to_ucsc.smk

shell:
    "cat {params.extra} {input.header} {input.reads} > {output} 2> {log}"

SnakeMake From line 126 of mapping/ensembl_to_ucsc.smk

wrapper:
    "v2.0.0/bio/sambamba/view"

SnakeMake From line 146 of mapping/ensembl_to_ucsc.smk

wrapper:
    "v2.0.0/bio/sambamba/index"

SnakeMake From line 182 of mapping/ensembl_to_ucsc.smk

wrapper:
    "v2.0.0/bio/sambamba/view"

SnakeMake From line 202 of mapping/ensembl_to_ucsc.smk

shell:
    "sed {params} {input} > {output} 2> {log}"

SnakeMake From line 224 of mapping/ensembl_to_ucsc.smk

wrapper:
    "v2.0.0/bio/sambamba/view"

SnakeMake From line 244 of mapping/ensembl_to_ucsc.smk

shell:
    "sed {params} {input} > {output} 2> {log}"

SnakeMake From line 266 of mapping/ensembl_to_ucsc.smk

shell:
    "awk --file {params.awk} {input} > {output} 2> {log}"

SnakeMake From line 288 of mapping/ensembl_to_ucsc.smk

shell:
    "cat {params.extra} {input.header} {input.reads} > {output} 2> {log}"

SnakeMake From line 311 of mapping/ensembl_to_ucsc.smk

wrapper:
    "v2.0.0/bio/sambamba/view"

SnakeMake From line 331 of mapping/ensembl_to_ucsc.smk

wrapper:
    "v2.0.0/bio/sambamba/index"

SnakeMake From line 367 of mapping/ensembl_to_ucsc.smk

wrapper:
    "v2.0.0/bio/sambamba/merge"

SnakeMake From line 15 of mapping/pull_bams.smk

wrapper:
    "v2.0.0/bio/sambamba/index"

SnakeMake From line 33 of mapping/pull_bams.smk

shell:
    "bamPEFragmentSize "
    "{params.extra} "
    "--bamfiles {input.bam} "
    "--histogram {output.hist} "
    "--numberOfProcessors {threads} "
    "--blackListFileName {input.blacklist} "
    "> {output.metrics} 2> {log} "

SnakeMake From line 22 of mapping_qc/deeptools_bampefragment_size.smk

script:
    "../../scripts/rsamtools/create_bam_file.R"

SnakeMake From line 15 of mapping/rsamtools.smk

script:
    "../../scripts/rsamtools/create_design.R"

SnakeMake From line 35 of mapping/rsamtools.smk

shell:
    "xenome classify {params.extra} "
    "--log-file {log.tool} "
    "--num-threads {threads} "
    "--tmp-dir {resources.tmpdir} "
    "--max-memory {resources.mem_mb} "
    "--output-filename-prefix {params.out_prefix} "
    "--prefix {params.idx_prefix} "
    "--fastq-in {input.r1} --fastq-in {input.r2} "
    "> {log.general} 2>&1 "

SnakeMake From line 27 of mapping/xenome_pdx.smk

shell:
    "xenome classify {params.extra} "
    "--log-file {log.tool} "
    "--num-threads {threads} "
    "--tmp-dir {resources.tmpdir} "
    "--max-memory {resources.mem_mb} "
    "--output-filename-prefix {params.out_prefix} "
    "--prefix {params.idx_prefix} "
    "--fastq-in {input.reads} "
    "> {log.general} 2>&1 "

SnakeMake From line 64 of mapping/xenome_pdx.smk

script:
    "../../scripts/medips_load.R"

SnakeMake From line 17 of medips/bam_import.smk

script:
    "../../scripts/medips_coupling.R"

SnakeMake From line 37 of medips/bam_import.smk

shell:
    "awk --file {params.script} {input} > {output} 2> {log}"

SnakeMake From line 17 of motif/homer_find_motifs_genome.smk

shell:
    "findMotifsGenome.pl "
    "{input.peak} "
    "{params.genome} "
    "{params.output_directory} "
    "-size {params.size} "
    "{params.extra} "
    "-p {threads} "
    "> {log} 2>&1 "

SnakeMake From line 40 of motif/homer_find_motifs_genome.smk

shell:
    "rsync {params} {input} {output} > {log} 2>&1"

SnakeMake From line 37 of peak-calling/macs2_callpeak.smk

shell:
    "rsync {params} {input} {output} > {log} 2>&1"

SnakeMake From line 77 of peak-calling/macs2_callpeak.smk

shell:
    "awk --file {params.script} {input} > {output} 2> {log}"

SnakeMake From line 98 of peak-calling/macs2_callpeak.smk

shell:
    "NB=$(sambamba view {params} {input}) && "
    'echo -e "{wildcards.sample}\t${{NB}}" > {output} 2> {log}'

SnakeMake From line 17 of peak-calling/manual_frip_score.smk

shell:
    "NB=$(sambamba view {params} {input}) && "
    'echo -e "{wildcards.sample}\t${{NB}}" > {output} 2> {log}'

SnakeMake From line 38 of peak-calling/manual_frip_score.smk

shell:
    "paste - - <(cat {input.called}) <(cat {input.total}) > {output} 2> {log}"

SnakeMake From line 64 of peak-calling/manual_frip_score.smk

shell:
    "awk --file {params.script} {input} > {output} 2> {log}"

SnakeMake From line 84 of peak-calling/manual_frip_score.smk

shell:
    "SEACR_1.3.sh "
    "{input.exp_bg} "
    "{params.extra} "
    "> {log} 2>&1"

SnakeMake From line 35 of peak-calling/seacr_callpeak.smk

shell:
    "awk --file {params.script} {input} > {output} 2> {log}"

SnakeMake From line 58 of peak-calling/seacr_callpeak.smk

shell:
    "wget {params.extra} {params.address} -O {output} > {log} 2>&1"

SnakeMake From line 18 of reference/download_blacklist.smk

shell:
    "wget {params.extra} {params.address} -O {output} > {log} 2>&1"

SnakeMake From line 39 of reference/download_blacklist.smk

shell:
    "wget {params.extra} {params.address} -O {output} > {log} 2>&1"

SnakeMake From line 60 of reference/download_blacklist.smk

shell:
    "wget {params.extra} {params.address} -O {output} > {log} 2>&1"

SnakeMake From line 81 of reference/download_blacklist.smk

shell:
    "wget {params} --directory-prefix {output} "
    "{params.url} > {log} 2>&1"

SnakeMake From line 21 of reference/download_fastq_screen_index.smk

shell:
    "wget {params} --directory-prefix {output} "
    "{params.url} > {log} 2>&1"

