Explore Workflows

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

Graph Name Retrieved From View
workflow graph STAR-Alignment-PE

This workflow aligns the fastq files using STAR for no spliced genomes

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

Path: workflows/Alignments/star-alignment-nosplice.cwl

Branch/Commit ID: 0e21d424267367d9dae7b980438ddee14c31a445
workflow graph checkm

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

Path: checkm/wf_checkm.cwl

Branch/Commit ID: 8ea3637b0f11eac1ea5599c41d74e00d85fb778d
workflow graph Immunotherapy Workflow

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

Path: definitions/pipelines/immuno.cwl

Branch/Commit ID: 457e101e3fb87e7fd792357afce00ed8ccbfbcdb
workflow graph wf-loadContents.cwl

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

Path: tests/wf-loadContents.cwl

Branch/Commit ID: c7c97715b400ff2194aa29fc211d3401cea3a9bf
workflow graph Cut-n-Run pipeline paired-end

Experimental pipeline for Cut-n-Run analysis. Uses mapping results from the following experiment types: - `chipseq-pe.cwl` - `trim-chipseq-pe.cwl` - `trim-atacseq-pe.cwl` Note, the upstream analyses should not have duplicates removed

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

Path: workflows/trim-chipseq-pe-cut-n-run.cwl

Branch/Commit ID: a68821bf3a9ceadc3b2ffbb535d601d9a645b377
workflow graph js-expr-req-wf.cwl#wf

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

Path: tests/js-expr-req-wf.cwl

Branch/Commit ID: c7c97715b400ff2194aa29fc211d3401cea3a9bf

Packed ID: wf
workflow graph MAnorm2 for Normalizing and Comparing ChIP-Seq/ATAC-Seq Samples

MAnorm2 for Normalizing and Comparing ChIP-Seq/ATAC-Seq Samples

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

Path: workflows/manorm2.cwl

Branch/Commit ID: 93b844a80f4008cc973ea9b5efedaff32a343895
workflow graph tt_blastn_wnode

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

Path: task_types/tt_blastn_wnode.cwl

Branch/Commit ID: 252e7214ac64cb1128881e76743013e61bc7ec38
workflow graph kmer_seq_entry_extract_wnode

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

Path: task_types/tt_kmer_seq_entry_extract_wnode.cwl

Branch/Commit ID: 16e3915d2a357e2a861b30911c832e5ddc0c1784
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: e238d1756f1db35571e84d72e1699e5d1540f10c