Explore Workflows

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: 4f48ee6f8665a34cdf96e89c012ee807f80c7a3d

Devel version of Single-Cell Advanced Cell Ranger Pipeline (SoupX) =================================================================

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

Path: workflows/soupx.cwl

Branch/Commit ID: 7ced5a5259dbd8b3fc64456beaeffd44f4a24081

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

Path: testdata/count-lines7-single-source-wf_v1_0.cwl

Branch/Commit ID: c1875d54dedc41b1d2fa08634dcf1caa8f1bc631

The entire test should take ~24 seconds. Test that the 20 second time limit applies to each step individually (so 1st step has 20 seconds and the 2nd step has 20 seconds). So this 20 second time limit should not cause the workflow to fail. The timing on this test was updated from shorter values to accommodate the startup time of certain container runners, the previous timelimit of 5 seconds was too short, which is why it is now 20 seconds.

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

Path: tests/timelimit2-wf.cwl

Branch/Commit ID: c7c97715b400ff2194aa29fc211d3401cea3a9bf

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

Path: tests/conditionals/cond-wf-002_nojs.cwl

Branch/Commit ID: e62f99dd79d6cb9c157cceb458f74200da84f6e9

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

Path: testdata/count-lines7-single-source-wf_v1_1.cwl

Branch/Commit ID: 0ad6983898f0d9001fe0f416f97c4d8b940e384a

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

Path: tests/conditionals/cond-wf-006_nojs.cwl

Branch/Commit ID: e62f99dd79d6cb9c157cceb458f74200da84f6e9

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

Path: definitions/subworkflows/merge_svs.cwl

Branch/Commit ID: ec45fad68ca10fb64d5c58e704991b146dc31d28

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

Path: task_types/tt_kmer_cache_store.cwl

Branch/Commit ID: 2afb5ebafd1353ba063cc74ee9a7eaf347afce5c

Generates a FASTA file with the DNA sequences for all transcripts in a GFF file and builds kallisto index

https://github.com/Barski-lab/workflows.git

Path: tools/genome-kallisto-index.cwl

Branch/Commit ID: a84cefded73e7c864ee2b6c7ab0604a0397462ec