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Graph Name Retrieved From View
workflow graph assm_assm_blastn_wnode

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

Path: task_types/tt_assm_assm_blastn_wnode.cwl

Branch/Commit ID: 16e3915d2a357e2a861b30911c832e5ddc0c1784
workflow graph js_output_workflow.cwl

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

Path: tests/wf/js_output_workflow.cwl

Branch/Commit ID: 047e69bb169e79fad6a7285ee798c4ecec3b218b
workflow graph count-lines8-wf.cwl

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

Path: cwltool/schemas/v1.0/v1.0/count-lines8-wf.cwl

Branch/Commit ID: 1eb6bfe3c77aebaf69453a669d21ae7a5a78056f
workflow graph Sounder SIPS L1B PGE

Processes Sounder SIPS L1A products into L1B Products

 https://github.com/unity-sds/unity-sps-workflows.git

Path: sounder_sips/l1b_package.cwl

Branch/Commit ID: 84ecf33903c453db1228ed372ac676ac771136ef

Packed ID: main
workflow graph Detect Variants workflow for WGS pipeline

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

Path: definitions/pipelines/detect_variants_wgs.cwl

Branch/Commit ID: adcae308fdccaa1190083616118dfadb4df65dca
workflow graph DESeq2 (LRT, step 1) - 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 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 performing 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. **Biological Replicates:** At least two biological replicates are required for every compared category. 2. **Metadata File:** The metadata file describes relations between compared experiments. For example: ```csv ,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 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:** Design and reduced formulas should start with `~` and include categories or, optionally, their interactions from the metadata file header. See details in the 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. **Batch Correction:** If batch correction is required, provide the `batch_file` input. This file should be a headerless TSV/CSV file where the first column contains sample names matching `expression_file_names`, and the second column contains the batch group name.

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

Path: workflows/deseq-lrt-step-1.cwl

Branch/Commit ID: 99066c338467af54064cc4eb1be7ea863a785202
workflow graph Filter differentially bound sites for heatmap analysis

Filter DiffBind results for deepTools heatmap analysis ====================================================== Filter differentially bound sites from DiffBind analysis to be used with deepTools heatmap analysis

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

Path: workflows/filter-diffbind-for-heatmap.cwl

Branch/Commit ID: 57863b6131d8262c5ce864adaf8e4038401e71a2
workflow graph foldseek easy-search workflow

foldseek easy-search workflow listing files and foldseek easy-search process

https://github.com/yonesora56/plant2human.git

Path: Workflow/10_foldseek_easy_search_wf.cwl

Branch/Commit ID: 1d9df140de0ffc7d22b6d151b98b59de162e7c37
workflow graph RNA-Seq pipeline single-read

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: 9ee330737f4603e4e959ffe786fbb2046db70a00
workflow graph RNA-Seq pipeline single-read

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: c602e3cdd72ff904dd54d46ba2b5146eb1c57022