Explore Workflows

Slightly changed 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. An additional steps were added to map data to mitochondrial chromosome only and then merge the output. Experiment files in [FASTQ](http://maq.sourceforge.net/fastq.shtml) format either compressed or not can be used. Current workflow should be used only with single-read strand specific RNA-Seq data. It performs the following steps: 1. `STAR` to align reads from input FASTQ file according to the predefined reference indices; generate unsorted BAM file and alignment statistics file 2. `fastx_quality_stats` to analyze input FASTQ file and generate quality statistics file 3. `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-mitochondrial.cwl

Branch/Commit ID: e9a24699d8b5ffe64412b1ba0af8448c281b223a

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: 3fc68366adb179927af5528c27b153abaf94494d

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

Path: cwltool/schemas/v1.0/v1.0/scatter-valuefrom-wf2.cwl

Branch/Commit ID: e6c2d955a448225f026a04130443d13661844440

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

Path: definitions/subworkflows/downsampled_alignment.cwl

Branch/Commit ID: a3e26136043c03192c38c335316d2d36e3e67478

https://github.com/EBI-Metagenomics/workflow-is-cwl.git

Path: workflows/TranscriptsAnnotation-wf.cwl

Branch/Commit ID: 11cba46ea263315d4d66e86819718fa157e927b1

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

Path: tests/wf/js_output_workflow.cwl

Branch/Commit ID: 31aa094dce60cbb176229d6b918bfd5ae09c0390

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

Path: definitions/pipelines/wgs.cwl

Branch/Commit ID: ae79bc51e8b502164dbe74ea3b068d6d4d36a1f8

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

Path: tests/timelimit3-wf.cwl

Branch/Commit ID: 86c46cb397de029e4c91f02cca40fa2b54d22f37

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

Path: tests/wf/conflict.cwl

Branch/Commit ID: 227f35a5ed50c423afba2353871950aa61d58872

Packed ID: main

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: d6f58c383d0676269afb519399061191a1144a6a