Explore Workflows

Motif Finding with HOMER with random background regions --------------------------------------------------- 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. Here is how we generate background for Motifs Analysis ------------------------------------- 1. Take input file with regions in a form of “chr\" “start\" “end\" 2. Sort and remove duplicates from this regions file 3. Extend each region in 20Kb into both directions 4. Merge all overlapped extended regions 5. Subtract not extended regions from the extended ones 6. Randomly distribute not extended regions within the regions that we got as a result of the previous step 7. Get fasta file from these randomly distributed regions (from the previous step). Use it as background 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.cwl

Branch/Commit ID: 2005c6b7f1bff6247d015ff6c116bd9ec97158bb

Differential Binding Analysis of ChIP-Seq Peak Data --------------------------------------------------- DiffBind processes ChIP-Seq data enriched for genomic loci where specific protein/DNA binding occurs, including peak sets identified by ChIP-Seq peak callers and aligned sequence read datasets. It is designed to work with multiple peak sets simultaneously, representing different ChIP experiments (antibodies, transcription factor and/or histone marks, experimental conditions, replicates) as well as managing the results of multiple peak callers. For more information please refer to: ------------------------------------- Ross-Innes CS, Stark R, Teschendorff AE, Holmes KA, Ali HR, Dunning MJ, Brown GD, Gojis O, Ellis IO, Green AR, Ali S, Chin S, Palmieri C, Caldas C, Carroll JS (2012). “Differential oestrogen receptor binding is associated with clinical outcome in breast cancer.” Nature, 481, -4.

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

Path: workflows/diffbind.cwl

Branch/Commit ID: 2005c6b7f1bff6247d015ff6c116bd9ec97158bb

FastQC - a quality control tool for high throughput sequence data ===================================== FastQC aims to provide a simple way to do some quality control checks on raw sequence data coming from high throughput sequencing pipelines. It provides a modular set of analyses which you can use to give a quick impression of whether your data has any problems of which you should be aware before doing any further analysis. The main functions of FastQC are: - Import of data from FastQ files (any variant) - Providing a quick overview to tell you in which areas there may be problems - Summary graphs and tables to quickly assess your data - Export of results to an HTML based permanent report - Offline operation to allow automated generation of reports without running the interactive application

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

Path: workflows/fastqc.cwl

Branch/Commit ID: 2005c6b7f1bff6247d015ff6c116bd9ec97158bb

Interval overlapping alignments counts ====================================== Reports the count of alignments from multiple samples that overlap specific intervals.

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

Path: workflows/bedtools-multicov.cwl

Branch/Commit ID: 2005c6b7f1bff6247d015ff6c116bd9ec97158bb

Creates indices for: * [STAR](https://github.com/alexdobin/STAR) v2.5.3a (03/17/2017) PMID: [23104886](https://www.ncbi.nlm.nih.gov/pubmed/23104886) * [bowtie](http://bowtie-bio.sourceforge.net/tutorial.shtml) v1.2.0 (12/30/2016) It performs the following steps: 1. `STAR --runMode genomeGenerate` to generate indices, based on [FASTA](http://zhanglab.ccmb.med.umich.edu/FASTA/) and [GTF](http://mblab.wustl.edu/GTF2.html) input files, returns results as an array of files 2. Outputs indices as [Direcotry](http://www.commonwl.org/v1.0/CommandLineTool.html#Directory) data type 3. Separates *chrNameLength.txt* file from Directory output 4. `bowtie-build` to generate indices requires genome [FASTA](http://zhanglab.ccmb.med.umich.edu/FASTA/) file as input, returns results as a group of main and secondary files

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

Path: workflows/genome-indices.cwl

Branch/Commit ID: 104059e07a2964673e21d371763e33c0afeb2d03

https://github.com/curoverse/l7g.git

Path: cwl-version/experimental/l7g-tile-assembly/tile-assembly-human_g1k_v37/l7g-build-human_g1k_v37-tile-assembly.cwl

Branch/Commit ID: cdfe9178ad4e481d2391cd2da74b82d66a61b0bb

https://github.com/curoverse/l7g.git

Path: cwl-version/experimental/l7g-tile-assembly/tile-assembly-hg38/cwl/hg38-tileassembly.cwl

Branch/Commit ID: cdfe9178ad4e481d2391cd2da74b82d66a61b0bb

https://github.com/curoverse/l7g.git

Path: cwl-version/experimental/l7g-tile-assembly/tile-assembly-hg19/l7g-liftover.cwl

Branch/Commit ID: cdfe9178ad4e481d2391cd2da74b82d66a61b0bb

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

Path: src/test/resources/cwl/lobstr-v1/lobSTR-workflow.cwl

Branch/Commit ID: 4697c41e576075a72b1fda7eb5424097230a0ce1

https://github.com/common-workflow-language/cwl-v1.2.git

Path: tests/search.cwl

Branch/Commit ID: a5073143db4155e05df8d2e7eb59d9e62acd65a5

Packed ID: main