SnakeMake From line 46 of reference/download_fastq_screen_index.smk

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


# Load libraries
base::library(package = "ChIPseeker", character.only = TRUE)

base::library(package = "org.Hs.eg.db", character.only = TRUE)
base::library(package = "org.Mm.eg.db", character.only = TRUE)

base::library(package = "EnsDb.Hsapiens.v86", character.only = TRUE)
base::library(package = "EnsDb.Mmusculus.v79", character.only = TRUE)
base::message("Libraries loaded")

organism <- "hg38"
if ("organism" %in% base::names(x = snakemake@params)) {
    organism <- base::as.character(x = snakemake@params[["organism"]])
}

organism <- base::tolower(x = organism)
if (organism == "hg38") {
    anno_db <- "org.Hs.eg.db"
    edb <- EnsDb.Hsapiens.v86
} else if (organism == "mm10") {
    anno_db <- "org.Mm.eg.db"
    edb <- EnsDb.Mmusculus.v79
} else {
    base::stop("Unknown organism annotation")
}
seqlevelsStyle(edb) <- "Ensembl"

ranges <- NULL
if ("ranges" %in% base::names(x = snakemake@input)) {
    ranges <- base::readRDS(file = base::as.character(x = snakemake@input[["ranges"]]))
} else {
    ranges <- ChIPseeker::readPeakFile(
        peakfile = base::as.character(x = snakemake@input[["bed"]]),
        as = "GRanges"
    )
}
base::message("Parameters and data loaded")


# Building command line
extra <- "peak = ranges, annoDb = anno_db, TxDb = edb"
if ("extra" %in% base::names(x = snakemake@params)) {
    extra <- base::paste(
        extra,
        base::as.character(x = snakemake@params[["extra"]]),
        sep = ", "
    )
}

command <- base::paste0(
    "ChIPseeker::annotatePeak(",
    extra,
    ")"
)
base::message("Command line used:")
base::message(command)

# Annotating
annotation <- base::eval(base::parse(text = command))
base::message("Data annotated")


# Saving results
base::saveRDS(
    object = annotation,
    file = base::as.character(x = snakemake@output[["rds"]])
)
base::message("RDS saved")

utils::write.table(
    base::as.data.frame(annotation),
    sep = "\t",
    col.names = TRUE,
    row.names = TRUE,
    file = base::as.character(x = snakemake@output["tsv"])
)
base::message("Provess over")


# Proper syntax to close the connection for the log file
# but could be optional for Snakemake wrapper
base::sink(type = "message")
base::sink()

R From line 10 of chipseeker/chipseeker_annotate.R

log_file <- file(snakemake@log[[1]], open = "wt")
sink(log_file)
sink(log_file, type = "message")
base::message("Logging defined")

# Load libraries
base::library(package = "ChIPseeker", character.only = TRUE)
base::message("Libraries loaded")

peak <- NULL
if ("bed" %in% base::names(x = snakemake@input)) {
  peak <- ChIPseeker::readPeakFile(
    peakfile = base::as.character(x = snakemake@input[["bed"]]),
    as = "GRanges",
    header = FALSE
  )
} else {
  peak <- base::readRDS(
    file = base::as.character(x = snakemake@input[["ranges"]])
  )
}
base::message("Ranges loaded")

# Build plot
grDevices::png(
  filename = snakemake@output[["png"]],
  width = 1024,
  height = 768,
  units = "px",
  type = "cairo"
)

ChIPseeker::covplot(peak, weightCol = "V5")


dev.off()
base::message("Plot saved")

# Proper syntax to close the connection for the log file
# but could be optional for Snakemake wrapper
base::sink(type = "message")
base::sink()

R From line 10 of chipseeker/chipseeker_covplot.R

log_file <- file(snakemake@log[[1]], open = "wt")
sink(log_file)
sink(log_file, type = "message")
base::message("Logging defined")

# Load libraries
base::library(package = "ChIPseeker", character.only = TRUE)
base::library(
    package = "TxDb.Hsapiens.UCSC.hg38.knownGene",
    character.only = TRUE
)
base::library(
    package = "TxDb.Mmusculus.UCSC.mm10.knownGene",
    character.only = TRUE
)
base::message("Libraries loaded")

# Load peaks
if ("bed" %in% base::names(x = snakemake@input)) {
    peaks <- base::as.character(x = snakemake@input[["bed"]])
} else if ("ranges" %in% base::names(x = snakemake@input)) {
    peaks <- base::readRDS(
        file = base::as.character(x = snakemake@input[["ranges"]])
    )
}
base::message("Peaks loaded")

# Select transcript database
txdb <- TxDb.Hsapiens.UCSC.hg38.knownGene
if ("organism" %in% base::names(x = snakemake@params)) {
    if (snakemake@params[["organism"]] == "mm10") {
        organism <- TxDb.Mmusculus.UCSC.mm10.knownGene
    }
}
seqlevelsStyle(txdb) <- "Ensembl"

# Acquire promoter position
promoter <- ChIPseeker::getPromoters(
    TxDb = txdb,
    upstream = 3000,
    downstream = 3000,
    by = 'gene'
)

# Build tagmatrix
tagMatrix <- ChIPseeker::getTagMatrix(
    peak = peaks,
    windows = promoter
)
base::message("TagMatrix built")

# Save object
base::saveRDS(
    file = base::as.character(x = snakemake@output[["rds"]]),
    object = tagMatrix
)

# Proper syntax to close the connection for the log file
# but could be optional for Snakemake wrapper
base::sink(type = "message")
base::sink()
base::message("Process over")

R From line 10 of chipseeker/chipseeker_make_tagmatrix.R

log_file <- file(snakemake@log[[1]], open = "wt")
sink(log_file)
sink(log_file, type = "message")
base::message("Logging defined")

# Load libraries
base::library(package = "ChIPseeker", character.only = TRUE)
base::message("Libraries loaded")

ranges <- base::readRDS(
    file = base::as.character(x = snakemake@input[["ranges"]])
)
base::message("Ranges loaded")

# Build plot
grDevices::png(
  filename = snakemake@output[["png"]],
  width = 1024,
  height = 384,
  units = "px",
  type = "cairo"
)

ChIPseeker::plotAnnoBar(ranges)


dev.off()
base::message("Plot saved")

# Proper syntax to close the connection for the log file
# but could be optional for Snakemake wrapper
base::sink(type = "message")
base::sink()

R From line 10 of chipseeker/chipseeker_plot_annobar.R

log_file <- file(snakemake@log[[1]], open = "wt")
sink(log_file)
sink(log_file, type = "message")
base::message("Logging defined")

