Explore Workflows

This is a workflow to go from UMI-tagged fastqs to standard bams. It does not include collapsing, or QC It does include modules 1 and 2

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

Path: workflows/standard_pipeline.cwl

Branch/Commit ID: 9998da2da694af2edad7c2135f6995e2282794a3

https://github.com/NAL-i5K/Organism_Onboarding.git

Path: flow_dispatch/2blat/workflow.cwl

Branch/Commit ID: f7894707dd30a0edd199d3b67c4c8678f64c90b3

Assemble a set of reads using SKESA

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

Path: assemble.cwl

Branch/Commit ID: 22ffe27d9d4a899def7592d75d5871c1856adbdb

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

Path: definitions/subworkflows/qc_exome.cwl

Branch/Commit ID: ffd73951157c61c1581d346628d75b61cdd04141

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

Path: task_types/tt_univec_wnode.cwl

Branch/Commit ID: 449f87c8365637e803ba66f83367e96f98c88f5c

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

Path: task_types/tt_kmer_cache_retrieve.cwl

Branch/Commit ID: f5c11df465aaadf712c38ba4933679fe1cbe03ca

https://git.astron.nl/RD/LINC.git

Path: workflows/HBA_target.cwl

Branch/Commit ID: 9ead9ff182f8233ffd908f72aa3b3ff516aefd9d

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: 2c486543c335bb99b245dfe7e2f033f535efb9cf

Reverse the lines in a document, then sort those lines.

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

Path: tests/wf/revsort.cwl

Branch/Commit ID: 5ae5798f1c0c8d2178986b77cfd74edff510877a

Motif Finding with HOMER with custom background regions --------------------------------------------------- HOMER contains a novel motif discovery algorithm that was designed for regulatory element analysis in genomics applications (DNA only, no protein). It is a differential motif discovery algorithm, which means that it takes two sets of sequences and tries to identify the regulatory elements that are specifically enriched in on set relative to the other. It uses ZOOPS scoring (zero or one occurrence per sequence) coupled with the hypergeometric enrichment calculations (or binomial) to determine motif enrichment. HOMER also tries its best to account for sequenced bias in the dataset. It was designed with ChIP-Seq and promoter analysis in mind, but can be applied to pretty much any nucleic acids motif finding problem. For more information please refer to: ------------------------------------- [Official documentation](http://homer.ucsd.edu/homer/motif/)

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

Path: workflows/homer-motif-analysis-bg.cwl

Branch/Commit ID: 1131f82a53315cca217a6c84b3bd272aa62e4bca