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Graph	Name	Retrieved From	View
	Running cellranger count and lineage inference	https://github.com/genome/analysis-workflows.git Path: definitions/subworkflows/single_cell_rnaseq.cwl Branch/Commit ID: 31602b94b34ff55876147c7299e1bec47e8d1a31
	Generate genome indices for STAR & bowtie Creates indices for: * [STAR](https://github.com/alexdobin/STAR) v2.5.3a (03/17/2017) PMID: [23104886](https://www.ncbi.nlm.nih.gov/pubmed/23104886) * [bowtie](http://bowtie-bio.sourceforge.net/tutorial.shtml) v1.2.0 (12/30/2016) It performs the following steps: 1. `STAR --runMode genomeGenerate` to generate indices, based on [FASTA](http://zhanglab.ccmb.med.umich.edu/FASTA/) and [GTF](http://mblab.wustl.edu/GTF2.html) input files, returns results as an array of files 2. Outputs indices as [Direcotry](http://www.commonwl.org/v1.0/CommandLineTool.html#Directory) data type 3. Separates chrNameLength.txt file from Directory output 4. `bowtie-build` to generate indices requires genome [FASTA](http://zhanglab.ccmb.med.umich.edu/FASTA/) file as input, returns results as a group of main and secondary files	https://github.com/datirium/workflows.git Path: workflows/genome-indices.cwl Branch/Commit ID: 00ea05e22788029370898fd4c17798b11edf0e57
	MEME motif This workflow uses MEME suite for motif finding	https://github.com/ncbi/cwl-ngs-workflows-cbb.git Path: workflows/ChIP-Seq/meme-motif.cwl Branch/Commit ID: 7364aa3799fd3bd7584049228618301bda53a3af
	align_merge_sas	https://github.com/ncbi/pgap.git Path: task_types/tt_align_merge_sas.cwl Branch/Commit ID: 50d161364e2859ed5c95ef07c9f7234f1431cf31
	assm_assm_blastn_wnode	https://github.com/ncbi/pgap.git Path: task_types/tt_assm_assm_blastn_wnode.cwl Branch/Commit ID: be32f1363f9a9a9247d738e9593b207e9c5172c8
	Single-Cell ATAC-Seq Differential Accessibility Analysis Single-Cell ATAC-Seq Differential Accessibility Analysis Identifies differentially accessible regions between any two groups of cells, optionally aggregating chromatin accessibility data from single-cell to pseudobulk form.	https://github.com/datirium/workflows.git Path: workflows/sc-atac-dbinding.cwl Branch/Commit ID: cc6fa135d04737fdde3b4414d6e214cf8c812f6e
	Bismark Methylation SE Sequence reads are first cleaned from adapters and transformed into fully bisulfite-converted forward (C->T) and reverse read (G->A conversion of the forward strand) versions, before they are aligned to similarly converted versions of the genome (also C->T and G->A converted). Sequence reads that produce a unique best alignment from the four alignment processes against the bisulfite genomes (which are running in parallel) are then compared to the normal genomic sequence and the methylation state of all cytosine positions in the read is inferred. A read is considered to align uniquely if an alignment has a unique best alignment score (as reported by the AS:i field). If a read produces several alignments with the same number of mismatches or with the same alignment score (AS:i field), a read (or a read-pair) is discarded altogether. On the next step we extract the methylation call for every single C analysed. The position of every single C will be written out to a new output file, depending on its context (CpG, CHG or CHH), whereby methylated Cs will be labelled as forward reads (+), non-methylated Cs as reverse reads (-). The output of the methylation extractor is then transformed into a bedGraph and coverage file. The bedGraph counts output is then used to generate a genome-wide cytosine report which reports the number on every single CpG (optionally every single cytosine) in the genome, irrespective of whether it was covered by any reads or not. As this type of report is informative for cytosines on both strands the output may be fairly large (~46mn CpG positions or >1.2bn total cytosine positions in the human genome).	https://github.com/datirium/workflows.git Path: workflows/bismark-methylation-se.cwl Branch/Commit ID: cc6fa135d04737fdde3b4414d6e214cf8c812f6e
	hmmsearch_wnode and gpx_qdump combined workflow to apply scatter/gather	https://github.com/ncbi/pgap.git Path: task_types/tt_hmmsearch_wnode_plus_qdump.cwl Branch/Commit ID: 76a9637a06e2102645eae29aff10b6f7185892a5
	Trim Galore RNA-Seq pipeline paired-end strand specific Modified original [BioWardrobe's](https://biowardrobe.com) [PubMed ID:26248465](https://www.ncbi.nlm.nih.gov/pubmed/26248465) RNA-Seq basic analysis for a pair-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 files 2. Use STAR to align reads from input FASTQ files according to the predefined reference indices; generate unsorted BAM file and alignment statistics file 3. Use fastx_quality_stats to analyze input FASTQ files and generate quality statistics files 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 files 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-pe-dutp.cwl Branch/Commit ID: c6bfa0de917efb536dd385624fc7702e6748e61d
	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: 0c4f4e59c265eb22aed3d2d37b173cb5430773d2