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Graph	Name	Retrieved From	View
	umi molecular alignment fastq workflow	https://github.com/genome/analysis-workflows.git Path: definitions/pipelines/alignment_umi_molecular.cwl Branch/Commit ID: 77ec4f26eb14ed82481828bd9f6ef659cfd8b40f
	format_rrnas_from_seq_entry	https://github.com/ncbi/pgap.git Path: task_types/tt_format_rrnas_from_seq_entry.cwl Branch/Commit ID: 001fab592188cb525afa1c4db6226b833faec106
	Motif Finding with HOMER with custom background regions Motif Finding with HOMER with custom 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. 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-bg.cwl Branch/Commit ID: 2f0db4b3c515f91c5cfda19c78cf90d339390986
	Bismark Methylation - pipeline for BS-Seq data analysis 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: 8bf36bfad5624fbc8fc315e82783a44e9e5e4470
	Filter ChIP/ATAC peaks for Tag Density Profile or Motif Enrichment analyses Filters ChIP/ATAC peaks with the neatest genes assigned for Tag Density Profile or Motif Enrichment analyses ============================================================================================================ Tool filters output from any ChIP/ATAC pipeline to create a file with regions of interest for Tag Density Profile or Motif Enrichment analyses. Peaks with duplicated coordinates are discarded.	https://github.com/datirium/workflows.git Path: workflows/filter-peaks-for-heatmap.cwl Branch/Commit ID: 2caa50434966ebdf4b33e5ca689c2e4df32f9058
	AltAnalyze Prepare Genome Devel version of AltAnalyze Prepare Genome ========================================== hg38 is not supported. Use hardcoded EnsMart72 until AltAnalyze starts support more recent Ensembl releases.	https://github.com/datirium/workflows.git Path: workflows/altanalyze-prepare-genome.cwl Branch/Commit ID: 7ced5a5259dbd8b3fc64456beaeffd44f4a24081
	Chipseq alignment with qc and creating homer tag directory	https://github.com/genome/analysis-workflows.git Path: definitions/pipelines/chipseq.cwl Branch/Commit ID: 43c790e2ee6a0f3f42e40fb4d9a9005eb8de747a
	Detect Variants workflow	https://github.com/genome/analysis-workflows.git Path: definitions/pipelines/detect_variants.cwl Branch/Commit ID: 43c790e2ee6a0f3f42e40fb4d9a9005eb8de747a
	THOR - differential peak calling of ChIP-seq signals with replicates What is THOR? -------------- THOR is an HMM-based approach to detect and analyze differential peaks in two sets of ChIP-seq data from distinct biological conditions with replicates. THOR performs genomic signal processing, peak calling and p-value calculation in an integrated framework. For more information please refer to: ------------------------------------- Allhoff, M., Sere K., Freitas, J., Zenke, M., Costa, I.G. (2016), Differential Peak Calling of ChIP-seq Signals with Replicates with THOR, Nucleic Acids Research, epub gkw680.	https://github.com/datirium/workflows.git Path: workflows/rgt-thor.cwl Branch/Commit ID: 8a92669a566589d80fde9d151054ffc220ed4ddd
	exome alignment and germline variant detection	https://github.com/genome/analysis-workflows.git Path: definitions/subworkflows/germline_detect_variants.cwl Branch/Commit ID: 43c790e2ee6a0f3f42e40fb4d9a9005eb8de747a