Explore Workflows

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

Path: protein_alignment/wf_protein_alignment.cwl

Branch/Commit ID: 4ffbad9ffeab15ec8af5f6f91bd352ef96d1ef77

https://github.com/reanahub/reana-demo-worldpopulation.git

Path: workflow/cwl/worldpopulation-htcondorcern.cwl

Branch/Commit ID: cf816c743b2df2acc6ce666470b640072376f0f6

https://github.com/EBI-Metagenomics/pipeline-v5.git

Path: workflows/conditionals/raw-reads/raw-reads-1.cwl

Branch/Commit ID: a83ee883bb3c7480010fa952939fac771491ddf4

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

Path: tests/count-lines11-extra-step-wf.cwl

Branch/Commit ID: 3e90671b25f7840ef2926ad2bacbf447772dda94

Runs ChIP-Seq BioWardrobe basic analysis with single-end data file.

https://github.com/Barski-lab/workflows.git

Path: workflows/chipseq-se.cwl

Branch/Commit ID: 896422c9ff1995024cb77675edcd4d973ae11f7a

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

Path: tests/count-lines4-wf.cwl

Branch/Commit ID: 7d7986a6e852ca6e3239c96d3a05dd536c76c903

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

Path: tests/io-any-wf-1.cwl

Branch/Commit ID: 7d7986a6e852ca6e3239c96d3a05dd536c76c903

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: d76110e0bfc40c874f82e37cef6451d74df4f908

Experimental pipeline for Cut-n-Run analysis. Uses mapping results from the following experiment types: - `chipseq-pe.cwl` - `trim-chipseq-pe.cwl` - `trim-atacseq-pe.cwl` Note, the upstream analyses should not have duplicates removed

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

Path: workflows/trim-chipseq-pe-cut-n-run.cwl

Branch/Commit ID: d76110e0bfc40c874f82e37cef6451d74df4f908

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: d76110e0bfc40c874f82e37cef6451d74df4f908