Explore Workflows

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

Path: tests/wf/1st-workflow.cwl

Branch/Commit ID: e835bc0487fe42fb330b6222c9be65d18dd81ec9

Create emblem textures

https://gitlab.com/unduthegun/stellaris-emblem-lab.git

Path: textures/textures.cwl

Branch/Commit ID: 03cbc30a5373f3056a052bb08995dfedffcec6ad

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

Path: tests/count-lines9-wf-noET.cwl

Branch/Commit ID: 0e37d46e793e72b7c16b5ec03e22cb3ce1f55ba3

This workflow will run OxoG, variantbam, and annotate. Run this as `dockstore --script --debug workflow launch --descriptor cwl --local-entry --entry ./oxog_varbam_annotate_wf.cwl --json oxog_varbam_annotat_wf.input.json `

https://github.com/icgc-tcga-pancancer/oxog-dockstore-tools.git

Path: oxog_varbam_annotate_wf.cwl

Branch/Commit ID: 6366ed398da10019b6d81a789291af6d909f28f4

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: 60854b5d299df91e135e05d02f4be61f6a310fbc

https://github.com/EBI-Metagenomics/ebi-metagenomics-cwl.git

Path: tools/rRNA_selection.cwl

Branch/Commit ID: ca6ca613f0d3728d9589a6ca6293e66dfde87bfb

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

Path: definitions/subworkflows/phase_vcf.cwl

Branch/Commit ID: 18600518ce6539a2e29c1707392a4c5da5687fa3

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

Path: definitions/pipelines/tumor_only_wgs.cwl

Branch/Commit ID: 174f3b239018328cec1d821947438b457552724c

Motif Finding with HOMER with target and background regions from peaks --------------------------------------------------- HOMER contains a novel motif discovery algorithm that was designed for regulatory element analysis in genomics applications (DNA only, no protein). It is a differential motif discovery algorithm, which means that it takes two sets of sequences and tries to identify the regulatory elements that are specifically enriched in on set relative to the other. It uses ZOOPS scoring (zero or one occurrence per sequence) coupled with the hypergeometric enrichment calculations (or binomial) to determine motif enrichment. HOMER also tries its best to account for sequenced bias in the dataset. It was designed with ChIP-Seq and promoter analysis in mind, but can be applied to pretty much any nucleic acids motif finding problem. For more information please refer to: ------------------------------------- [Official documentation](http://homer.ucsd.edu/homer/motif/)

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

Path: workflows/homer-motif-analysis-peak.cwl

Branch/Commit ID: b1a5dabeeeb9079b30b2871edd9c9034a1e00c1c

Fusion workflow, runs STARFusion and Arriba

https://github.com/bd2kgenomics/dockstore_workflow_fusion.git

Path: fusion_workflow.cwl

Branch/Commit ID: a0475b43610078cb2b881908aa9bcbd6beb3f1e9