# Load libraries
base::library(package = "ChIPseeker", character.only = TRUE)
base::message("Libraries loaded")

ranges_list <- base::lapply(
    snakemake@input[["ranges"]],
    function(range_path) {
        base::readRDS(
            file = base::as.character(x = range_path)
        )
    }
)
base::message("Ranges loaded")

# Build plot
grDevices::png(
  filename = snakemake@output[["png"]],
  width = 1024,
  height = 384,
  units = "px",
  type = "cairo"
)

ChIPseeker::plotDistToTSS(ranges_list)


dev.off()
base::message("Plot saved")

# Proper syntax to close the connection for the log file
# but could be optional for Snakemake wrapper
base::sink(type = "message")
base::sink()

R From line 11 of chipseeker/chipseeker_plot_distance_tss_list.R

log_file <- file(snakemake@log[[1]], open = "wt")
sink(log_file)
sink(log_file, type = "message")
base::message("Logging defined")

# Load libraries
base::library(package = "ChIPseeker", character.only = TRUE)
base::message("Libraries loaded")

ranges <- base::readRDS(
    file = base::as.character(x = snakemake@input[["ranges"]])
)
base::message("Ranges loaded")

# Build plot
grDevices::png(
  filename = snakemake@output[["png"]],
  width = 1024,
  height = 384,
  units = "px",
  type = "cairo"
)

ChIPseeker::plotDistToTSS(ranges)


dev.off()
base::message("Plot saved")

# Proper syntax to close the connection for the log file
# but could be optional for Snakemake wrapper
base::sink(type = "message")
base::sink()

R From line 10 of chipseeker/chipseeker_plot_distance_tss.R

log_file <- file(snakemake@log[[1]], open = "wt")
sink(log_file)
sink(log_file, type = "message")
base::message("Logging defined")

# Load libraries
base::library(package = "ChIPseeker", character.only = TRUE)
base::library(
    package = "TxDb.Hsapiens.UCSC.hg38.knownGene",
    character.only = TRUE
)
base::library(
    package = "TxDb.Mmusculus.UCSC.mm10.knownGene",
    character.only = TRUE
)

ncpus <- base::as.numeric(snakemake@threads[[1]])
mc.cores <- ncpus
base::message("Libraries loaded")

ranges <- NULL
if ("bed" %in% base::names(x = snakemake@input)) {
    bed_path <- base::as.character(x = snakemake@input[["bed"]])
    ranges <- ChIPseeker::readPeakFile(
        peakfile = bed_path, as = "GRanges", header = FALSE
    )
} else if ("ranges" %in% base::names(x = snakemake@input)) {
    ranges <- base::readRDS(
        file = base::as.character(x = snakemake@input[["ranges"]])
    )
}
base::message("Peaks/Ranges loaded")
tag_matrix <- base::readRDS(
    file = base::as.character(x = snakemake@input[["tagmatrix"]])
)
upstream <- base::attr(x = tag_matrix, which = "upsteam")
downstream <- base::attr(x = tag_matrix, which = "downstream")
base::message("Tagmatrix loaded")

# The default resample value
resample <- 500
window_number <- base::length(tag_matrix)

# Search all prime factors for the given number of windows
prime_factors <- function(x) {
  factors <- c()
  last_prime <- 2
  while(x >= last_prime){
    if (! x %% last_prime) {
      factors <- c(factors, last_prime)
      x <- x / last_prime
      last_prime <- last_prime - 1
      }
    last_prime <- last_prime + 1
  }
  base::return(factors)
}

primes <- base::as.data.frame(x = base::table(prime_factors(x = window_number)))
primes$Var1 <- base::as.integer(base::as.character(primes$Var1))
primes <- primes$Var1 ^ primes$Freq



# Closest prime to default resample value
primes_min <- base::abs(primes - resample)
resample <- primes[base::which(primes_min == base::min(primes_min))]
base::message("Resampling value: ", resample)

txdb <- TxDb.Hsapiens.UCSC.hg38.knownGene
if ("organism" %in% base::names(x = snakemake@params)) {
    if (snakemake@params[["organism"]] == "mm10") {
        organism <- TxDb.Mmusculus.UCSC.mm10.knownGene
    }
}
base::message("Organism library available")

# Build plot
grDevices::png(
  filename = snakemake@output[["png"]],
  width = 1024,
  height = 768,
  units = "px",
  type = "cairo"
)

ChIPseeker::plotPeakProf(
    peak = ranges,
    tagMatrix = tag_matrix,
    upstream = upstream, # rel(0.2),
    downstream = downstream, # rel(0.2),
    weightCol = "V5",
    ignore_strand = TRUE,
    conf = 0.95,
    by = "gene",
    type = "body",
    TxDb = txdb,
    facet = "row",
    nbin = 800,
    verbose = TRUE,
    resample = resample
)

dev.off()
base::message("Plot saved")

# Proper syntax to close the connection for the log file
# but could be optional for Snakemake wrapper
base::sink(type = "message")
base::sink()

R From line 10 of chipseeker/chipseeker_plot_gene_body.R

log_file <- file(snakemake@log[[1]], open = "wt")
sink(log_file)
sink(log_file, type = "message")
base::message("Logging defined")

# Load libraries
base::library(package = "ChIPseeker", character.only = TRUE)
base::message("Libraries loaded")

ranges <- base::readRDS(
    file = base::as.character(x = snakemake@input[["ranges"]])
)
base::message("Ranges loaded")

# Build plot
grDevices::png(
  filename = snakemake@output[["png"]],
  width = 1024,
  height = 768,
  units = "px",
  type = "cairo"
)

ChIPseeker::upsetplot(ranges, vennpie = TRUE)


dev.off()
base::message("Plot saved")

# Proper syntax to close the connection for the log file
# but could be optional for Snakemake wrapper
base::sink(type = "message")
base::sink()

R From line 10 of chipseeker/chipseeker_plot_upsetvenn.R

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

# Load libraries
base::library(package = "BiocParallel", character.only = TRUE)
base::library(package = "csaw", character.only = TRUE)
base::message("Libraries loaded")

if (snakemake@threads > 1) {
    BiocParallel::register(
        BiocParallel::MulticoreParam(snakemake@threads)
    )
    options("mc.cores" = snakemake@threads)
    base::message("Process multi-threaded")
}

# Loading list of input files
design <- readRDS(
    file = base::as.character(x = snakemake@input[["design"]])
)

frag_length <- readRDS(
    file = base::as.character(x = snakemake@input[["fragment_length"]])
)

# Loading filter parameters
read_params <- base::readRDS(
    file = base::as.character(x = snakemake@input[["read_params"]])
)
base::message("Input data loaded")

extra <- "bam.files = design$BamPath, param = read_params, ext=frag_length"
if ("extra" %in% base::names(x = snakemake@params)) {
    extra <- base::paste(
        extra,
        base::as.character(x = snakemake@params[["extra"]]),
        sep = ", "
    )
}


command <- base::paste(
    "csaw::windowCounts(",
    extra,
    ")"
)
base::message("Count command line:")
base::print(command)

