Explore Workflows

Note: should be updated The original [BioWardrobe's](https://biowardrobe.com) [PubMed ID:26248465](https://www.ncbi.nlm.nih.gov/pubmed/26248465) **RNA-Seq** basic analysis for **strand specific single-read** experiment. A corresponded input [FASTQ](http://maq.sourceforge.net/fastq.shtml) file has to be provided. Current workflow should be used only with the single-read RNA-Seq data. It performs the following steps: 1. Use STAR to align reads from input FASTQ file according to the predefined reference indices; generate unsorted BAM file and alignment statistics file 2. Use fastx_quality_stats to analyze input FASTQ file and generate quality statistics file 3. Use samtools sort to generate coordinate sorted BAM(+BAI) file pair from the unsorted BAM file obtained on the step 1 (after running STAR) 5. Generate BigWig file on the base of sorted BAM file 6. Map input FASTQ file to predefined rRNA reference indices using Bowtie to define the level of rRNA contamination; export resulted statistics to file 7. Calculate isoform expression level for the sorted BAM file and GTF/TAB annotation file using GEEP reads-counting utility; export results to file

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

Path: workflows/rnaseq-se-dutp.cwl

Branch/Commit ID: 954bb2f213d97dfef1cddaf9e830169a92ad0c6b

Single-cell Multiome ATAC and RNA-Seq Alignment Runs Cell Ranger ARC Count to quantifies chromatin accessibility and gene expression from a single-cell Multiome ATAC and RNA-Seq library

https://github.com/Barski-lab/sc-seq-analysis.git

Path: workflows/sc-multiome-align-wf.cwl

Branch/Commit ID: 8614e5d20f5e81dce537216bd340cdbc1067bbc7

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

Path: tests/scatter-valuefrom-inputs-wf1.cwl

Branch/Commit ID: b1d4a69df86350059bd49aa127c02be0c349f7de

https://github.com/anilnatha/sounder-sips-application.git

Path: cwl/l1a_workflow.cwl

Branch/Commit ID: 0d5a368095c85c0fd3598dcfb92a8af5c4305541

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

Path: definitions/subworkflows/gatk_haplotypecaller_iterator.cwl

Branch/Commit ID: 24e5290aec441665c6976ee3ee8ae3574c49c6b5

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

Path: cwltool/schemas/v1.0/v1.0/scatter-valuefrom-wf3.cwl

Branch/Commit ID: 478c2ffc09fb189c4f36ccb82aad945b3db5f9b3

Packed ID: main

https://github.com/chgarth/cwl-openfoam.git

Path: cwl/run-openfoam.cwl

Branch/Commit ID: 2fbe585034c501b7cc8a20ad495d8eeb86b7ba3d

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

Path: cwltool/schemas/v1.0/v1.0/dynresreq-workflow.cwl

Branch/Commit ID: bbe20f54deea92d9c9cd38cb1f23c4423133d3de

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: a0b22644ca178b640fb74849d23b7c631022f0b5

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

Path: workflows/subworkflows/vcf_concat.cwl

Branch/Commit ID: 3441040dfaecba58150c13a95a6a93657b00778a