Explore Workflows

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

Graph Name Retrieved From View
workflow graph cram_to_bam workflow

https://github.com/genome/analysis-workflows.git

Path: definitions/subworkflows/cram_to_bam_and_index.cwl

Branch/Commit ID: 0805e8e0d358136468e0a9f49e06005e41965adc
workflow graph cluster_blastp_wnode and gpx_qdump combined

https://github.com/ncbi/pgap.git

Path: task_types/tt_cluster_and_qdump.cwl

Branch/Commit ID: 2801ce53744a085580a8de91cd007c45146b51e8
workflow graph 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

 https://github.com/datirium/workflows.git

Path: workflows/trim-chipseq-pe-cut-n-run.cwl

Branch/Commit ID: 4ab9399a4777610a579ea2c259b9356f27641dcc
workflow graph tt_hmmsearch_wnode.cwl

https://github.com/ncbi/pgap.git

Path: task_types/tt_hmmsearch_wnode.cwl

Branch/Commit ID: 5282690e0f634a5f83107ba878fe62cbbb347408
workflow graph Bisulfite alignment and QC

https://github.com/genome/analysis-workflows.git

Path: definitions/pipelines/bisulfite.cwl

Branch/Commit ID: 1750cd5cc653f058f521b6195e3bec1e7df1a086
workflow graph Cell Ranger Build Reference Indices

Cell Ranger Build Reference Indices ===================================

 https://github.com/datirium/workflows.git

Path: workflows/cellranger-mkref.cwl

Branch/Commit ID: 60854b5d299df91e135e05d02f4be61f6a310fbc
workflow graph running cellranger mkfastq and count

https://github.com/genome/analysis-workflows.git

Path: definitions/subworkflows/cellranger_mkfastq_and_count.cwl

Branch/Commit ID: 6f9f8a2057c6a9f221a44559f671e87a75c70075
workflow graph Filter single sample sv vcf from paired read callers(Manta/Smoove)

https://github.com/genome/analysis-workflows.git

Path: definitions/subworkflows/sv_paired_read_caller_filter.cwl

Branch/Commit ID: da335d9963418f7bedd84cb2791a0df1b3165ffe
workflow graph GSEApy - Gene Set Enrichment Analysis in Python

GSEAPY: Gene Set Enrichment Analysis in Python ============================================== Gene Set Enrichment Analysis is a computational method that determines whether an a priori defined set of genes shows statistically significant, concordant differences between two biological states (e.g. phenotypes). GSEA requires as input an expression dataset, which contains expression profiles for multiple samples. While the software supports multiple input file formats for these datasets, the tab-delimited GCT format is the most common. The first column of the GCT file contains feature identifiers (gene ids or symbols in the case of data derived from RNA-Seq experiments). The second column contains a description of the feature; this column is ignored by GSEA and may be filled with “NA”s. Subsequent columns contain the expression values for each feature, with one sample's expression value per column. It is important to note that there are no hard and fast rules regarding how a GCT file's expression values are derived. The important point is that they are comparable to one another across features within a sample and comparable to one another across samples. Tools such as DESeq2 can be made to produce properly normalized data (normalized counts) which are compatible with GSEA.

 https://github.com/datirium/workflows.git

Path: workflows/gseapy.cwl

Branch/Commit ID: cbefc215d8286447620664fb47076ba5d81aa47f
workflow graph cnv_exomedepth

CNV ExomeDepth calling

https://gitlab.bsc.es/lrodrig1/structuralvariants_poc.git

Path: structuralvariants/cwl/abstract_operations/subworkflows/cnv_exome_depth.cwl

Branch/Commit ID: de9cb009f8fe0c8d5a94db5c882cf21ddf372452