Explore Workflows

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

Path: cwltool/schemas/v1.0/v1.0/conflict-wf.cwl

Branch/Commit ID: 49cd284a8fc7884de763573075d3e1d6a4c1ffdd

Packed ID: collision

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: 30031ca5e69cec603c4733681de54dc7bffa20a3

Single-Cell RNA-Seq Filtering Analysis Removes low-quality cells from the outputs of the “Cell Ranger Count (RNA)”, “Cell Ranger Count (RNA+VDJ)”, and “Cell Ranger Aggregate (RNA, RNA+VDJ)” pipelines. The results of this workflow are used in the “Single-Cell RNA-Seq Dimensionality Reduction Analysis” pipeline.

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

Path: workflows/sc-rna-filter.cwl

Branch/Commit ID: 30031ca5e69cec603c4733681de54dc7bffa20a3

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

Path: cwltool/schemas/v1.0/v1.0/step-valuefrom-wf.cwl

Branch/Commit ID: 6300a49ec29be956ab451311fe9781522f461aee

What is MAnorm? -------------- MAnorm is a robust model for quantitative comparison of ChIP-Seq data sets of TFs (transcription factors) or epigenetic modifications and you can use it for: * Normalization of two ChIP-seq samples * Quantitative comparison (differential analysis) of two ChIP-seq samples * Evaluating the overlap enrichment of the protein binding sites(peaks) * Elucidating underlying mechanisms of cell-type specific gene regulation How MAnorm works? ---------------- MAnorm uses common peaks of two samples as a reference to build the rescaling model for normalization, which is based on the empirical assumption that if a chromatin-associated protein has a substantial number of peaks shared in two conditions, the binding at these common regions will tend to be determined by similar mechanisms, and thus should exhibit similar global binding intensities across samples. The observed differences on common peaks are presumed to reflect the scaling relationship of ChIP-Seq signals between two samples, which can be applied to all peaks. What do the inputs mean? ---------------- ### General **Experiment short name/Alias** * short name for you experiment to identify among the others **ChIP-Seq SE sample 1** * previously analyzed ChIP-Seq single-read experiment to be used as Sample 1 **ChIP-Seq SE sample 2** * previously analyzed ChIP-Seq single-read experiment to be used as Sample 2 **Genome** * Reference genome to be used for gene assigning ### Advanced **Reads shift size for sample 1** * This value is used to shift reads towards 3' direction to determine the precise binding site. Set as half of the fragment length. Default 100 **Reads shift size for sample 2** * This value is used to shift reads towards 5' direction to determine the precise binding site. Set as half of the fragment length. Default 100 **M-value (log2-ratio) cutoff** * Absolute M-value (log2-ratio) cutoff to define biased (differential binding) peaks. Default: 1.0 **P-value cutoff** * P-value cutoff to define biased peaks. Default: 0.01 **Window size** * Window size to count reads and calculate read densities. 2000 is recommended for sharp histone marks like H3K4me3 and H3K27ac, and 1000 for TFs or DNase-seq. Default: 2000

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

Path: workflows/manorm-se.cwl

Branch/Commit ID: 30031ca5e69cec603c4733681de54dc7bffa20a3

https://github.com/umccr/cwl-ica.git

Path: workflows/dragen-germline-pipeline/4.2.4/dragen-germline-pipeline__4.2.4.cwl

Branch/Commit ID: a552aab9adc419df90774954f361cdf3d336ee32

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

Path: testdata/scatter-wf3_v1_1.cwl

Branch/Commit ID: 0ad6983898f0d9001fe0f416f97c4d8b940e384a

Packed ID: main

ATAC-seq 03 mapping - reads: SE - blacklist removal

https://github.com/Duke-GCB/GGR-cwl.git

Path: v1.0/ATAC-seq_pipeline/03-map-se-blacklist-removal.cwl

Branch/Commit ID: 6e68bda2cb45e8dc8e4d067c4220d65acfa53065

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

Path: definitions/pipelines/alignment_umi_molecular.cwl

Branch/Commit ID: f77a920bcc73f6cfdb091eed75a149d02cd8a263

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

Path: protein_alignment/wf_compart_filter_prosplign.cwl

Branch/Commit ID: 54c5074587af001a44eccb4762a4cb25fa24cb3e