Explore Workflows

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

Path: bacterial_orthology/wf_bacterial_orthology.cwl

Branch/Commit ID: master

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

Path: tests/wf/mut3.cwl

Branch/Commit ID: main

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 - Chen EY, Tan CM, Kou Y, Duan Q, Wang Z, Meirelles GV, Clark NR, Ma'ayan A. Enrichr: interactive and collaborative HTML5 gene list enrichment analysis tool. BMC Bioinformatics. 2013; 128(14). - Kuleshov MV, Jones MR, Rouillard AD, Fernandez NF, Duan Q, Wang Z, Koplev S, Jenkins SL, Jagodnik KM, Lachmann A, McDermott MG, Monteiro CD, Gundersen GW, Ma'ayan A. Enrichr: a comprehensive gene set enrichment analysis web server 2016 update. Nucleic Acids Research. 2016; gkw377 . - Xie Z, Bailey A, Kuleshov MV, Clarke DJB., Evangelista JE, Jenkins SL, Lachmann A, Wojciechowicz ML, Kropiwnicki E, Jagodnik KM, Jeon M, & Ma’ayan A. Gene set knowledge discovery with Enrichr. Current Protocols, 1, e90. 2021. doi: 10.1002/cpz1.90

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

Path: workflows/gseapy.cwl

Branch/Commit ID: master

https://github.com/proteinswebteam/ebi-metagenomics-cwl.git

Path: tools/collate_unique_SSU_headers.cwl

Branch/Commit ID: 5dc7c5c

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

Path: bacterial_noncoding/wf_gcmsearch.cwl

Branch/Commit ID: test

Run BQSR pre+post+plot flow

https://github.com/Sentieon/Sentieon-cwl.git

Path: stage/bqsr-flow.cwl

Branch/Commit ID: master

https://github.com/ProteinsWebTeam/ebi-metagenomics-cwl.git

Path: workflows/16S_taxonomic_analysis.cwl

Branch/Commit ID: 0cd2d70

Differential abundance testing with ANCOM from https://docs.qiime2.org/2018.4/tutorials/moving-pictures/

https://github.com/Duke-GCB/bespin-cwl.git

Path: packed/qiime2-step2-dada2.cwl

Branch/Commit ID: qiime2-workflow

Packed ID: qiime2-09-ancom.cwl

This workflow is intended to be used to test the QC module, without having to run the long waltz step

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

Path: workflows/QC/qc_workflow_wo_waltz.cwl

Branch/Commit ID: 0.0.33_dmp

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

Path: v1.0/v1.0/count-lines10-wf.cwl

Branch/Commit ID: master