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Graph Name Retrieved From View
workflow graph rnaseq-alignment-circRNA-multiple-samples

This workflow aligns multiple samples using STAR for paired-end samples to be used in circRNA pipeline

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

Path: workflows/Alignments/star-alignment-circRNA-multiple-samples.cwl

Branch/Commit ID: 1b1cb5bbbe53a2dd5d7de7cdbff19c1bdbe23a49
workflow graph examplePipeline.cwl

https://github.com/InsightSoftwareConsortium/GetYourBrainStraight.git

Path: HCK01_2022_Virtual/Tutorials/GetYourBrainPipelined/CWL-Demo/examplePipeline.cwl

Branch/Commit ID: d6bc3672ac4047f52eb4b1695e66930d2072b4dc
workflow graph alignment workflow

https://github.com/griffithlab/pmbio.org.git

Path: assets/CWL/workflow.cwl

Branch/Commit ID: 1fe1f526a10bb2f363dcc7b36d7ce6fa8031b68e
workflow graph Bacterial Annotation, pass 1, genemark training, by HMMs (first pass)

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

Path: bacterial_annot/wf_bacterial_annot_pass1.cwl

Branch/Commit ID: c009eeba7379efbbd37b8d5013a83f161f06939b
workflow graph scatter-wf1.cwl

https://github.com/common-workflow-language/cwl-v1.1.git

Path: tests/scatter-wf1.cwl

Branch/Commit ID: 368b562a1449e8cd39ae8b7f05926b2bfb9b22df
workflow graph cond-wf-001.cwl

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

Path: tests/conditionals/cond-wf-001.cwl

Branch/Commit ID: 57baec040c99d7edef8242ef51b5470b1c82d733
workflow graph steplevel-resreq.cwl

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

Path: tests/steplevel-resreq.cwl

Branch/Commit ID: 57baec040c99d7edef8242ef51b5470b1c82d733
workflow graph Run tRNAScan

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

Path: bacterial_trna/wf_trnascan.cwl

Branch/Commit ID: c009eeba7379efbbd37b8d5013a83f161f06939b
workflow graph beast-2step-workflow.cwl

https://github.com/GusEllerm/CWL_workflows.git

Path: workflows/BEAST_examples/beast-2step-workflow.cwl

Branch/Commit ID: 3b6b802c616609bdd39d8fafb8a14dd82e5cb17a
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: 664de58d95728edbf7d369d894f9037ebe2475fa