Explore Workflows

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

Graph	Name	Retrieved From	View
	cluster_blastp_wnode and gpx_qdump combined	https://github.com/ncbi/pgap.git Path: task_types/tt_cluster_and_qdump.cwl Branch/Commit ID: 8fb4ac7f5a66897206c7469101a471108b06eada
	tt_kmer_compare_wnode Pairwise comparison	https://github.com/ncbi/pgap.git Path: task_types/tt_kmer_compare_wnode.cwl Branch/Commit ID: 72804b6506c9f54ec75627f82aafe6a28d7a49fa
	sc_atac_seq_initial_analysis.cwl	https://github.com/hubmapconsortium/sc-atac-seq-pipeline.git Path: steps/sc_atac_seq_initial_analysis.cwl Branch/Commit ID: 06aeffe8b2a13e62d412d6e1b03482f6b9247960
	sc_atac_seq_prep_process_init.cwl	https://github.com/hubmapconsortium/multiome-rna-atac-pipeline.git Path: sc-atac-seq-pipeline/steps/sc_atac_seq_prep_process_init.cwl Branch/Commit ID: 68e0cc1be35751f5ef5958050742ddfffd564d3c
	rnaseq-pe.cwl Runs RNA-Seq BioWardrobe basic analysis with pair-end data file.	https://github.com/Barski-lab/workflows.git Path: workflows/rnaseq-pe.cwl Branch/Commit ID: 8587882f145d3eb8e258e7bf819a94f8dd666dbf
	multiome pipeline using Salmon and Alevin (HuBMAP scRNA-seq pipeline) and HuBMAP scATAC-seq pipeline	https://github.com/hubmapconsortium/multiome-rna-atac-pipeline.git Path: pipeline.cwl Branch/Commit ID: 68e0cc1be35751f5ef5958050742ddfffd564d3c
	gathered exome alignment and somatic variant detection for cle purpose	https://github.com/genome/analysis-workflows.git Path: definitions/pipelines/somatic_exome_cle_gathered.cwl Branch/Commit ID: 22fce2dbdada0c4135b6f0677f78535cf980cb07
	umi molecular alignment workflow	https://github.com/genome/analysis-workflows.git Path: definitions/subworkflows/molecular_qc.cwl Branch/Commit ID: 2979b565f88ceebca934611adbf3fb8cefd65a19
	Motif Finding with HOMER with target and background regions from peaks Motif Finding with HOMER with target and background regions from peaks --------------------------------------------------- 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-peak.cwl Branch/Commit ID: 5561f7ee11dd74848680351411a19aa87b13d27b
	Detect Variants workflow	https://github.com/genome/analysis-workflows.git Path: definitions/pipelines/detect_variants_mouse.cwl Branch/Commit ID: 9143dc4ebacb9e1df36a712b0be6fa5d982b0c4f