Explore Workflows

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

Graph Name Retrieved From View
workflow graph 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: e1c19e64f6fc210f65472ee227786d33c9b4909a
workflow graph Create tagAlign file

This workflow creates tagAlign file

 https://github.com/ncbi/cwl-ngs-workflows-cbb.git

Path: workflows/File-formats/create-tagAlign.cwl

Branch/Commit ID: e1c19e64f6fc210f65472ee227786d33c9b4909a
workflow graph tt_fscr_calls_pass1

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

Path: task_types/tt_fscr_calls_pass1.cwl

Branch/Commit ID: c17cac4c046f8ba2b8574a121c44a72d2e6b27e6
workflow graph Seed Search Compartments

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

Path: protein_alignment/wf_seed.cwl

Branch/Commit ID: c17cac4c046f8ba2b8574a121c44a72d2e6b27e6
workflow graph testTimeSIMLR.cwl

https://github.com/Gepiro/rCASC_StreamFlow.git

Path: setting/testTimeSIMLR.cwl

Branch/Commit ID: 130d399d0fbbbd65042d103176be67d7952ba003
workflow graph module-3.cwl

https://github.com/mskcc/ACCESS-Pipeline.git

Path: workflows/module-3.cwl

Branch/Commit ID: b0f226a9ac5152f3afe0d38c8cd54aa25b8b01cf
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. Documents ============================================== - GSEA Home Page: https://www.gsea-msigdb.org/gsea/index.jsp - Results Interpretation: https://www.gsea-msigdb.org/gsea/doc/GSEAUserGuideTEXT.htm#_Interpreting_GSEA_Results - GSEA User Guide: https://gseapy.readthedocs.io/en/latest/faq.html - GSEAPY Docs: https://gseapy.readthedocs.io/en/latest/introduction.html References ============================================== - Subramanian, Tamayo, et al. (2005, PNAS), https://www.pnas.org/content/102/43/15545 - Mootha, Lindgren, et al. (2003, Nature Genetics), http://www.nature.com/ng/journal/v34/n3/abs/ng1180.html

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

Path: workflows/gseapy.cwl

Branch/Commit ID: 12e5256de1b680c551c87fd5db6f3bc65428af67
workflow graph scatter-wf2.cwl

https://github.com/common-workflow-language/cwltool.git

Path: cwltool/schemas/v1.0/v1.0/scatter-wf2.cwl

Branch/Commit ID: f207d168f4e7eb4dd2279840d4062ba75d9c79c3
workflow graph taxonomy_check_16S

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

Path: task_types/tt_taxonomy_check_16S.cwl

Branch/Commit ID: e71779665f42fcf34601b0f65e030bb0dd47fa79
workflow graph count-lines1-wf-noET.cwl

https://github.com/common-workflow-language/cwl-v1.2.git

Path: tests/count-lines1-wf-noET.cwl

Branch/Commit ID: a5073143db4155e05df8d2e7eb59d9e62acd65a5