# Counting reads over sliding window
counts <- base::eval(base::parse(text = command))
base::message(
    "Number of extended reads ",
    "overlapping a sliding window ",
    "at spaced positions across the ",
    "genome, acquired."
)

# Saving results
base::saveRDS(
    object = counts,
    file = snakemake@output[["rds"]]
)
base::print(counts$totals)
base::message("RDS saved")

if ("ranges" %in% base::names(snakemake@output)) {
    if (base::endsWith(x = snakemake@output[["ranges"]], suffix = ".RDS")) {
        base::saveRDS(
            object = rowRanges(x = counts),
            file = snakemake@output[["ranges"]]
        )
    } else {
        utils::write.table(
            x = base::as.data.frame(rowRanges(x = counts)),
            file = snakemake@output[["ranges"]],
            sep = "\t",
            col.names = TRUE,
            row.names = TRUE
        )
    }
}

# base::message("Total number of ranges counted: ")
# base::print(counts$totals)
base::message("Process over.")


# Proper syntax to close the connection for the log file
# but could be optional for Snakemake wrapper
base::sink(type = "message")
base::sink()

R BiocParallel From line 10 of csaw/csaw_count.R

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


# Load libraries
base::library(package = "edgeR", character.only = TRUE)
base::library(package = "csaw", character.only = TRUE)
base::message("Libraries loaded")

counts <- base::readRDS(
    file = base::as.character(x = snakemake@input[["counts"]])
)
y <- csaw::asDGEList(object = counts)
base::message("Normalized-filtered counts loaded")

exp_design <- utils::read.table(
    file = base::as.character(x = snakemake@input[["design"]]),
    sep = "\t",
    header = TRUE,
    stringsAsFactors = FALSE
)
design <- stats::model.matrix(
    object = stats::as.formula(snakemake@params[["formula"]]),
    data = exp_design
)
base::message("Model matrix built")

prior.n <- edgeR::getPriorN(
    y = y,
    design = design,
    prior.df = 20
)
base::message("Prior N estimated with 20 dof")

y <- edgeR::estimateDisp(
    y = y,
    design = design,
    prior.n = prior.n,

)
base::message("Dispersion estimated")

grDevices::png(
    filename = base::as.character(x = snakemake@output[["disp_png"]]),
    width = 1024,
    height = 768
)

edgeR::plotBCV(y = y)

grDevices::dev.off()
base::message("Dispersion plot saved")

fit <- edgeR::glmQLFit(
    y = y,
    design = design,
    robust = TRUE
)
base::message("GLM model fitted")

results <- edgeR::glmQLFTest(
    glmfit = fit,
    coef = utils::tail(x = base::colnames(x = design), 1)
)
base::message("Window counts tested")

grDevices::png(
    filename = base::as.character(x = snakemake@output[["ql_png"]]),
    width = 1024,
    height = 768
)

edgeR::plotQLDisp(glmfit = results)

grDevices::dev.off()
base::message("Quasi-likelihood dispersion plot saved")

# rowData(counts) <- base::cbind(rowData(counts), results$table)
merged <-  csaw::mergeResults(
    counts,
    results$table,
    tol = 100,
    merge.args = list(max.width = 5000)
)
base::message("Results stored in RangedSummarizedExperiment object")


# Save results
if ("qc" %in% base::names(snakemake@output)) {
    is_significative <- summary(merged$combined$FDR <= 0.05)
    direction <- summary(table(merged$combined$direction[is_significative]))
    sig <- base::data.frame(
        Differentially_Expressed = is_significative[["TRUE"]],
        Not_Significative = is_significative[["FALSE"]],
        Up_Regulated = direction[["up"]],
        Down_Regulated = direction[["down"]]
    )
    utils::write.table(
        x = sig,
        file = base::as.character(x = snakemake@output[["qc"]]),
        sep = "\t"
    )
    base::message("QC table saved")
}

if ("csaw" %in% base::names(snakemake@output)) {
    base::saveRDS(
        object = counts,
        file = base::as.character(x = snakemake@output[["csaw"]])
    )
    base::message("csaw results saved")
}

if ("edger" %in% base::names(snakemake@ouptput)) {
    base::saveRDS(
        object = results,
        file = base::as.character(x = snakemake@output[["edger"]])
    )
    base::message("edgeR results saved")
}
base::message("Process over")

# Proper syntax to close the connection for the log file
# but could be optional for Snakemake wrapper
base::sink(type = "message")
base::sink()

R From line 11 of csaw/csaw_edger.R

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


# Load libraries
base::library(package = "csaw", character.only = TRUE)
base::message("Libraries loaded")


counts <- base::readRDS(
    file = base::as.character(x = snakemake@input[["counts"]])
)

log_threshold <- 1.1
if ("log_threshold" %in% base::names(snakemake@params)) {
    log_threshold <- as.numeric(snakemake@params[["log_threshold"]])
}


filter_method <- "average_log_cpm"
if ("filter_method" %in% base::names(x = snakemake@params)) {
    filter_method <- base::as.character(
        x = snakemake@params[["filter_method"]]
    )
}

keep <- base::data.frame(
    "Kept" = 0,
    "Filtered" = 0,
    "PercentFiltered" = 0,
    "PercentKept" = 0
)
base::message("Input data loaded")

# Filtering counts
if (filter_method == "average_log_cpm") {
    # Filter based on average CPM counts.
    # not that good in case of large sequencing libraries
    # not that good in case of large variability in sequencing library sizes

    base::message("Filtering based on count size (aveLogCPM)")

    min_cpm <- 5
    if ("min_cpm" %in% base::names(snakemake@params)) {
        min_cpm <- base::as.numeric(x = snakemake@params[["min_cpm"]])
    }
    abundance <- edgeR::aveLogCPM(SummarizedExperiment::asDGEList(counts))
    keep <- abundances > aveLogCPM(min_cpm, lib.size = mean(counts$totals))
    counts <- counts[keep, ]
} else if (filter_method == "proportion") {
    # Filter based on estimated proportion of signal of interest / noise
    # Not that good if site proportion varies across samples

    base::message("Filtering based on signal / noise proportion")

    proportion <- 0.999
    if ("proportion" %in% base::names(x = snakemake@params)) {
        proportion <- base::as.numeric(x = snakemake@params[["proportion"]])
    }

    keep <- csaw::filterWindowsProportion(counts)$filter > proportion
    counts <- counts[keep, ]
} else if (filter_method == "global_enrichment") {
    # Filtering by global enrichment. The degree of background enrichment
    # is estimated by counting reads into large bins across the genome.
    # Not that good if there are differences in the background coverage
    # across the genome.

