Explore Workflows

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

Graph Name Retrieved From View
workflow graph env-wf1.cwl

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

Path: cwltool/schemas/v1.0/v1.0/env-wf1.cwl

Branch/Commit ID: 3e9bca4e006eae7e9febd76eb9b8292702eba2cb
workflow graph harmonization_bwa_mem_no_trim.cwl

https://github.com/uc-cdis/genomel_pipelines.git

Path: genomel/cwl/workflows/harmonization/harmonization_bwa_mem_no_trim.cwl

Branch/Commit ID: 3c62f1ddf8a54601da3cfc16c37d0cb0d6a2ea28
workflow graph GATKBaseRecalBQSRWorkflow_4_1_3.cwl

https://github.com/PMCC-BioinformaticsCore/janis-pipelines.git

Path: janis_pipelines/wgs_somatic/cwl/tools/GATKBaseRecalBQSRWorkflow_4_1_3.cwl

Branch/Commit ID: ccca639fe0b3a8104ff9fcfa285f1134706032b8
workflow graph qc-assembled.workflow.cwl

https://github.com/MG-RAST/pipeline.git

Path: CWL/Workflows/qc-assembled.workflow.cwl

Branch/Commit ID: 6c5d0068bdb4f19a36a653c39964aefb9e5a7b1b
workflow graph Varscan Workflow

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

Path: definitions/subworkflows/varscan_pre_and_post_processing.cwl

Branch/Commit ID: a7838a5ca72b25db5c2af20a15f34303a839980e
workflow graph bact_get_kmer_reference

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

Path: task_types/tt_bact_get_kmer_reference.cwl

Branch/Commit ID: bba6c580ab88e077f6aa2c2ee7c73159f3f9156e
workflow graph mergeAndMarkBams_4_1_3.cwl

https://github.com/PMCC-BioinformaticsCore/janis-pipelines.git

Path: janis_pipelines/wgs_somatic/cwl/tools/mergeAndMarkBams_4_1_3.cwl

Branch/Commit ID: ccca639fe0b3a8104ff9fcfa285f1134706032b8
workflow graph phase VCF

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

Path: definitions/subworkflows/phase_vcf.cwl

Branch/Commit ID: ae75b938e6e8ae777a55686bbacad824b3c6788c
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: 1a46cb0e8f973481fe5ae3ae6188a41622c8532e
workflow graph count-lines1-wf.cwl

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

Path: tests/count-lines1-wf.cwl

Branch/Commit ID: a5073143db4155e05df8d2e7eb59d9e62acd65a5