Explore Workflows

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: 3b2e0de49d9ee6fd9a8c9580b6a02d0f7e4c8f7c

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: a839eb6390974089e1a558c49fc07b4c66c50767

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

Path: definitions/pipelines/detect_variants_wgs.cwl

Branch/Commit ID: 3034168d652bfa930ba09af20e473a4564a8010d

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

Path: bacterial_orthology/wf_bacterial_orthology.cwl

Branch/Commit ID: 91181df8d9ef8eed9d8f40db707b9a4376fecaf5

Workflow to find hotspot VAFs from duplex (for Tumor sample) and unfiltered (for Normal sample) pileups. These inputs are all required to be sorted in the same order: sample_ids patient_ids sample_classes unfiltered_pileups duplex_pileups

https://github.com/mskcc/ACCESS-Pipeline.git

Path: workflows/subworkflows/find_hotspots_in_normals.cwl

Branch/Commit ID: b0f226a9ac5152f3afe0d38c8cd54aa25b8b01cf

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

Path: definitions/subworkflows/cellranger_mkfastq_and_count.cwl

Branch/Commit ID: 1560e7817fdb71d58aca7f98aba68809d840ade1

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

Path: task_types/tt_align_merge_sas.cwl

Branch/Commit ID: ce433f771ebf5677c9f40858e2ae91b1a7e75d30

https://github.com/biosciencedbc/jga-analysis.git

Path: per-sample/Workflows/per-sample.cwl

Branch/Commit ID: 651b97b982a48cdb4e5f36edc0b1f38b25b30c10

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

Path: definitions/subworkflows/docm_cle.cwl

Branch/Commit ID: d57c2af01a3cb6016e5a264f60641eafd2e5aa05

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

Path: definitions/subworkflows/umi_alignment.cwl

Branch/Commit ID: d57c2af01a3cb6016e5a264f60641eafd2e5aa05