Explore Workflows

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

Path: workflows/sanbi_cwltutorial/revcomp/revcomp_with_rename.cwl

Branch/Commit ID: 4c325f63bb179a7f6c1b693c886fafbc41d5b933

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

Path: definitions/subworkflows/bam_to_bqsr_no_dup_marking.cwl

Branch/Commit ID: 735be84cdea041fcc8bd8cbe5728b29ca3586a21

Seurat Cluster ============== Runs filtering, integration, and clustering analyses for Cell Ranger Count Gene Expression or Cell Ranger Aggregate experiments.

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

Path: workflows/seurat-cluster.cwl

Branch/Commit ID: ebbf23764ede324cabc064bd50647c1f643726fa

Comvert BAM to BEDPE and compress the output

https://github.com/ncbi/cwl-ngs-workflows-cbb.git

Path: workflows/File-formats/bamtobedpe-gzip.cwl

Branch/Commit ID: 793e327acc1d159ff601043ee88651fca62350dd

# Differential gene expression analysis This differential gene expression (DGE) analysis takes as input samples from two experimental conditions that have been processed with a spike-in normalized RNA-Seq workflow (see list of \"Upstream workflows\" at top of file). The size factor estimation and application for normalization is disabled in this version of the DESeq workflow, otherwise all other aspects are the same. DESeq estimates variance-mean dependence in count data from high-throughput sequencing assays, then tests for DGE based on a model which assumes a negative binomial distribution of gene expression (aligned read count per gene). ### Experimental Setup and Results Interpretation The workflow design uses as its fold change (FC) calculation: condition 1 (c1, e.g. treatment) over condition 2 (c2, e.g. control). In other words: `FC == (c1/c2)` Therefore: - if FC<1 the log2(FC) is <0 (negative), meaning expression in condition1<condition2 (gene is downregulated in c1) - if FC>1 the log2(FC) is >0 (positive), meaning expression in condition1>condition2 (gene is upregulated in c1) In other words, if you have input TREATMENT samples as condition 1, and CONTROL samples as condition 2, a positive L2FC for a gene indicates that expression of the gene in TREATMENT is greater (or upregulated) compared to CONTROL. Next, threshold the p-adjusted values with your FDR (false discovery rate) cutoff to determine if the change may be considered significant or not. It is important to note when DESeq1 or DESeq2 is used in our DGE analysis workflow. If a user inputs only a single sample per condition DESeq1 is used for calculating DGE. In this experimental setup, there are no repeated measurements per gene per condition, therefore biological variability in each condition cannot be captured so the output p-values are assumed to be purely \"technical\". On the other hand, if >1 sample(s) are input per condition DESeq2 is used. In this case, biological variability per gene within each condition is available to be incorporated into the model, and resulting p-values are assumed to be \"biological\". Additionally, DESeq2 fold change is \"shrunk\" to account for sample variability, and as Michael Love (DESeq maintainer) puts it, \"it looks at the largest fold changes that are not due to low counts and uses these to inform a prior distribution. So the large fold changes from genes with lots of statistical information are not shrunk, while the imprecise fold changes are shrunk. This allows you to compare all estimated LFC across experiments, for example, which is not really feasible without the use of a prior\". In either case, the null hypothesis (H0) tested is that there are no significantly differentially expressed genes between conditions, therefore a smaller p-value indicates a lower probability of the H0 occurring by random chance and therefore, below a certain threshold (traditionally <0.05), H0 should be rejected. Additionally, due to the many thousands of independent hypotheses being tested (each gene representing an independent test), the p-values attained by the Wald test are adjusted using the Benjamini and Hochberg method by default. These \"padj\" values should be used for determination of significance (a reasonable value here would be <0.10, i.e. below a 10% FDR). Further Analysis: Output from the DESeq workflow may be used as input to the GSEA (Gene Set Enrichment Analysis) workflow for identifying enriched marker gene sets between conditions. ### 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://www.bioconductor.org/packages/3.8/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. ### __References__ - Anders S, Huber W (2010). “Differential expression analysis for sequence count data.” Genome Biology, 11, R106. doi: 10.1186/gb-2010-11-10-r106, http://genomebiology.com/2010/11/10/R106/. - Love MI, Huber W, Anders S (2014). “Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2.” Genome Biology, 15, 550. doi: 10.1186/s13059-014-0550-8.

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

Path: workflows/deseq-for-spikein.cwl

Branch/Commit ID: 261c0232a7a40880f2480b811ed2d7e89c463869

https://github.com/Marco-Salvi/dtc61.git

Path: WF6101.cwl

Branch/Commit ID: 8c2621b1d757d890cbbd0dfe5b363ad014f0a684

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

Path: tests/conflict-wf.cwl

Branch/Commit ID: 664835e83eb5e57eee18a04ce7b05fb9d70d77b7

Packed ID: collision

The original [BioWardrobe's](https://biowardrobe.com) [PubMed ID:26248465](https://www.ncbi.nlm.nih.gov/pubmed/26248465) **RNA-Seq** basic analysis for a **pair-end** 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-end RNA-Seq data. It performs the following steps: 1. Trim adapters from input FASTQ files 2. Use STAR to align reads from input FASTQ files according to the predefined reference indices; generate unsorted BAM file and alignment statistics file 3. Use fastx_quality_stats to analyze input FASTQ files and generate quality statistics files 4. 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 files 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/trim-rnaseq-pe.cwl

Branch/Commit ID: 7030da528559c7106d156284e50ff0ecedab0c4e

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

Path: cwltool/schemas/v1.0/v1.0/scatter-valuefrom-wf1.cwl

Branch/Commit ID: 5c7799a145595323d0a8628be1fe0e24985e793a

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

Path: definitions/subworkflows/single_sample_sv_callers.cwl

Branch/Commit ID: 7b4b489474473c3d2d992a838b89632c2b97dc2c