Explore Workflows

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

Path: definitions/pipelines/detect_variants_nonhuman.cwl

Branch/Commit ID: 1750cd5cc653f058f521b6195e3bec1e7df1a086

This workflow performs calibrations, related to the molecular content of the atmosphere. To be updated...

https://gitlab.cta-observatory.org/cta-computing/dpps/dpps-workflows.git

Path: workflows/calibpipe/AtmosphericCalibrations/MolecularAtmosphere/uc-dpps-cp-110.cwl

Branch/Commit ID: edd82fc41baddb7e01c638998d882d4f4fddaadc

This workflow performs calibrates atmosphere and produces an atmospheric model and an extinction hypercube.

https://gitlab.cta-observatory.org/cta-computing/dpps/dpps-workflows.git

Path: workflows/calibpipe/AtmosphericCalibrations/uc-cp-100.cwl

Branch/Commit ID: edd82fc41baddb7e01c638998d882d4f4fddaadc

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

Path: v1.0/v1.0/sum-wf.cwl

Branch/Commit ID: 622134ebc48980676b7e53fe39405c428920c03e

The original [BioWardrobe's](https://biowardrobe.com) [PubMed ID:26248465](https://www.ncbi.nlm.nih.gov/pubmed/26248465) **RNA-Seq** basic analysis for a **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.cwl

Branch/Commit ID: ee66d03be8a7fd61367db40c37a973ff55ece4da

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

Path: v1.0/v1.0/count-lines1-wf.cwl

Branch/Commit ID: 622134ebc48980676b7e53fe39405c428920c03e

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

Path: definitions/subworkflows/molecular_alignment.cwl

Branch/Commit ID: 22fce2dbdada0c4135b6f0677f78535cf980cb07

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

Path: task_types/tt_kmer_build_tree.cwl

Branch/Commit ID: f1eb0f4eaaf1661044f28d859f7e8d4302525ead

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: 09267e79fd867aa68a219c69e6db7d8e2e877be2

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

Path: definitions/subworkflows/fp_filter.cwl

Branch/Commit ID: 258bd4353ad1ca7790b3ae626bf42ab8194e7561