Explore Workflows

Runs RNA-Seq BioWardrobe basic analysis with single-end data file.

https://github.com/Barski-lab/workflows.git

Path: workflows/rnaseq-se.cwl

Branch/Commit ID: 144eee15187c1a1145ce1ee0239da69059fd2752

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

Path: definitions/subworkflows/qc_exome_no_verify_bam.cwl

Branch/Commit ID: 60edaf6f57eaaf02cda1a3d8cb9a825aa64a43e2

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: 30031ca5e69cec603c4733681de54dc7bffa20a3

DESeq2 Multi-factor Analysis Runs DeSeq2 multi-factor analysis with manual control over major parameters

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

Path: workflows/deseq-multi-factor.cwl

Branch/Commit ID: 30031ca5e69cec603c4733681de54dc7bffa20a3

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

Path: cwltool/schemas/v1.0/v1.0/steplevel-resreq.cwl

Branch/Commit ID: 8010fd2bf1e7090ba6df6ca8c84bbb96e2272d32

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

Path: task_types/tt_checkm_wnode.cwl

Branch/Commit ID: 54c5074587af001a44eccb4762a4cb25fa24cb3e

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

Path: definitions/pipelines/tumor_only_detect_variants.cwl

Branch/Commit ID: 5be54bf09092c53e6c7797a875f64a360d511d7f

reproduces https://github.com/DataBiosphere/toil/issues/3141

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

Path: tests/default_with_falsey_value.cwl

Branch/Commit ID: c7c97715b400ff2194aa29fc211d3401cea3a9bf

This workflow indexes the input reference FASTA with kallisto, and generates a kallisto index file (.kdx). This index sample can then be used as input into the kallisto transcript-level quantification workflow (kallisto-quant-pe.cwl), or others that may include this workflow as an upstream source. ### __Inputs__ - FASTA file of the reference genome that will be indexed - number of threads to use for multithreading processes ### __Outputs__ - kallisto index file (.kdx). - stdout log file (output in Overview tab as well) - stderr log file ### __Data Analysis Steps__ 1. cwl calls dockercontainer robertplayer/scidap-kallisto to index reference FASTA with `kallisto index`, generating a kallisto index file. ### __References__ - Bray, N. L., Pimentel, H., Melsted, P. & Pachter, L. Near-optimal probabilistic RNA-seq quantification, Nature Biotechnology 34, 525-527(2016), doi:10.1038/nbt.3519

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

Path: workflows/kallisto-index.cwl

Branch/Commit ID: 30031ca5e69cec603c4733681de54dc7bffa20a3

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

Path: task_types/tt_extract_gencoll_ids.cwl

Branch/Commit ID: 42df0c0f9a4e5697abadd9cb52440691fafc8f5d