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: 2f0db4b3c515f91c5cfda19c78cf90d339390986

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

Path: v1.0/v1.0/count-lines9-wf-noET.cwl

Branch/Commit ID: e67f19d8a713759d761ecad050966d1eb043b85c

https://github.com/genome/cancer-genomics-workflow.git

Path: docm/germline_workflow.cwl

Branch/Commit ID: ab3cc1f460146c60d7de417508f0c1ea70506e6a

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

Path: definitions/subworkflows/fastq_to_bqsr.cwl

Branch/Commit ID: 00df82a529a58d362158110581e1daa28b4d7ecb

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

Path: definitions/subworkflows/varscan_germline.cwl

Branch/Commit ID: f0cdc773e31e4aa116838e8aba4954c31bd3d68b

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

Path: definitions/pipelines/alignment_exome.cwl

Branch/Commit ID: 93656ed6582073e434eab168c610625a835dce37

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

Path: definitions/pipelines/immuno.cwl

Branch/Commit ID: f0cdc773e31e4aa116838e8aba4954c31bd3d68b

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

Path: task_types/tt_bact_get_kmer_reference.cwl

Branch/Commit ID: 5463361069e263ad6455858e054c1337b1d9e752

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

Path: definitions/subworkflows/single_cell_rnaseq.cwl

Branch/Commit ID: 742dbafb5fb103d8578f48a0576c14dd8dae3b2a

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: 2f0db4b3c515f91c5cfda19c78cf90d339390986