Explore Workflows

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

Path: definitions/subworkflows/align.cwl

Branch/Commit ID: 295e7b7f51727c0f2d6cc86ce817449b2e8dba3c

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

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

Branch/Commit ID: 8d8512061f2367c90aac67bcbf92af1061b4af59

Packed ID: collision

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: 1f03ff02ef829bdb9d582825bcd4ca239e84ca2e

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

Path: subworkflows/xenbase-sra-to-fastq-pe.cwl

Branch/Commit ID: c602e3cdd72ff904dd54d46ba2b5146eb1c57022

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

Path: progs/protein_extract.cwl

Branch/Commit ID: 505b91e41741ccbcd5ebd2b6a09a3be604f9ece3

https://github.com/mskcc/ACCESS-Pipeline.git

Path: workflows/waltz/waltz_workflow_all_bams.cwl

Branch/Commit ID: 9e6eae9eb8448e68d509397a46303551a93a164d

https://github.com/bioexcel/biobb_wf_cwl_tutorial.git

Path: biobb_wf_cwl_tutorial/examples/BioExcel-CWL-firstWorkflow.cwl

Branch/Commit ID: 9de04c9eb4bb449de8277d3ddfe88dd5518fc966

https://gitlab.bsc.es/lrodrig1/structuralvariants_poc.git

Path: structuralvariants/subworkflows/bwa_mem.cwl

Branch/Commit ID: 70eec658fd1b92c4d0e3b24146820010b5983d41

Allele specific RNA-Seq (using vcf) paired-end workflow

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

Path: workflows/allele-vcf-rnaseq-pe.cwl

Branch/Commit ID: cf107bc24a37883ef01b959fd89c19456aaecc02

Workflow for Metagenomics from raw reads to annotated bins. Steps: - workflow_illumina_quality.cwl: - FastQC (control) - fastp (quality trimming) - kraken2 (taxonomy) - bbmap contamination filter - SPAdes (Assembly) - QUAST (Assembly quality report) - BBmap (Read mapping to assembly) - Contig binning (OPTIONAL)

https://git.wageningenur.nl/unlock/cwl.git

Path: cwl/workflows/workflow_metagenomics_assembly.cwl

Branch/Commit ID: b9097b82e6ab6f2c9496013ce4dd6877092956a0