    base::message("Filtering based on global enrichment")

    binned <- base::readRDS(
        file = base::as.character(x = snakemake@input[["binned"]])
    )

    filter_stat <- csaw::filterWindowsGlobal(
        data = counts,
        background = binned
    )

    keep <- filter_stat$filter > log2(log_threshold)
    counts <- counts[keep, ]
} else if (filter_method == "local_enrichment") {
    # Mimicking single-sample peak callers.
    # Not good if any peaks are in the neighborhood ! Please make
    # sure there are not peak clusters of any kind.

    base::message("Filtering based on local enrichment")

    neighbor <- base::readRDS(
        file = base::as.character(x = snakemake@input[["neighbor"]])
    )

    filter_stat <- csaw::filterWindowsLocal(
        data = counts,
        background = neighbor
    )

    keep <- filter_stat$filter > log2(log_threshold)
    counts <- counts[keep, ]
} else if (filter_method == "local_maxima") {
    # Use highest average abundance within 1kbp on either side.
    # Very aggressive, use only in datasets with sharp binding.

    base::message("Filtering based on local peak maxima")

    maxed <- base::readRDS(
        file = base::as.character(x = snakemake@input[["maxed"]])
    )

    filter_stat <- csaw::filterWindowsLocal(
        data = counts,
        background = maxed
    )

    keep <- filter_stat$filter > log2(log_threshold)
    counts <- counts[keep, ]
} else if (filter_method == "input_controls") {
    # Use negative controls for ChIP-seq to account for
    # both sequencing and mapping biases in ChIP-seq data

    base::message("Filtering based on negative controls (aka Input)")

    input_counts <- base::readRDS(
        file = base::as.character(x = snakemake@input[["input_counts"]])
    )
    base::message("Input counts loaded")
    counts_binned <- base::readRDS(
        file = base::as.character(x = snakemake@input[["binned"]])
    )
    base::message("Tested (binned) counts loaded")
    input_binned <- base::readRDS(
        file = base::as.character(x = snakemake@input[["input_binned"]])
    )
    base::message("Input (binned) counts loaded")

    scale_info <- csaw::scaleControlFilter(
        data.bin = counts_binned,
        back.bin = input_binned
    )
    base::message("Scale control filter acquired")

    filter_stat <- csaw::filterWindowsControl(
        data = counts,
        background = input_counts,
        prior.count = 2,
        scale.info = scale_info
    )
    base::message("Negative control signal filtered")

    keep <- filter_stat$filter > log2(log_threshold)
    counts <- counts[keep, ]
} else {
    base::stop("Unknown filter method")
}


k <- base::summary(object = keep)
base::message("Number of targets passing filters")
base::print(k)
keep <- base::as.data.frame(keep)
keep$Kept <- k[["TRUE"]]
keep$Filtered <- k[["FALSE"]]
keep$PercentFiltered <- keep$Filtered / (keep$Kept + keep$Filtered)
keep$PercentKept <- keep$Kept / (keep$Kept + keep$Filtered)


# Saving results
utils::write.table(
    x = keep,
    file = base::as.character(x = snakemake@output[["qc"]]),
    sep = "\t"
)
base::message("QC table saved")

if ("png" %in% base::names(x = snakemake@output)) {
    # Saving QC plot
    grDevices::png(
        filename = base::as.character(x = snakemake@output[["png"]]),
        width = 1024,
        height = 768
    )

    graphics::hist(
        filter_stat$filter,
        main = "",
        breaks = 50,
        xlab = "Background abundance (log2-CPM)"
    )
    graphics::abline(v = log2(log_threshold), col = "red")

    grDevices::dev.off()
    base::message("QC plot saved")
}


base::saveRDS(
    object = counts,
    file = base::as.character(x = snakemake@output[["rds"]])
)
base::message("RDS saved, process over")

# Proper syntax to close the connection for the log file
# but could be optional for Snakemake wrapper
base::sink(type = "message")
base::sink()

R From line 10 of csaw/csaw_filter.R

base::library(package = "BiocParallel", character.only = TRUE)
base::library(package = "csaw", character.only = TRUE)
base::message("Library loaded")

# Loading input files
read_params <- base::readRDS(
    file = base::as.character(x = snakemake@input[["read_params"]])
)
read_params <- csaw::reform(x = read_params, dedup = TRUE)
base::message("Read parameters loaded, with dedup set to TRUE")

design <- base::readRDS(file = snakemake@input[["design"]])
base::message("Experimental design loaded with BamFiles")

max_delay <- 800

if (snakemake@threads > 1) {
    BiocParallel::register(
      BPPARAM = BiocParallel::MulticoreParam(workers = snakemake@threads)
    )
    options("mc.cores" = snakemake@threads)
    base::message("Process multi-threaded")
}

# Compute fragment size with cross-correlation
correlation <- csaw::correlateReads(
    bam.files = design$BamPath,
    max.dist = max_delay,
    cross = TRUE,
    param = read_params
)

frag_length <- csaw::maximizeCcf(correlation, ignore = 100)
base::message("Fragment length computed")

base::saveRDS(
    object = frag_length,
    file = base::as.character(x = snakemake@output[["fragment_length"]])
)
base::message("Fragment length saved")

grDevices::png(
    filename = base::as.character(x = snakemake@output[["png"]]),
    width = 1024,
    height = 788
)

graphics::plot(
    x = 0:max_delay,
    y = correlation,
    xlab = "Delay (bp)",
    ylab = "CCF",
    type = "l"
)
graphics::abline(v = frag_length, col = "red")
graphics::text(
    paste(frag_length, "bp"),
    x = frag_length,
    y = min(correlation),
    pos = "4",
    col = "red"
)

grDevices::dev.off()
base::message("Plot saved, process over")

# Proper syntax to close the connection for the log file
# but could be optional for Snakemake wrapper
base::sink(type = "message")
base::sink()

R BiocParallel From line 8 of csaw/csaw_fregment_length.R

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


# Load libraries
base::library(package = "csaw", character.only = TRUE)
base::library(package = "edgeR", character.only = TRUE)
base::message("Libraries loaded")

norm_method <- "composition"
if ("norm_method" %in% base::names(snakemake@params)) {
    norm_method <- base::as.character(x = snakemake@params[["norm_method"]])
}

design <- base::readRDS(
    file = base::as.character(x = snakemake@input[["design"]])
)
base::message("Design loaded")


counts <- base::readRDS(
    file = base::as.character(x = snakemake@input[["counts"]])
)
base::message("Counts loaded")


if (norm_method == "composition") {
    # Highly enriched regions consume more sequencing resources and
    # thereby suppress the representation of other regions. Differences
    # in the magnitude of suppression between libraries can lead to
    # spurious DB calls. Scaling by library size fails to correct for this as
    # composition biases can still occur in libraries of the same size.
    base::message("Normalizing composition bias")

    binned <- base::readRDS(
        file = base::as.character(x = snakemake@input[["bins"]])
    )

    counts <- csaw::normFactors(
        object = binned,
        se.out = counts
    )

    adj_counts <- edgeR::cpm(
        y = csaw::asDGEList(object = binned, log = TRUE)
    )
    normfacs <- counts$norm.factors

