Explore Workflows

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

Graph Name Retrieved From View
workflow graph conflict.cwl#main

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

Path: tests/wf/conflict.cwl

Branch/Commit ID: e8b3565a008d95859fc44227987a54e6a53a8c29

Packed ID: main
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: 449bc7e45bb02316d040f73838ef18359e770268
workflow graph align_merge_sas

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

Path: task_types/tt_align_merge_sas.cwl

Branch/Commit ID: 77a9fa25b89ce73582a1ce6ba75fa6d2537fb8e8
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: 195b4ab487c939eb32a55d9f78bc1befd100caae
workflow graph step-valuefrom-wf.cwl

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

Path: cwltool/schemas/v1.0/v1.0/step-valuefrom-wf.cwl

Branch/Commit ID: a70a83fe14a100cd16e2402ec17b2904f5eeb17d
workflow graph EMG assembly for paired end Illumina

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

Path: workflows/emg-assembly.cwl

Branch/Commit ID: 30397448563d06c342b25a3603c97b6fff7ba7d3
workflow graph chip-seq-alignment.cwl

https://github.com/ncbi/cwl-ngs-workflows-cbb.git

Path: workflows/ChIP-Seq/chip-seq-alignment.cwl

Branch/Commit ID: 265440c63ab75d2451c90bcd116e725626e9a608
workflow graph cache_asnb_entries

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

Path: task_types/tt_cache_asnb_entries.cwl

Branch/Commit ID: 5331b0836aa7c451d759ef39dc2062000ac21a47
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: e0a30aa1ad516dd2ec0e9ce006428964b840daf4
workflow graph TransDecoder 2 step workflow, running TransDecoder.LongOrfs (step 1) followed by TransDecoder.Predict (step2)

https://github.com/stain/workflow-is-cwl.git

Path: workflows/TransDecoder-v5-wf-2steps.cwl

Branch/Commit ID: b1e88a8c2f6f07d236193d3e89dc2d724700780a