Explore Workflows

### Devel version of QuantSeq 3' FWD, FWD-UMI or REV for single-read mRNA-Seq data

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

Path: workflows/trim-quantseq-mrnaseq-se-strand-specific.cwl

Branch/Commit ID: 60854b5d299df91e135e05d02f4be61f6a310fbc

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: 104059e07a2964673e21d371763e33c0afeb2d03

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

Path: cwltool/schemas/v1.0/v1.0/count-lines10-wf.cwl

Branch/Commit ID: fc6ca8b1498926f705dcfde7ab0a365bd09a9675

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

Path: tests/wf/count-lines1-wf.cwl

Branch/Commit ID: 20d664eff23e59aa57908345bfdb1ceeab3438f2

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

Path: metadata/indices-header.cwl

Branch/Commit ID: 6db250e2f71fbf4f0b1b35a79b588f7cd7a39f0f

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

Path: cwltool/schemas/v1.0/v1.0/env-wf1.cwl

Branch/Commit ID: fc6ca8b1498926f705dcfde7ab0a365bd09a9675

https://github.com/HrishiDhondge/CroMaSt.git

Path: CroMaSt.cwl

Branch/Commit ID: 9f3832867eab6c7a6363f8ca594a4bcf2ff7e96f

Workflow maps FASTQ files from `fastq_files` input into reference genome `reference_star_indices_folder` and insilico generated `insilico_star_indices_folder` genome (concatenated genome for both `strain1` and `strain2` strains). For both genomes STAR is run with `outFilterMultimapNmax` parameter set to 1 to discard all of the multimapped reads. For insilico genome SAM file is generated. Then it's splitted into two SAM files based on strain names and then sorted by coordinates into the BAM format. For reference genome output BAM file from STAR slignment is also coordinate sorted.

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

Path: subworkflows/allele-alignreads-se-pe.cwl

Branch/Commit ID: 6bf56698c6fe6e781723dea32bc922b91ef49cf3

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

Path: cwltool/schemas/v1.0/v1.0/scatter-wf4.cwl

Branch/Commit ID: cb81b22abc52838823da9945f04d06739ab32fda

Packed ID: main

https://github.com/ProteinsWebTeam/ebi-metagenomics-cwl.git

Path: workflows/emg-pipeline-v3.cwl

Branch/Commit ID: 5dc7c5ca618a248a99bd4bf5f3042cdb21947193