} else if (norm_method == "efficiency") {
    # Efficiency biases in ChIP-seq data refer to fold changes in enrichment
    # that are introduced by variability in IP efficiencies between libraries.
    base::message("Normalizing efficiency bias")

    counts <- csaw::normFactors(
        object = counts,
        se.out = TRUE
    )

    adj_counts <- edgeR::cpm(
        y = csaw::asDGEList(object = counts, log = TRUE)
    )
    normfacs <- counts$norm.factors
}

base::saveRDS(
    object = adj_counts,
    file = base::as.character(x = snakemake@output["adj_counts"])
)
base::message("Adjusted counts saved")

base::saveRDS(
    object = counts,
    file = base::as.character(x = snakemake@output[["rds"]])
)
base::message("RDS saved, process over")

# Proper syntax to close the connection for the log file
# but could be optional for Snakemake wrapper
base::sink(type = "message")
base::sink()

R From line 10 of csaw/csaw_normalize.R

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

# Load libraries
base::library(package = "csaw", character.only = TRUE)
base::library(package = "rtracklayer", character.only = TRUE)
base::message("Libraries loaded")

standard_chrom <- c(1:23, "X", "Y")
if ("organism" %in% base::names(x = snakemake@params)) {
    if (base::as.character(x = snakemake@params[["organism"]]) == "mm10") {
        standard_chrom <- c(1:19, "X", "Y")
    }
}

blacklist <- base::as.character(x = snakemake@input[["blacklist"]])
blacklist <- rtracklayer::import(blacklist)

# Build command line
extra <- "minq=20, restrict=standard_chrom, discard=blacklist"
if ("extra" %in% base::names(x = snakemake@params)) {
    extra <- base::as.character(x = snakemake@params[["extra"]])
}

command <- base::paste0(
    "csaw::readParam(",
    extra,
    ")"
)
base::message("Command line:")
base::print(command)


# Run command line
read_params <- base::eval(base::parse(text = command))

# Save results
base::saveRDS(
    object = read_params,
    file = snakemake@output[["rds"]]
)
base::message("RDS saved, process over")

# Proper syntax to close the connection for the log file
# but could be optional for Snakemake wrapper
base::sink(type = "message")
base::sink()

R rtracklayer From line 10 of csaw/csaw_readparam.R

base::message("Loading libraries ... ")
suppressPackageStartupMessages(library("ATACseqQC"))
suppressPackageStartupMessages(library("TxDb.Hsapiens.UCSC.hg38.knownGene"))
suppressPackageStartupMessages(library("BSgenome.Hsapiens.UCSC.hg38"))
suppressPackageStartupMessages(library("phastCons100way.UCSC.hg38"))
suppressPackageStartupMessages(library("MotifDb"))
suppressPackageStartupMessages(library("ChIPpeakAnno"))
suppressPackageStartupMessages(library("Rsamtools"))
base::message("Libraries loaded.")

# base::message("Setting sequence level style...")
# seqlevelsStyle(TxDb.Hsapiens.UCSC.hg38.knownGene) <- "Ensembl"
# seqlevelsStyle(BSgenome.Hsapiens.UCSC.hg38) <- "Ensembl"
# base::message("Database chromosome renamed.")

base::message("Acquiering bam file...")
bamfile <- BamFile(
    file = base::as.character(x = snakemake@input[["bam"]])
)
name <- base::as.character(x = snakemake@params[["name"]])
base::print(bamfile)
base::print(name)
base::message("BamFiles identified")

base::message("Reading bam tags...")
possibleTag <- list(
    "integer" = c(
        "AM", "AS", "CM", "CP", "FI", "H0", "H1",
        "H2", "HI", "IH", "MQ", "NH", "NM", "OP",
        "PQ", "SM", "TC", "UQ"
    ),
    "character" = c(
        "BC", "BQ", "BZ", "CB", "CC", "CO", "CQ", "CR",
        "CS", "CT", "CY", "E2", "FS", "LB", "MC", "MD",
        "MI", "OA", "OC", "OQ", "OX", "PG", "PT", "PU",
        "Q2", "QT", "QX", "R2", "RG", "RX", "SA", "TS", "U2"
    )
)
bamTop100 <- scanBam(
    BamFile(bamfile$path, yieldSize = 100),
    param = ScanBamParam(tag=unlist(possibleTag))
)[[1]]$tag
tags <- names(bamTop100)[lengths(bamTop100) > 0]
base::print(tags)
base::message("Tags Acquired")

base::message("Retrieving sequence level informations...")
seqlev <- as.vector(
    sapply(c(1:22, "X", "Y"), function(chrom) paste0("chr", chrom))
)
seqinformation <- seqinfo(TxDb.Hsapiens.UCSC.hg38.knownGene)
which <- as(seqinformation[seqlev], "GRanges")
base::print(which)
base::message("Sequences retrived")

base::message("Loading bam file...")
bamdata <- readBamFile(
    bamFile = bamfile$path,
    bigFile = TRUE,
    asMates = TRUE,
    tags = tags,
    which = which,
)
base::message("Bam file loaded")

base::message("Shifting bam...")
shiftedBamfile <- base::as.character(x = snakemake@output[["shifted"]])
shiftedBamdir <- base::dirname(shiftedBamfile)
print(shiftedBamdir)
base::dir.create(
    path = shiftedBamdir,
    recursive = TRUE
)
bamdata <- shiftGAlignmentsList(
    gal = bamdata,
    outbam = shiftedBamfile
)
print(bamdata)
base::message("Shift OK")

base::message("Acquiering motif...")
motif_name <- base::as.character(x = snakemake@params[["motif"]])
motif <- query(MotifDb, c(motif_name))
motif <- as.list(motif)
print(motif[[1]], digits = 2)
base::message("Motif retrieved.")

base::message("plot Footprints...")
genome <- Hsapiens
print(genome)

png(
    filename = snakemake@output[["png"]],
    width = 1024,
    height = 768,
    units = "px"
)
sigs <- factorFootprints(
    shiftedBamfile,
    pfm = motif[[1]],
    genome = genome,
    min.score = "90%",
    seqlev = c(1:22, "X", "Y"),
    upstream = 100,
    downstream = 100
)
dev.off()
base::message("Done.")

