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Graph	Name	Retrieved From	View
	Trim Galore RNA-Seq pipeline single-read strand specific Note: should be updated The original [BioWardrobe's](https://biowardrobe.com) [PubMed ID:26248465](https://www.ncbi.nlm.nih.gov/pubmed/26248465) RNA-Seq basic analysis for a single-end experiment. A corresponded input [FASTQ](http://maq.sourceforge.net/fastq.shtml) file has to be provided. Current workflow should be used only with the single-end RNA-Seq data. It performs the following steps: 1. Trim adapters from input FASTQ file 2. Use STAR to align reads from input FASTQ file according to the predefined reference indices; generate unsorted BAM file and alignment statistics file 3. Use fastx_quality_stats to analyze input FASTQ file and generate quality statistics file 4. Use 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/trim-rnaseq-se-dutp.cwl Branch/Commit ID: 9850a859de1f42d3d252c50e15701928856fe774
	rRNA_selection.cwl	https://github.com/proteinswebteam/ebi-metagenomics-cwl.git Path: tools/rRNA_selection.cwl Branch/Commit ID: 71d9c83761ea301a895dd669902979ef5a4b279b
	gather AML trio outputs	https://github.com/genome/analysis-workflows.git Path: definitions/pipelines/aml_trio_cle_gathered.cwl Branch/Commit ID: 174f3b239018328cec1d821947438b457552724c
	Single-Cell Preprocessing Pipeline Devel version of Single-Cell Preprocessing Pipeline ===================================================	https://github.com/datirium/workflows.git Path: workflows/single-cell-preprocess.cwl Branch/Commit ID: a8eaf61c809d76f55780b14f2febeb363cf6373f
	Trim Galore ChIP-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. _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. 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-chipseq-se.cwl Branch/Commit ID: ad948b2691ef7f0f34de38f0102c3cd6f5182b29
	cram_to_bam workflow	https://github.com/genome/analysis-workflows.git Path: definitions/subworkflows/cram_to_bam_and_index.cwl Branch/Commit ID: 6f9f8a2057c6a9f221a44559f671e87a75c70075
	Functional analyis of sequences that match the 16S SSU	https://github.com/ProteinsWebTeam/ebi-metagenomics-cwl.git Path: workflows/16S_taxonomic_analysis.cwl Branch/Commit ID: f993cada89d2c6f7480a0d56baa7836a361b1f3a
	Varscan Workflow	https://github.com/genome/analysis-workflows.git Path: definitions/subworkflows/varscan_germline.cwl Branch/Commit ID: a59a803e1809a8fbfabca6b8962a8ad66dd01f1d
	functional analysis prediction with InterProScan	https://github.com/ProteinsWebTeam/ebi-metagenomics-cwl.git Path: workflows/functional_analysis.cwl Branch/Commit ID: b6d3aaf3fa6695061208c6cdca3d7881cc45400d
	Cut-n-Run pipeline paired-end Experimental pipeline for Cut-n-Run analysis. Uses mapping results from the following experiment types: - `chipseq-pe.cwl` - `trim-chipseq-pe.cwl` - `trim-atacseq-pe.cwl` Note, the upstream analyses should not have duplicates removed	https://github.com/datirium/workflows.git Path: workflows/trim-chipseq-pe-cut-n-run.cwl Branch/Commit ID: 581156366f91861bd4dbb5bcb59f67d468b32af3