Explore Workflows
View already parsed workflows here or click here to add your own
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tt_blastn_wnode
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https://github.com/ncbi/pgap.git
Path: task_types/tt_blastn_wnode.cwl Branch/Commit ID: e2a6cbcc36212433d8fbc804919442787a5e2a49 |
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adapter for sequence_align_and_tag
Some workflow engines won't stage files in our nested structure, so parse it out here |
https://github.com/genome/analysis-workflows.git
Path: definitions/subworkflows/sequence_align_and_tag_adapter.cwl Branch/Commit ID: 7638b3075863ae8172f4adaec82fb2eb8e80d3d5 |
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MACE ChIP-exo peak caller workflow for single-end samples
This workflow execute peak caller and QC from ChIP-exo for single-end samples using MACE |
https://github.com/ncbi/cwl-ngs-workflows-cbb.git
Path: workflows/ChIP-exo/peak-caller-MACE-SE.cwl Branch/Commit ID: 0207b0171ab142dfb85db9c39050c5b4be51dd9e |
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Varscan Workflow
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https://github.com/genome/analysis-workflows.git
Path: definitions/subworkflows/varscan_germline.cwl Branch/Commit ID: 3034168d652bfa930ba09af20e473a4564a8010d |
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align_merge_sas
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https://github.com/ncbi/pgap.git
Path: task_types/tt_align_merge_sas.cwl Branch/Commit ID: f225cd99b0e0a5043dd102f8b33a6139fefe9ea4 |
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gcaccess_from_list
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https://github.com/ncbi/pgap.git
Path: task_types/tt_gcaccess_from_list.cwl Branch/Commit ID: 5463361069e263ad6455858e054c1337b1d9e752 |
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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: 104059e07a2964673e21d371763e33c0afeb2d03 |
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1st-workflow.cwl
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https://github.com/BiodataAnalysisGroup/intro-to-cwl-docker.git
Path: _includes/cwl/1st-workflow.cwl Branch/Commit ID: 5e18a40c1c17696b0b37ac8d7c11ce9a19f247d9 |
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Alignment without BQSR
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https://github.com/genome/analysis-workflows.git
Path: definitions/subworkflows/sequence_to_bqsr_nonhuman.cwl Branch/Commit ID: 480c438a6a7e78c624712aec01bc4214d2bc179c |
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AltAnalyze Build Reference Indices
AltAnalyze Build Reference Indices ================================== |
https://github.com/datirium/workflows.git
Path: workflows/altanalyze-prepare-genome.cwl Branch/Commit ID: 8049a781ac4aae579fbd3036fa0bf654532f15be |
