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Graph	Name	Retrieved From	View
	ChIPmentation_pipeline_packed.cwl#chip_qc.cwl	https://github.com/CompEpigen/ChIPseq_workflows.git Path: CWL/workflows/ChIPmentation_pipeline_packed.cwl Branch/Commit ID: 503df61c4d7fd6078a4089b62382a3c6811d7749 Packed ID: chip_qc.cwl
	Workflow to run pVACseq from detect_variants and rnaseq pipeline outputs	https://github.com/genome/analysis-workflows.git Path: definitions/subworkflows/pvacseq.cwl Branch/Commit ID: 5cb188131f786ed33156e2f0e3dd63ab9c04245d
	Detect Variants workflow	https://github.com/genome/analysis-workflows.git Path: definitions/pipelines/detect_variants.cwl Branch/Commit ID: 3bebaf9b70331de9f4845e2223c55082f5a812fb
	cluster_blastp_wnode and gpx_qdump combined	https://github.com/ncbi/pgap.git Path: task_types/tt_cluster_and_qdump.cwl Branch/Commit ID: 093b60e546237c06cfe7820d6ac8d66467e66725
	ChIPmentation_pipeline_packed.cwl#main	https://github.com/CompEpigen/ChIPseq_workflows.git Path: CWL/workflows/ChIPmentation_pipeline_packed.cwl Branch/Commit ID: 503df61c4d7fd6078a4089b62382a3c6811d7749 Packed ID: main
	rna amplicon analysis for fasta files RNAs - qc, preprocess, annotation, index, abundance	https://github.com/MG-RAST/pipeline.git Path: CWL/Workflows/amplicon-fasta.workflow.cwl Branch/Commit ID: 49e29dfc5b1f7a7630831a1052f9136caa29dbf7
	RNA-Seq pipeline single-read stranded mitochondrial Slightly changed original [BioWardrobe's](https://biowardrobe.com) [PubMed ID:26248465](https://www.ncbi.nlm.nih.gov/pubmed/26248465) RNA-Seq basic analysis for strand specific single-read experiment. An additional steps were added to map data to mitochondrial chromosome only and then merge the output. Experiment files in [FASTQ](http://maq.sourceforge.net/fastq.shtml) format either compressed or not can be used. Current workflow should be used only with single-read strand specific RNA-Seq data. It performs the following steps: 1. `STAR` to align reads from input FASTQ file according to the predefined reference indices; generate unsorted BAM file and alignment statistics file 2. `fastx_quality_stats` to analyze input FASTQ file and generate quality statistics file 3. `samtools sort` to generate coordinate sorted BAM(+BAI) file pair from the unsorted BAM file obtained on the step 1 (after running STAR) 5. Generate BigWig file on the base of sorted BAM file 6. Map input FASTQ file to predefined rRNA reference indices using Bowtie to define the level of rRNA contamination; export resulted statistics to file 7. Calculate isoform expression level for the sorted BAM file and GTF/TAB annotation file using `GEEP` reads-counting utility; export results to file	https://github.com/datirium/workflows.git Path: workflows/rnaseq-se-dutp-mitochondrial.cwl Branch/Commit ID: 00ea05e22788029370898fd4c17798b11edf0e57
	Trim Galore ATAC-Seq pipeline single-read The original [BioWardrobe's](https://biowardrobe.com) [PubMed ID:26248465](https://www.ncbi.nlm.nih.gov/pubmed/26248465) ChIP-Seq basic analysis workflow for a single-read experiment with Trim Galore. The pipeline was adapted for ATAC-Seq single-read data analysis by updating genome coverage step. _Trim Galore_ is a wrapper around [Cutadapt](https://github.com/marcelm/cutadapt) and [FastQC](http://www.bioinformatics.babraham.ac.uk/projects/fastqc/) to consistently apply adapter and quality trimming to FastQ files, with extra functionality for RRBS data. In outputs it returns coordinate sorted BAM file alongside with index BAI file, quality statistics of the input FASTQ file, reads coverage in a form of BigWig file, peaks calling data in a form of narrowPeak or broadPeak files, islands with the assigned nearest genes and region type, data for average tag density plot (on the base of BAM file). Workflow starts with step fastx\_quality\_stats from FASTX-Toolkit to calculate quality statistics for input FASTQ file. At the same time `bowtie` is used to align reads from input FASTQ file to reference genome bowtie\_aligner. The output of this step is unsorted SAM file which is being sorted and indexed by `samtools sort` and `samtools index` samtools\_sort\_index. Based on workflow’s input parameters indexed and sorted BAM file can be processed by `samtools rmdup` samtools\_rmdup to get rid of duplicated reads. If removing duplicates is not required the original input BAM and BAI files return. Otherwise step samtools\_sort\_index\_after\_rmdup repeat `samtools sort` and `samtools index` with BAM and BAI files. Right after that `macs2 callpeak` performs peak calling macs2\_callpeak. On the base of returned outputs the next step macs2\_island\_count calculates the number of islands and estimated fragment size. If the last one is less that 80bp (hardcoded in the workflow) `macs2 callpeak` is rerun again with forced fixed fragment size value (macs2\_callpeak\_forced). If at the very beginning it was set in workflow input parameters to force run peak calling with fixed fragment size, this step is skipped and the original peak calling results are saved. In the next step workflow again calculates the number of islands and estimates fragment size (macs2\_island\_count\_forced) for the data obtained from macs2\_callpeak\_forced step. If the last one was skipped the results from macs2\_island\_count\_forced step are equal to the ones obtained from macs2\_island\_count step. Next step (macs2\_stat) is used to define which of the islands and estimated fragment size should be used in workflow output: either from macs2\_island\_count step or from macs2\_island\_count\_forced step. If input trigger of this step is set to True it means that macs2\_callpeak\_forced step was run and it returned different from macs2\_callpeak step results, so macs2\_stat step should return [fragments\_new, fragments\_old, islands\_new], if trigger is False the step returns [fragments\_old, fragments\_old, islands\_old], where sufix \"old\" defines results obtained from macs2\_island\_count step and sufix \"new\" - from macs2\_island\_count\_forced step. The following two steps (bamtools\_stats and bam\_to\_bigwig) are used to calculate coverage on the base of input BAM file and save it in BigWig format. For that purpose bamtools stats returns the number of mapped reads number which is then used as scaling factor by bedtools genomecov when it performs coverage calculation and saves it in BED format. The last one is then being sorted and converted to BigWig format by bedGraphToBigWig tool from UCSC utilities. To adapt the pipeline for ATAC-Seq data analysis we calculate genome coverage using only the first 9 bp from every read. Step get\_stat is used to return a text file with statistics in a form of [TOTAL, ALIGNED, SUPRESSED, USED] reads count. Step island\_intersect assigns genes and regions to the islands obtained from macs2\_callpeak\_forced. Step average\_tag\_density is used to calculate data for average tag density plot on the base of BAM file.	https://github.com/datirium/workflows.git Path: workflows/trim-atacseq-se.cwl Branch/Commit ID: dda9e6e06a656b7b3fa7504156474b962fe3953c
	rna_prediction-sub-wf.cwl	https://github.com/EBI-Metagenomics/pipeline-v5.git Path: workflows/subworkflows/rna_prediction-sub-wf.cwl Branch/Commit ID: 4b98d8bf882bc96d924b5d2d4e6d9c188fa7b273
	exome alignment and somatic variant detection	https://github.com/genome/analysis-workflows.git Path: definitions/pipelines/somatic_exome_mouse.cwl Branch/Commit ID: 4bc0a4577d626b65a4b44683e5a1ab2f7d7faf4c