Explore Workflows

View already parsed workflows here or click here to add your own

Graph Name Retrieved From View
workflow graph cluster_blastp_wnode and gpx_qdump combined

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

Path: task_types/tt_cluster_and_qdump.cwl

Branch/Commit ID: 122aba2dafbb63241413c82b725b877c04523aaf
workflow graph umi molecular alignment workflow

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

Path: definitions/subworkflows/molecular_qc.cwl

Branch/Commit ID: c6bbd4cdd612b3b5cc6e9000df4800c21e192bf5
workflow graph QuantSeq 3' FWD, FWD-UMI or REV for single-read mRNA-Seq data

### Devel version of QuantSeq 3' FWD, FWD-UMI or REV for single-read mRNA-Seq data

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

Path: workflows/trim-quantseq-mrnaseq-se-strand-specific.cwl

Branch/Commit ID: 09267e79fd867aa68a219c69e6db7d8e2e877be2
workflow graph ST610109.cwl

https://github.com/Marco-Salvi/dtc61.git

Path: ST610109.cwl

Branch/Commit ID: 668634ce0f044d6e9ccdbd839b015d384cbfb0ab
workflow graph ST610106.cwl

https://github.com/Marco-Salvi/dtc61.git

Path: ST610106.cwl

Branch/Commit ID: 668634ce0f044d6e9ccdbd839b015d384cbfb0ab
workflow graph cache_asnb_entries

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

Path: task_types/tt_cache_asnb_entries.cwl

Branch/Commit ID: c17cac4c046f8ba2b8574a121c44a72d2e6b27e6
workflow graph exome alignment and germline variant detection

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

Path: germline_exome_workflow.cwl

Branch/Commit ID: 6eb7d35ad46207f4ff49e84106b717e17331eb4b
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: 5561f7ee11dd74848680351411a19aa87b13d27b
workflow graph exome alignment and germline variant detection

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

Path: definitions/pipelines/germline_exome.cwl

Branch/Commit ID: 3034168d652bfa930ba09af20e473a4564a8010d
workflow graph tt_kmer_compare_wnode

Pairwise comparison

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

Path: task_types/tt_kmer_compare_wnode.cwl

Branch/Commit ID: 122aba2dafbb63241413c82b725b877c04523aaf