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Graph	Name	Retrieved From	View
	Pipeline for evaluating differential expression of genes across datasets	https://github.com/hubmapconsortium/rna-data-products.git Path: pipeline.cwl Branch/Commit ID: 52b0d9dbd8e14025e6e1dcfe8f5ad5f9e0cb80d2
	Dockstore.cwl	https://github.com/kathy-t/ghapps-single-workflow.git Path: Dockstore.cwl Branch/Commit ID: a417425d00767444de03b4a875163543159f7d43
	sc_atac_seq_prep_process_analyze.cwl	https://github.com/hubmapconsortium/sc-atac-seq-pipeline.git Path: sc_atac_seq_prep_process_analyze.cwl Branch/Commit ID: 3da5dd0c6f974ec62f78d654f0ce7948975e741f
	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: 664de58d95728edbf7d369d894f9037ebe2475fa
	Motif Finding with HOMER with random background regions Motif Finding with HOMER with random background regions --------------------------------------------------- HOMER contains a novel motif discovery algorithm that was designed for regulatory element analysis in genomics applications (DNA only, no protein). It is a differential motif discovery algorithm, which means that it takes two sets of sequences and tries to identify the regulatory elements that are specifically enriched in on set relative to the other. It uses ZOOPS scoring (zero or one occurrence per sequence) coupled with the hypergeometric enrichment calculations (or binomial) to determine motif enrichment. HOMER also tries its best to account for sequenced bias in the dataset. It was designed with ChIP-Seq and promoter analysis in mind, but can be applied to pretty much any nucleic acids motif finding problem. Here is how we generate background for Motifs Analysis ------------------------------------- 1. Take input file with regions in a form of “chr\" “start\" “end\" 2. Sort and remove duplicates from this regions file 3. Extend each region in 20Kb into both directions 4. Merge all overlapped extended regions 5. Subtract not extended regions from the extended ones 6. Randomly distribute not extended regions within the regions that we got as a result of the previous step 7. Get fasta file from these randomly distributed regions (from the previous step). Use it as background For more information please refer to: ------------------------------------- [Official documentation](http://homer.ucsd.edu/homer/motif/)	https://github.com/datirium/workflows.git Path: workflows/homer-motif-analysis.cwl Branch/Commit ID: c6bfa0de917efb536dd385624fc7702e6748e61d
	count-lines5-wf.cwl	https://github.com/common-workflow-language/cwl-v1.1.git Path: tests/count-lines5-wf.cwl Branch/Commit ID: 664835e83eb5e57eee18a04ce7b05fb9d70d77b7
	Run pindel on provided region	https://github.com/genome/analysis-workflows.git Path: definitions/subworkflows/pindel_region.cwl Branch/Commit ID: f42c889734c8f709ad2fd9090493bcaac8326c98
	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: 735be84cdea041fcc8bd8cbe5728b29ca3586a21
	Single-cell ATAC-Seq Cluster Analysis Single-cell ATAC-Seq Cluster Analysis Clusters single-cell ATAC-Seq datasets, identifies differentially accessible peaks.	https://github.com/datirium/workflows.git Path: workflows/sc-atac-cluster.cwl Branch/Commit ID: 22880e0f41d0420a17d643e8a6e8ee18165bbfbf
	Cell Ranger Aggregate Cell Ranger Aggregate Aggregates outputs from multiple runs of Cell Ranger Count Gene Expression or Cell Ranger Multi Gene Expression and V(D)J Repertoire Profiling experiments	https://github.com/datirium/workflows.git Path: workflows/cellranger-aggr.cwl Branch/Commit ID: 7030da528559c7106d156284e50ff0ecedab0c4e