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Graph Name Retrieved From View
workflow graph wf_get_peaks_pe.cwl

https://github.com/YeoLab/eclip.git

Path: cwl/wf_get_peaks_pe.cwl

Branch/Commit ID: c0fffc4979a92371dc0667a03e3d957bf7f77600
workflow graph bulk scRNA-seq pipeline using Salmon

https://github.com/hubmapconsortium/salmon-rnaseq.git

Path: bulk-pipeline.cwl

Branch/Commit ID: 14d004a163e02a5f8f8590e568a2f4f153508931
workflow graph pcawg_minibam_wf.cwl

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/pcawg-minibam.git

Path: pcawg_minibam_wf.cwl

Branch/Commit ID: e9f3694e31e3c1570183a4f444a44015766f9f2f
workflow graph star-index.cwl

Generates indices for STAR v2.5.3a (03/17/2017).

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

Path: workflows/star-index.cwl

Branch/Commit ID: cf107bc24a37883ef01b959fd89c19456aaecc02
workflow graph minibam_sub_wf.cwl

This is a subworkflow of the main oxog_varbam_annotat_wf workflow - this is not meant to be run as a stand-alone workflow!

https://github.com/david4096/oxog-dockstore-tools.git

Path: minibam_sub_wf.cwl

Branch/Commit ID: 6366ed398da10019b6d81a789291af6d909f28f4
workflow graph Subworkflow that runs cnvkit in single sample mode and returns a vcf file

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

Path: definitions/subworkflows/cnvkit_single_sample.cwl

Branch/Commit ID: ffd73951157c61c1581d346628d75b61cdd04141
workflow graph SoupX - an R package for the estimation and removal of cell free mRNA contamination

Devel version of Single-Cell Advanced Cell Ranger Pipeline (SoupX) =================================================================

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

Path: workflows/soupx.cwl

Branch/Commit ID: 4ab9399a4777610a579ea2c259b9356f27641dcc
workflow graph kmer_seq_entry_extract_wnode

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

Path: task_types/tt_kmer_seq_entry_extract_wnode.cwl

Branch/Commit ID: 17bae57a1f00f5c6db8f3a82d86262f12b8153cf
workflow graph basename-fields-test.cwl

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

Path: cwltool/schemas/v1.0/v1.0/basename-fields-test.cwl

Branch/Commit ID: 047e69bb169e79fad6a7285ee798c4ecec3b218b
workflow graph DESeq2 (LRT) - differential gene expression analysis using likelihood ratio test

Runs DESeq2 using LRT (Likelihood Ratio Test) ============================================= The LRT examines two models for the counts, a full model with a certain number of terms and a reduced model, in which some of the terms of the full model are removed. The test determines if the increased likelihood of the data using the extra terms in the full model is more than expected if those extra terms are truly zero. The LRT is therefore useful for testing multiple terms at once, for example testing 3 or more levels of a factor at once, or all interactions between two variables. The LRT for count data is conceptually similar to an analysis of variance (ANOVA) calculation in linear regression, except that in the case of the Negative Binomial GLM, we use an analysis of deviance (ANODEV), where the deviance captures the difference in likelihood between a full and a reduced model. When one performs a likelihood ratio test, the p values and the test statistic (the stat column) are values for the test that removes all of the variables which are present in the full design and not in the reduced design. This tests the null hypothesis that all the coefficients from these variables and levels of these factors are equal to zero. The likelihood ratio test p values therefore represent a test of all the variables and all the levels of factors which are among these variables. However, the results table only has space for one column of log fold change, so a single variable and a single comparison is shown (among the potentially multiple log fold changes which were tested in the likelihood ratio test). This indicates that the p value is for the likelihood ratio test of all the variables and all the levels, while the log fold change is a single comparison from among those variables and levels. **Technical notes** 1. At least two biological replicates are required for every compared category 2. Metadata file describes relations between compared experiments, for example ``` ,time,condition DH1,day5,WT DH2,day5,KO DH3,day7,WT DH4,day7,KO DH5,day7,KO ``` where `time, condition, day5, day7, WT, KO` should be a single words (without spaces) and `DH1, DH2, DH3, DH4, DH5` correspond to the experiment aliases set in **RNA-Seq experiments** input. 3. Design and reduced formulas should start with **~** and include categories or, optionally, their interactions from the metadata file header. See details in DESeq2 manual [here](https://bioconductor.org/packages/release/bioc/vignettes/DESeq2/inst/doc/DESeq2.html#interactions) and [here](https://bioconductor.org/packages/release/bioc/vignettes/DESeq2/inst/doc/DESeq2.html#likelihood-ratio-test) 4. Contrast should be set based on your metadata file header and available categories in a form of `Factor Numerator Denominator`, where `Factor` - column name from metadata file, `Numerator` - category from metadata file to be used as numerator in fold change calculation, `Denominator` - category from metadata file to be used as denominator in fold change calculation. For example `condition WT KO`.

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

Path: workflows/deseq-lrt.cwl

Branch/Commit ID: 4360fb2e778ecee42e5f78f83b78c65ab3a2b1df