Explore Workflows

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

Path: task_types/tt_cache_asnb_entries.cwl

Branch/Commit ID: 5331b0836aa7c451d759ef39dc2062000ac21a47

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: e0a30aa1ad516dd2ec0e9ce006428964b840daf4

https://github.com/stain/workflow-is-cwl.git

Path: workflows/TransDecoder-v5-wf-2steps.cwl

Branch/Commit ID: b1e88a8c2f6f07d236193d3e89dc2d724700780a

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

Path: definitions/pipelines/bisulfite.cwl

Branch/Commit ID: 0b6e8fd8ead7644cf5398395b76af5cf4011686f

https://github.com/cancerit/workflow-seq-import.git

Path: cwls/chksum_xam_to_interleaved_fq.cwl

Branch/Commit ID: 11212c6812774859c74246e04b32d6386e76256c

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: e0a30aa1ad516dd2ec0e9ce006428964b840daf4

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

Path: definitions/pipelines/germline_exome_gvcf.cwl

Branch/Commit ID: 0b6e8fd8ead7644cf5398395b76af5cf4011686f

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

Path: definitions/subworkflows/cram_to_bam_and_index.cwl

Branch/Commit ID: 449bc7e45bb02316d040f73838ef18359e770268

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

Path: definitions/pipelines/rnaseq_star_fusion.cwl

Branch/Commit ID: 0b6e8fd8ead7644cf5398395b76af5cf4011686f

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

Path: definitions/subworkflows/sequence_to_bqsr_nonhuman.cwl

Branch/Commit ID: 77ec4f26eb14ed82481828bd9f6ef659cfd8b40f