Explore Workflows

Motif Finding with HOMER with target and background regions from peaks --------------------------------------------------- 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-peak.cwl

Branch/Commit ID: a8eaf61c809d76f55780b14f2febeb363cf6373f

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

Path: task_types/tt_assm_assm_blastn_wnode.cwl

Branch/Commit ID: cd97086739ae5988bab09b05e9259675c4b6bce6

https://github.com/EBI-Metagenomics/ebi-metagenomics-cwl.git

Path: workflows/emg-qc-single.cwl

Branch/Commit ID: 7bb76f33bf40b5cd2604001cac46f967a209c47f

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

Path: workflows/emg-core-analysis-v4.cwl

Branch/Commit ID: cac44f2cf14110fde9951161c663c4525772f616

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: 4700fbee9a5a3271eef8bc9ee595619d0720431b

https://doi.org/10.1007/s12275-011-1213-z

https://github.com/EBI-Metagenomics/ebi-metagenomics-cwl.git

Path: workflows/rna-selector.cwl

Branch/Commit ID: 3f85843d4a6debdabe96bc800bf2a4efdcda1ef3

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

Path: definitions/subworkflows/bam_to_trimmed_fastq_and_biscuit_alignments.cwl

Branch/Commit ID: fbeea265295ae596d5a3ba563e766be0c4fc26e8

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

Path: workflows/emg-pipeline-v4-assembly-metaSPAdes.cwl

Branch/Commit ID: 7bb76f33bf40b5cd2604001cac46f967a209c47f

https://github.com/farahzkhan/bcbio_test_cwlprov.git

Path: somatic/somatic-workflow/wf-alignment.cwl

Branch/Commit ID: 7c46d5c6ef6501dc0e07a9b740e9de64ffec83f5

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: 581156366f91861bd4dbb5bcb59f67d468b32af3