Explore Workflows

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

Graph Name Retrieved From View
workflow graph Running cellranger count and lineage inference

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

Path: definitions/subworkflows/single_cell_rnaseq.cwl

Branch/Commit ID: fbeea265295ae596d5a3ba563e766be0c4fc26e8
workflow graph SoupX (workflow) - an R package for the estimation and removal of cell free mRNA contamination

Wrapped in a workflow SoupX tool for easy access to Cell Ranger pipeline compressed outputs.

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

Path: tools/soupx-subworkflow.cwl

Branch/Commit ID: 480e99a4bb3046e0565113d9dca294e0895d3b0c
workflow graph gk-load-code-pseudos.cwl

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

Path: cwl/gk-load-code-pseudos.cwl

Branch/Commit ID: 02e5bf961627e54ba18c4f832f4e3f463a9adc42
workflow graph HS Metrics workflow

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

Path: definitions/subworkflows/hs_metrics.cwl

Branch/Commit ID: 788bdc99c1d5b6ee7c431c3c011eb30d385c1370
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: dc6014dc7c1f160ec4ae3d5a57388a6dceaacbc5
workflow graph Detect DoCM variants

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

Path: definitions/subworkflows/docm_germline.cwl

Branch/Commit ID: fbeea265295ae596d5a3ba563e766be0c4fc26e8
workflow graph Create Genomic Collection for Bacterial Pipeline, ASN.1 input

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

Path: genomic_source/wf_genomic_source_asn.cwl

Branch/Commit ID: 2801ce53744a085580a8de91cd007c45146b51e8
workflow graph scRNA-seq pipeline using Salmon and Alevin

https://github.com/hubmapconsortium/salmon-rnaseq.git

Path: pipeline.cwl

Branch/Commit ID: 2dc339d7d922610af97fde5d6d25798d0bdd5441
workflow graph umi per-lane alignment subworkflow

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

Path: definitions/subworkflows/umi_alignment.cwl

Branch/Commit ID: 061d3a2fbcd8a1c39c0b38c549e528deb24a9d54
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: 9e3c3e65c19873cd1ed3cf7cc3b94ebc75ae0cc5