Explore Workflows

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Graph Name Retrieved From View
workflow graph fp_filter workflow

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

Path: definitions/subworkflows/fp_filter.cwl

Branch/Commit ID: fbeea265295ae596d5a3ba563e766be0c4fc26e8
workflow graph GSEApy - Gene Set Enrichment Analysis in Python

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: 480e99a4bb3046e0565113d9dca294e0895d3b0c
workflow graph RNA-Seq alignment and transcript/gene abundance workflow

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

Path: definitions/pipelines/rnaseq.cwl

Branch/Commit ID: fbeea265295ae596d5a3ba563e766be0c4fc26e8
workflow graph blastp_wnode_struct

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

Path: task_types/tt_blastp_wnode_struct.cwl

Branch/Commit ID: 61eaea2f746c8a1fc2a2b731056b068e28ca4e20
workflow graph assemble.cwl

Assemble a set of reads using SKESA

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

Path: assemble.cwl

Branch/Commit ID: 17bae57a1f00f5c6db8f3a82d86262f12b8153cf
workflow graph wgs alignment and germline variant detection

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

Path: definitions/pipelines/germline_wgs.cwl

Branch/Commit ID: fbeea265295ae596d5a3ba563e766be0c4fc26e8
workflow graph gk-parse-current.cwl

https://github.com/vdikan/cwl-gk-thermal.git

Path: cwl/gk-parse-current.cwl

Branch/Commit ID: 48f4b8266a5a199cfc100d45148996f10a092f3b
workflow graph Unaligned bam to sorted, markduped bam

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

Path: definitions/subworkflows/align_sort_markdup.cwl

Branch/Commit ID: ef7f3345b352319ec22dffba26c79df033b141f9
workflow graph somatic_exome: exome alignment and somatic variant detection

somatic_exome is designed to perform processing of mutant/wildtype H.sapiens exome sequencing data. It features BQSR corrected alignments, 4 caller variant detection, and vep style annotations. Structural variants are detected via manta and cnvkit. In addition QC metrics are run, including somalier concordance metrics. example input file = analysis_workflows/example_data/somatic_exome.yaml

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

Path: definitions/pipelines/somatic_exome.cwl

Branch/Commit ID: fbeea265295ae596d5a3ba563e766be0c4fc26e8
workflow graph bam to trimmed fastqs and HISAT alignments

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

Path: definitions/subworkflows/bam_to_trimmed_fastq_and_hisat_alignments.cwl

Branch/Commit ID: 6949082038c1ad36d6e9848b97a2537aef2d3805