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Graph	Name	Retrieved From	View
	Seed Protein Alignments	https://github.com/ncbi/pgap.git Path: protein_alignment/wf_seed_seqids.cwl Branch/Commit ID: 4ffbad9ffeab15ec8af5f6f91bd352ef96d1ef77
	Bismark Methylation PE 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-pe.cwl Branch/Commit ID: d76110e0bfc40c874f82e37cef6451d74df4f908
	scRNA-seq pipeline using Salmon and Alevin	https://github.com/hubmapconsortium/salmon-rnaseq.git Path: pipeline.cwl Branch/Commit ID: 0e913979ac4a989a482211dfdf6fe204d26b05ab
	Salmon quantification, FASTQ -> H5AD count matrix	https://github.com/hubmapconsortium/salmon-rnaseq.git Path: steps/salmon-quantification.cwl Branch/Commit ID: 0e913979ac4a989a482211dfdf6fe204d26b05ab
	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: d76110e0bfc40c874f82e37cef6451d74df4f908
	kmer_cache_retrieve	https://github.com/ncbi/pgap.git Path: task_types/tt_kmer_cache_retrieve.cwl Branch/Commit ID: 1cfd46014be8d867044cb10d1ddde0cb3068ee84
	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: 5b498b4c4f17bb8f17e6886aa4c5661d7aba34fc
	extract_gencoll_ids	https://github.com/ncbi/pgap.git Path: task_types/tt_extract_gencoll_ids.cwl Branch/Commit ID: 1cfd46014be8d867044cb10d1ddde0cb3068ee84
	Single-Cell Manual Cell Type Assignment Single-Cell Manual Cell Type Assignment Assigns identities to cells clustered with any of the “Single-Cell Cluster Analysis” pipelines. For “Single-Cell RNA-Seq Cluster Analysis” the results of this workflow are used in the “Single-Cell RNA-Seq Differential Expression Analysis”, “Single-Cell RNA-Seq Trajectory Analysis”, and — when combined with outputs from the “Cell Ranger Count (RNA+VDJ)” or “Cell Ranger Aggregate (RNA, RNA+VDJ)” workflow — in the “Single-Cell Immune Profiling Analysis” pipeline. For “Single-Cell ATAC-Seq Cluster Analysis”, the results of this workflow are used in the “Single-Cell ATAC-Seq Differential Accessibility Analysis” and “Single-Cell ATAC-Seq Genome Coverage” pipelines. For “Single-Cell WNN Cluster Analysis”, the results of this workflow are used in all of the above, except the “Single-Cell Immune Profiling Analysis” pipeline.	https://github.com/datirium/workflows.git Path: workflows/sc-ctype-assign.cwl Branch/Commit ID: d76110e0bfc40c874f82e37cef6451d74df4f908
	super-enhancer.cwl Both `islands_file` and `islands_control_file` should be produced by the same cwl tool (iaintersect.cwl or macs2-callpeak-biowardrobe-only.cwl)	https://github.com/Barski-lab/workflows.git Path: workflows/super-enhancer.cwl Branch/Commit ID: 896422c9ff1995024cb77675edcd4d973ae11f7a