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Graph Name Retrieved From View
workflow graph HBA_calibrator.cwl

https://git.astron.nl/RD/LINC.git

Path: workflows/HBA_calibrator.cwl

Branch/Commit ID: 1e91001f761abbddeb2c9f4528ed4cec41d113f3
workflow graph Pipeline for evaluating differential expression of genes across datasets

https://github.com/hubmapconsortium/rna-data-products.git

Path: pipeline.cwl

Branch/Commit ID: 8fa4b879f070f698816d222024cea5c32be1c469
workflow graph revsort.cwl

Reverse the lines in a document, then sort those lines.

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

Path: tests/wf/revsort.cwl

Branch/Commit ID: 83038feb2a6fc3bab952e1ecc2a11bfbc8c557b4
workflow graph scRNA-seq pipeline using Salmon and Alevin

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

Path: pipeline.cwl

Branch/Commit ID: 69da10ae891ce1ea821a59b9d0f33c9b931c88a9
workflow graph scRNA-seq pipeline using Salmon and Alevin

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

Path: pipeline.cwl

Branch/Commit ID: ada7831c1ec6ce8ff529d25130e3c89feff25874
workflow graph bulk scRNA-seq pipeline using Salmon

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

Path: bulk-pipeline.cwl

Branch/Commit ID: d71be688b76ed09a64cf5d8cf36182fe35cce6b3
workflow graph kmer_cache_retrieve

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

Path: task_types/tt_kmer_cache_retrieve.cwl

Branch/Commit ID: 550682d2fe3348161eab1b8612e48a59af4ac6a5
workflow graph fillout_workflow.cwl

Workflow to run GetBaseCountsMultiSample fillout on a number of bam files with a single maf file

https://github.com/mskcc/pluto-cwl.git

Path: cwl/fillout_workflow.cwl

Branch/Commit ID: 5cad957fec135aa55ca8d588372db0557ca1cad5
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: 261c0232a7a40880f2480b811ed2d7e89c463869
workflow graph count-lines6-wf.cwl

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

Path: tests/count-lines6-wf.cwl

Branch/Commit ID: 707ebcd2173889604459c5f4ffb55173c508abb3