base::save.image(file = base::as.character(x = snakemake@output[["rda"]]))
base::message("Process over")

R BSgenome.Hsapiens.UCSC.hg38 TxDb.Hsapiens.UCSC.hg38.knownGene Rsamtools ChIPpeakAnno ATACseqQC MotifDb From line 1 of factorfootprints/factor_footprints.R

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


# Load libraries
base::library(package = "MEDIPS", character.only = TRUE)
base::library(package = "BSgenome.Hsapiens.UCSC.hg38", character.only = TRUE)
base::library(package = "BSgenome.Mmusculus.UCSC.mm10", character.only = TRUE)

results <- base::readRDS(
    file = base::as.character(x = snakemake@input[["meth"]])
)
base::message("Dataset loaded")


# Build command line
extra <- "results = results"
if ("extra" %in% base::names(x = snakemake@params)) {
    extra <- base::paste(
        extra,
        base::as.character(x = snakemake@params[["extra"]]),
        sep = ", "
    )
}

command <- base::paste0(
    "MEDIPS.selectSig(",
    extra,
    ")"
)
base::message("Command line:")
base::paste(command)


# Launch analysis
edger_results <- base::eval(base::parse(text = command))
base::message("Differential peak coverage perfomed")

# Optionally merge close peaks
if ("merge_distance" %in% base::names(x = snakemake@params)) {
    edger_results <- MEDIPS.mergeFrames(
        frames = edger_results,
        distance = base::as.numeric(x = snakemake@params[["merge_distance"]])
    )
    base::message("Close peaks merged")
}

# Save results
base::saveRDS(
    object = edger_results,
    file = base::as.character(x = snakemake@output[["rds"]])
)

utils::write.table(
    x = edger_results,
    file = base::as.character(x = snakemake@output[["tv"]]),
    sep = "\t",
    quote = FALSE
)
base::message("Process over")

R From line 10 of medip-seq/medips_edger.R

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


# Load libraries
base::library(package = "MEDIPS", character.only = TRUE)
base::library(package = "BSgenome.Hsapiens.UCSC.hg38", character.only = TRUE)
base::library(package = "BSgenome.Mmusculus.UCSC.mm10", character.only = TRUE)
base::library(package = "DNAcopy", character.only = TRUE)
base::message("Libraries loaded")


mset1 <- base::readRDS(file = base::as.character(x = "mset1"))
extra <- "MSet1 = mset1, diff.method = 'edgeR', p.adj = 'bonferroni'"
    base::message("MSet1 loaded")

mset2 <- NULL
if ("mset2" %in% base::names(snakemake@input)) {
    mset2 <- base::readRDS(file = base::as.character(x = "mset2"))
    extra <- base::paste(extra, "MSet2 = mset2", sep = ", ")
    base::message("MSet2 loaded")
}

cset <- NULL
if ("cset" %in% base::names(snakemake@input)) {
    cset <- base::readRDS(file = base::as.character(x = "cset"))
    extra <- base::paste(extra, "CSet = cset", sep = ", ")
    base::message("CSet loaded")
}

iset1 <- NULL
if ("iset1" %in% base::names(snakemake@input)) {
    iset1 <- iset1base::readRDS(file = base::as.character(x = "iset1"))
    extra <- base::paste(extra, "ISet1 = iset1", sep = ", ")
    base::message("ISet1 loaded")
}

iset2 <- NULL
if ("iset2" %in% base::names(snakemake@input)) {
    iset2 <- iset1base::readRDS(file = base::as.character(x = "iset2"))
    extra <- base::paste(extra, "ISet2 = iset2", sep = ", ")
    base::message("ISet2 loaded")
}

if (! (base::is.null(iset1) && base::is.null(iset2))) {
    extra <- base::paste(extra, "CNV = TRUE", sep = ", ")
    base::message(
        "copy number variation will be tested by ",
        "applying the package DNAcopy to the ",
        "window-wise log-ratios calculated based on ",
        "the the means per group"
    )
}

if (! (base::is.null(mset2) && base::is.null(cset))) {
    extra <- base::paste(extra, "MeDIP = TRUE", sep = ", ")
    base::message(
        "CpG density dependent relative methylation scores ",
        "(rms) will be calculated for the MEDIPS SETs"
    )
}

medip_meth_command <- base::paste0(
    "MEDIPS::MEDIPS.meth(",
    extra,
    ")"
)
base::message("Command line used to determine coverage and analysis methods:")
base::message(medip_meth_command)

coverage_profiles <- base::eval(base::parse(text = medip_meth_command))

base::saveRDS(
    object = coverage_profiles,
    file = base::as.character(x = snakemake@output[["rds"]])
)

# Proper syntax to close the connection for the log file
# but could be optional for Snakemake wrapper
sink(type = "message")
sink()

R From line 10 of medip-seq/medips_meth.R

__author__ = "Thibault Dayris"
__copyright__ = "Copyright 2019, Thibault Dayris"
__email__ = "[email protected]"
__license__ = "MIT"


import os
import os.path as op
import logging

from random import randint
from snakemake.shell import shell
from tempfile import TemporaryDirectory

# Logging behaviour
logging.basicConfig(filename=snakemake.log[0], filemode="w", level=logging.DEBUG)


# Prepare logging
log = snakemake.log_fmt_shell(stdout=False, stderr=True, append=True)
extra_cp = snakemake.params.get("extra", "--force --recursive --verbose")
extra_iget = snakemake.params.get("extra_irods", "-vKrf")
extra_ln = snakemake.params.get("extra_ln", "--relative --verbose --symbolic --force")
extra_rsync = snakemake.params.get(
    "extra_rsync",
    "--verbose --recursive --checksum --human-readable --partial --progress",
)
use_cp_over_rsync = snakemake.params.get("use_cp_over_rsync", False)
master_node = snakemake.params.get("master_node", "flamingo-lg-01")
parallel = snakemake.params.get("internal_parallel", False)
logging.info("Extra acquired")

if ("-N" not in extra_iget) and snakemake.threads > 1:
    max_threads = snakemake.threads
    if os.uname()[1] == master_node and max_threads > 4:
        logging.warning(
            "More than 4 threads per copy on master node is not allowed. "
            "Max threads set to 4. This does not change your original "
            "threading reservation. Run this script on a computing node, "
            "or lower the number of threads."
        )
        max_threads = 4
    if parallel:
        logging.info("Internal parallelization")
        max_threads = 1
    extra_iget += f" -N {max_threads} "

