Explore Workflows

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

Graph Name Retrieved From View
workflow graph env-wf2.cwl

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

Path: cwltool/schemas/v1.0/v1.0/env-wf2.cwl

Branch/Commit ID: cb81b22abc52838823da9945f04d06739ab32fda
workflow graph count-lines5-wf.cwl

https://github.com/common-workflow-language/cwl-v1.2.git

Path: tests/count-lines5-wf.cwl

Branch/Commit ID: 5f27e234b4ca88ed1280dedf9e3391a01de12912
workflow graph count-lines5-wf.cwl

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

Path: cwltool/schemas/v1.0/v1.0/count-lines5-wf.cwl

Branch/Commit ID: cb81b22abc52838823da9945f04d06739ab32fda
workflow graph workflow_demultiplexing.cwl

https://git.wur.nl/unlock/cwl.git

Path: cwl/workflows/workflow_demultiplexing.cwl

Branch/Commit ID: b9097b82e6ab6f2c9496013ce4dd6877092956a0
workflow graph workflow_mock_ngtax.cwl

https://git.wur.nl/unlock/cwl.git

Path: cwl/workflows/workflow_mock_ngtax.cwl

Branch/Commit ID: b9097b82e6ab6f2c9496013ce4dd6877092956a0
workflow graph indexing_bed

https://gitlab.bsc.es/lrodrig1/structuralvariants_poc.git

Path: structuralvariants/cwl/abstract_operations/subworkflows/indexing_bed.cwl

Branch/Commit ID: 27011e882ea07bfcac23faec299341ec6215312b
workflow graph DESeq - differential gene expression analysis

Differential gene expression analysis ===================================== Differential gene expression analysis based on the negative binomial distribution Estimate variance-mean dependence in count data from high-throughput sequencing assays and test for differential expression based on a model using the negative binomial distribution. DESeq1 ------ High-throughput sequencing assays such as RNA-Seq, ChIP-Seq or barcode counting provide quantitative readouts in the form of count data. To infer differential signal in such data correctly and with good statistical power, estimation of data variability throughout the dynamic range and a suitable error model are required. Simon Anders and Wolfgang Huber propose a method based on the negative binomial distribution, with variance and mean linked by local regression and present an implementation, [DESeq](http://bioconductor.org/packages/release/bioc/html/DESeq.html), as an R/Bioconductor package DESeq2 ------ In comparative high-throughput sequencing assays, a fundamental task is the analysis of count data, such as read counts per gene in RNA-seq, for evidence of systematic changes across experimental conditions. Small replicate numbers, discreteness, large dynamic range and the presence of outliers require a suitable statistical approach. [DESeq2](http://www.bioconductor.org/packages/release/bioc/html/DESeq2.html), a method for differential analysis of count data, using shrinkage estimation for dispersions and fold changes to improve stability and interpretability of estimates. This enables a more quantitative analysis focused on the strength rather than the mere presence of differential expression.

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

Path: workflows/deseq.cwl

Branch/Commit ID: 4360fb2e778ecee42e5f78f83b78c65ab3a2b1df
workflow graph conflict.cwl#main

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

Path: tests/wf/conflict.cwl

Branch/Commit ID: aec33fcfa3459a90cbba8c88ebb991be94d21429

Packed ID: main
workflow graph samtools_view_sam2bam

https://gitlab.bsc.es/lrodrig1/structuralvariants_poc.git

Path: structuralvariants/cwl/subworkflows/samtools_view_sam2bam.cwl

Branch/Commit ID: a4a3547b9790e99a58424a0dfcb4e467a7691d6a
workflow graph cnv_manta

CNV Manta calling

https://gitlab.bsc.es/lrodrig1/structuralvariants_poc.git

Path: structuralvariants/cwl/subworkflows/cnv_manta.cwl

Branch/Commit ID: a4a3547b9790e99a58424a0dfcb4e467a7691d6a