Explore Workflows
View already parsed workflows here or click here to add your own
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blastp_wnode_struct
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Path: task_types/tt_blastp_wnode_struct.cwl Branch/Commit ID: c17cac4c046f8ba2b8574a121c44a72d2e6b27e6 |
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cnv_manta
CNV Manta calling |
Path: structuralvariants/subworkflows/cnv_manta.cwl Branch/Commit ID: e1fd26587a78afc376c10bf6db36abd2c840768e |
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Single-cell Differential Expression
Single-cell Differential Expression =================================== Runs differential expression analysis for a subset of cells between two selected conditions. |
Path: workflows/sc_diff_expr.cwl Branch/Commit ID: 7ae3b75bbe614e59cdeaba06047234a6c40c0fe9 |
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Generate genome indices for Bismark
Copy input fasta file to the folder and run bismark_genome_preparation script to prepare indices for Bismark Methylation Analysis. Bowtie2 aligner is used by default. The name of the output indices folder is equal to the fasta file basename without extension. |
Path: workflows/bismark-indices.cwl Branch/Commit ID: 9ee330737f4603e4e959ffe786fbb2046db70a00 |
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echo-wf-default.cwl
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Path: cwltool/schemas/v1.0/v1.0/echo-wf-default.cwl Branch/Commit ID: 2ae8117360a3cd4909d9d3f2b35c30bfffb25d0a |
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cache_asnb_entries
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Path: task_types/tt_cache_asnb_entries.cwl Branch/Commit ID: 122aba2dafbb63241413c82b725b877c04523aaf |
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Cut-n-Run pipeline paired-end
Experimental pipeline for Cut-n-Run analysis. Uses mapping results from the following experiment types: - `chipseq-pe.cwl` - `trim-chipseq-pe.cwl` - `trim-atacseq-pe.cwl` Note, the upstream analyses should not have duplicates removed |
Path: workflows/trim-chipseq-pe-cut-n-run.cwl Branch/Commit ID: 5561f7ee11dd74848680351411a19aa87b13d27b |
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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/) |
Path: workflows/homer-motif-analysis-bg.cwl Branch/Commit ID: 7ae3b75bbe614e59cdeaba06047234a6c40c0fe9 |
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scatter-valuefrom-wf5.cwl
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Path: cwltool/schemas/v1.0/v1.0/scatter-valuefrom-wf5.cwl Branch/Commit ID: c6cced7a2e6389d2eb43342e702677ccb7c7497c |
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ani_top_n
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Path: task_types/tt_ani_top_n.cwl Branch/Commit ID: 122aba2dafbb63241413c82b725b877c04523aaf |