# No node can access a cold storage
# these files must be copied. However,
# any file else where should be symlinked!
cold_storage = ("/mnt/isilon", "/mnt/archivage", "/mnt/install")
if "cold_storage" in snakemake.params.keys():
    cold_storage = snakemake.params["cold_storage"]
logging.info("Mounting point qualified")


def cat_files(dest: str, *src: str, log: str = log) -> None:
    command = f"cat {' '.join(src)} > {dest} {log}"
    logging.info(command)
    shell(command)


def bash_copy(
    src: str,
    dest: str,
    extra_cp: str = extra_cp,
    extra_rsync: str = extra_rsync,
    extra_ln: str = extra_ln,
    log: str = log,
    cold: str = cold_storage,
    use_cp: bool = use_cp_over_rsync,
) -> None:
    if not src.startswith(cold):
        logging.info("Operation on cold storage")
        command = f"ln {extra_ln} {src} {dest} {log}"
        logging.info(command)
        shell(command)
    else:
        logging.info("Operation on active storage")
        command = "cp" if use_cp else "rsync"
        extra = extra_cp if use_cp else extra_rsync
        command = f"{command} {extra} {src} {dest} {log}"
        logging.info(command)
        shell(command)


def iRODS_copy(src: str, dest: str, extra: str = extra_iget, log: str = log) -> None:
    command = f"iget {extra} {src} {dest} {log}"
    logging.info(command)
    shell(command)


def copy(src: str, dest: str) -> None:
    if src.startswith("/odin/kdi/dataset/"):
        logging.info("Using iRODS")
        iRODS_copy(src, dest)
    else:
        logging.info("*Not* using iRODS")
        bash_copy(src, dest)


def copy_then_concat(dest: str, *src: str) -> None:
    logging.info("Performing conatenation")
    with TemporaryDirectory() as tmpdir:
        outfiles = []
        for path in src:
            tmp_dest = f"{tmpdir}/{os.path.basename(path)}.{randint(0, 100_000_000)}"
            copy(path, tmp_dest)
            outfiles.append(tmp_dest)
        cat_files(dest, *outfiles)


def copy_or_concat(src: str, dest: str) -> None:
    if "," in src:
        copy_then_concat(dest, *src.split(","))
    else:
        copy(src, dest)


output_directory = op.realpath(op.dirname(snakemake.output[0]))
logging.debug(output_directory)
sources = snakemake.params.get("input", snakemake.input)
logging.debug(sources)
destinations = snakemake.output
logging.debug(destinations)

if len(destinations) == 1:
    # Then there is only one directory as a destination
    destination = op.realpath(str(destinations))
    command = f"mkdir --parents --verbose {op.dirname(destination)} {log}"
    logging.info(command)
    shell(command)
    if isinstance(sources, str):
        copy_or_concat(sources, destination)
    else:
        for source in sources:
            copy_or_concat(source, destination)
elif len(sources) == len(destinations):
    # Then there must be as many paths as source
    for source, destination in zip(sources, destinations):
        command = f"mkdir --parents --verbose {op.dirname(destination)} {log}"
        logging.info(command)
        shell(command)
        copy_or_concat(source, destination)
else:
    logging.error(f"nb of input: {len(sources)}, nb of output: {len(destinations)}")
    logging.debug(sources)
    logging.debug(destinations)
    raise ValueError("Number of input and output paths do not match.")

Python Snakemake From line 16 of misc/rename.py

base::library(package = "Rsamtools", character.only = TRUE)
base::message("Library loaded")

bam_file <- Rsamtools::BamFile(
    file = base::as.character(x = snakemake@input[["bam"]]),
    index = base::as.character(x = snakemake@input[["bai"]])
)
base::message("Bam file created")

base::saveRDS(
    object = bam_file,
    file = base::as.character(x = snakemake@output["rds"])
)
base::message("Process over")

# Proper syntax to close the connection for the log file
# but could be optional for Snakemake wrapper
base::sink(type = "message")
base::sink()

R From line 8 of rsamtools/create_bam_file.R

base::library(package = "Rsamtools", character.only = TRUE)
base::message("Library loaded")

# Loading input data
rds_paths <- base::sapply(
    snakemake@input[["bam_files"]],
    function(rds_path) base::readRDS(file = base::as.character(x = rds_path))
)
base::message("BamFiles loaded")

design <- utils::read.table(
    file = base::as.character(x = snakemake@input[["design"]]),
    header = TRUE,
    sep = "\t",
    stringsAsFactors = FALSE
)
base::rownames(design) <- design$Sample_id
base::message("Design loaded")

# Building quality control table
design$BamPath <- rds_paths
base::message("Design enhanced")

diagnostics <- base::list()
for (i in base::seq_along(design$BamPath)) {
    bam <- design$BamPath[[i]]
    total_reads <- Rsamtools::countBam(file = bam)$records

    params <- Rsamtools::ScanBamParam(
        flag = Rsamtools::scanBamFlag(isUnmapped = FALSE)
    )
    tota_mapped <- Rsamtools::countBam(file = bam, param = params)

    params <- Rsamtools::ScanBamParam(
        flag = Rsamtools::scanBamFlag(isUnmapped = FALSE, isDuplicate = TRUE)
    )
    marked <- Rsamtools::countBam(bam, param = params)$records

    diagnostics[[i]] <- c(
        Total = total_reads,
        Mapped = tota_mapped,
        Marked = marked
    )
}

base::print(diagnostics)
diag_stats <- base::data.frame(base::do.call(rbind, diagnostics))
base::rownames(diag_stats) <- design$Sample_id


diag_stats$Prop.mapped <- (
    as.numeric(
        x = diag_stats[["Mapped.records"]]
    ) / as.numeric(
        x = diag_stats[["Total"]]
    )
) * 100
diag_stats$Prop.marked <- (
    as.numeric(
        x = diag_stats[["Marked"]]
    ) / as.numeric(
        x = diag_stats[["Mapped.records"]]
    )
) * 100
base::message("Quality controls performed")

# diag_stats <- data.frame(t(sapply(diag_stats, c)))
diag_stats <- data.frame(lapply(diag_stats, as.character), stringsAsFactors=FALSE)
qc_path <- base::as.character(x = snakemake@output[['qc']])
print(diag_stats)
print(qc_path)
print(str(diag_stats))
# Saving results
utils::write.table(
    x = diag_stats,
    file = qc_path,
    sep = "\t",
    row.names = TRUE,
    col.names = TRUE
)
base::message("QC saved")

base::saveRDS(
    object = design,
    file = base::as.character(x = snakemake@output[["design"]])
)
base::message("Design saved")

# Proper syntax to close the connection for the log file
# but could be optional for Snakemake wrapper
base::sink(type = "message")
base::sink()