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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: 104059e07a2964673e21d371763e33c0afeb2d03

https://github.com/hubmapconsortium/create-vis-symlink-archive.git

Path: pipeline.cwl

Branch/Commit ID: 6e55233fb14a96fc5ed6f40b4e5a259ab0e8446c

Obtaining and importing data from https://docs.qiime2.org/2018.4/tutorials/moving-pictures/

https://github.com/duke-gcb/bespin-cwl.git

Path: packed/qiime2-step1-import-demux-paired.cwl

Branch/Commit ID: e2dc95d4f12210359360d814382e7201d836dfcf

Packed ID: qiime2-01-import-data-paired.cwl

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

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

Branch/Commit ID: 03af16c9df2ee77485d4ab092cd64ae096d2e71c

Packed ID: collision

This workflow clean up vectros from fastq files

https://github.com/ncbi/cwl-ngs-workflows-cbb.git

Path: workflows/File-formats/fastq-vector-removal.cwl

Branch/Commit ID: 433720e6ba8c2d85b15de3ffb9ce1236f08978a4

Note: should be updated The original [BioWardrobe's](https://biowardrobe.com) [PubMed ID:26248465](https://www.ncbi.nlm.nih.gov/pubmed/26248465) **RNA-Seq** basic analysis for **strand specific single-read** experiment. A corresponded input [FASTQ](http://maq.sourceforge.net/fastq.shtml) file has to be provided. Current workflow should be used only with the single-read RNA-Seq data. It performs the following steps: 1. Use STAR to align reads from input FASTQ file according to the predefined reference indices; generate unsorted BAM file and alignment statistics file 2. Use fastx_quality_stats to analyze input FASTQ file and generate quality statistics file 3. Use samtools sort to generate coordinate sorted BAM(+BAI) file pair from the unsorted BAM file obtained on the step 1 (after running STAR) 5. Generate BigWig file on the base of sorted BAM file 6. Map input FASTQ file to predefined rRNA reference indices using Bowtie to define the level of rRNA contamination; export resulted statistics to file 7. Calculate isoform expression level for the sorted BAM file and GTF/TAB annotation file using GEEP reads-counting utility; export results to file

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

Path: workflows/rnaseq-se-dutp.cwl

Branch/Commit ID: 799575ce58746813f066a665adeacdda252d8cab

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

Path: subworkflows/allele-process-reference.cwl

Branch/Commit ID: 6bf56698c6fe6e781723dea32bc922b91ef49cf3

https://github.com/NCI-GDC/gdc-dnaseq-cwl.git

Path: workflows/dnaseq/integrity.cwl

Branch/Commit ID: dd7f86b3cc10eb1cda07dc2fc279ba2529c8ad61

https://github.com/proteinswebteam/ebi-metagenomics-cwl.git

Path: workflows/cmsearch-multimodel.cwl

Branch/Commit ID: 5dc7c5ca618a248a99bd4bf5f3042cdb21947193

https://github.com/ProteinsWebTeam/ebi-metagenomics-cwl.git

Path: workflows/emg-pipeline-v3-paired.cwl

Branch/Commit ID: 7bb76f33bf40b5cd2604001cac46f967a209c47f