Explore Workflows

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

Path: cwltool/schemas/v1.0/v1.0/step-valuefrom-wf.cwl

Branch/Commit ID: 4700fbee9a5a3271eef8bc9ee595619d0720431b

Simulation steps pipeline

https://github.com/ILIAD-ocean-twin/application_package.git

Path: workflow_in_workflow/original.cwl

Branch/Commit ID: 9a0db98839bbc655e12d49f56c61deecd77ff14c

Packed ID: main_pipeline

Cell Ranger Count Gene Expression =================================

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

Path: workflows/single-cell-preprocess-cellranger.cwl

Branch/Commit ID: bf80c9339d81a78aefb8de661bff998ed86e836e

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: 29bf638904709cfbf10908adcd51ba4886ace94a

Copy fasta_file file to the folder and run run bismark_genome_preparation script to prepare indices for Bismark Methylation Analysis. Bowtie2 aligner is used by default. The name of the output indices folder is equal to the genome input.

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

Path: workflows/bismark-index.cwl

Branch/Commit ID: ee66d03be8a7fd61367db40c37a973ff55ece4da

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

Path: definitions/subworkflows/align_sort_markdup.cwl

Branch/Commit ID: 39ac49f5d080bbb6bfa97246f46a5b621254f622

Option 2: Deblur from https://docs.qiime2.org/2018.4/tutorials/moving-pictures/

https://github.com/Duke-GCB/bespin-cwl.git

Path: packed/qiime2-step2-deblur.cwl

Branch/Commit ID: 777dbcd05b5d115371dcda6d54ebaf75dae8afb8

Packed ID: qiime2-03-deblur.cwl

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

Path: progs/protein_extract.cwl

Branch/Commit ID: 424a01693259a75641dc249d553235aa38a6ce23

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

Path: task_types/tt_hmmsearch_wnode.cwl

Branch/Commit ID: 424a01693259a75641dc249d553235aa38a6ce23

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

Path: testdata/count-lines7-wf_v1_1.cwl

Branch/Commit ID: b76b039edb62dea76c43f173848cdc57e4b4aab7