Explore Workflows

View already parsed workflows here or click here to add your own

Graph	Name	Retrieved From	View
	Unaligned BAM to BQSR and VCF	https://github.com/genome/analysis-workflows.git Path: definitions/subworkflows/bam_to_bqsr_no_dup_marking.cwl Branch/Commit ID: 869b331cfeb9dbd5907498e3eccdebc7c28283e5
	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: 92118627c800e4addb7e29b9dabcca073a5bae71
	Merge, annotate, and generate a TSV for SVs	https://github.com/genome/analysis-workflows.git Path: definitions/subworkflows/merge_svs.cwl Branch/Commit ID: 889a077a20c0fdb01f4ed97aa4bc40f920c37a1a
	DiffBind - Differential Binding Analysis of ChIP-Seq Peak Data Differential Binding Analysis of ChIP-Seq Peak Data --------------------------------------------------- DiffBind processes ChIP-Seq data enriched for genomic loci where specific protein/DNA binding occurs, including peak sets identified by ChIP-Seq peak callers and aligned sequence read datasets. It is designed to work with multiple peak sets simultaneously, representing different ChIP experiments (antibodies, transcription factor and/or histone marks, experimental conditions, replicates) as well as managing the results of multiple peak callers. For more information please refer to: ------------------------------------- Ross-Innes CS, Stark R, Teschendorff AE, Holmes KA, Ali HR, Dunning MJ, Brown GD, Gojis O, Ellis IO, Green AR, Ali S, Chin S, Palmieri C, Caldas C, Carroll JS (2012). “Differential oestrogen receptor binding is associated with clinical outcome in breast cancer.” Nature, 481, -4.	https://github.com/datirium/workflows.git Path: workflows/diffbind.cwl Branch/Commit ID: 7fb8a1ebf8145791440bc2fed9c5f2d78a19d04c
	Gathered Downsample and HaplotypeCaller	https://github.com/genome/analysis-workflows.git Path: definitions/pipelines/gathered_downsample_and_recall.cwl Branch/Commit ID: fbeea265295ae596d5a3ba563e766be0c4fc26e8
	align_merge_sas	https://github.com/ncbi/pgap.git Path: task_types/tt_align_merge_sas.cwl Branch/Commit ID: 92118627c800e4addb7e29b9dabcca073a5bae71
	kmer_cache_store	https://github.com/ncbi/pgap.git Path: task_types/tt_kmer_cache_store.cwl Branch/Commit ID: f1eb0f4eaaf1661044f28d859f7e8d4302525ead
	merge-bam-parallel This workflow merge BAM files per condition in parallel	https://github.com/ncbi/cwl-ngs-workflows-cbb.git Path: workflows/File-formats/merge-bam-parallel.cwl Branch/Commit ID: dde32ff6c8e653a4e6b93316f28737706d5ec367
	running cellranger mkfastq and count	https://github.com/genome/analysis-workflows.git Path: definitions/subworkflows/cellranger_mkfastq_and_count.cwl Branch/Commit ID: 869b331cfeb9dbd5907498e3eccdebc7c28283e5
	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: 581156366f91861bd4dbb5bcb59f67d468b32af3