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Graph Name Retrieved From View
workflow graph oc-workflow.cwl

https://github.com/karchinlab/opencravat-cwl.git

Path: oc-workflow.cwl

Branch/Commit ID: d3dd7c649d229d4a0d78c1ba5ccef560da8f09a0
workflow graph baysorStaged.cwl

https://github.com/hubmapconsortium/spatial-transcriptomics-pipeline.git

Path: steps/baysorStaged.cwl

Branch/Commit ID: 03b8da4ede3afcbf97b4e780c153155f33e76c84
workflow graph WGS and MT analysis for fastq files

rna / protein - qc, preprocess, filter, annotation, index, abundance

 https://github.com/MG-RAST/pipeline.git

Path: CWL/Workflows/wgs-fastq.workflow.cwl

Branch/Commit ID: 81feefc84ec0faecf1ade718001d5f07610e616e
workflow graph Xenbase ChIP-Seq pipeline paired-end

1. Convert input SRA file into pair of upsrtream and downstream FASTQ files (run fastq-dump) 2. Analyze quality of FASTQ files (run fastqc with each of the FASTQ files) 3. If any of the following fields in fastqc generated report is marked as failed for at least one of input FASTQ files: \"Per base sequence quality\", \"Per sequence quality scores\", \"Overrepresented sequences\", \"Adapter Content\", - trim adapters (run trimmomatic) 4. Align original or trimmed FASTQ files to reference genome (run Bowtie2) 5. Sort and index generated by Bowtie2 BAM file (run samtools sort, samtools index) 6. Remove duplicates in sorted BAM file (run picard) 7. Sort and index BAM file after duplicates removing (run samtools sort, samtools index) 8. Count mapped reads number in sorted BAM file (run bamtools stats) 9. Generate genome coverage BED file (run bedtools genomecov) 10. Sort genearted BED file (run sort) 11. Generate genome coverage bigWig file from BED file (run bedGraphToBigWig)

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

Path: workflows/xenbase-chipseq-pe.cwl

Branch/Commit ID: 9bf0aa495735f8081bb5870cb32fc898b9e6eb22
workflow graph assm_assm_blastn_wnode

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

Path: task_types/tt_assm_assm_blastn_wnode.cwl

Branch/Commit ID: 7319ccfd2108929588bdc266d9df198629dfaa65
workflow graph mut3.cwl

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

Path: tests/wf/mut3.cwl

Branch/Commit ID: fec7a10466a26e376b14181a88734983cfb1b8cb
workflow graph taxonomy_check_16S

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

Path: task_types/tt_taxonomy_check_16S.cwl

Branch/Commit ID: 7319ccfd2108929588bdc266d9df198629dfaa65
workflow graph sec-wf.cwl

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

Path: tests/wf/sec-wf.cwl

Branch/Commit ID: fec7a10466a26e376b14181a88734983cfb1b8cb
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: 1131f82a53315cca217a6c84b3bd272aa62e4bca
workflow graph gcaccess_from_list

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

Path: task_types/tt_gcaccess_from_list.cwl

Branch/Commit ID: 449f87c8365637e803ba66f83367e96f98c88f5c