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Graph Name Retrieved From View
workflow graph Build Bismark indices

Copy fasta_file file to the folder and run run bismark_genome_preparation script to prepare indices for Bismark Methylation Analysis. Bowtie2 aligner is used by default. The name of the output indices folder is equal to the genome input.

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

Path: workflows/bismark-index.cwl

Branch/Commit ID: 5561f7ee11dd74848680351411a19aa87b13d27b

workflow graph env-wf2.cwl

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

Path: cwltool/schemas/v1.0/v1.0/env-wf2.cwl

Branch/Commit ID: 2ae8117360a3cd4909d9d3f2b35c30bfffb25d0a

workflow graph exomeseq-gatk4-01-preprocessing.cwl

https://github.com/Duke-GCB/bespin-cwl.git

Path: subworkflows/exomeseq-gatk4-01-preprocessing.cwl

Branch/Commit ID: 216ff9bf78130add564f7bcfba6385d5dab4c77d

workflow graph ROSE: rank ordering of super-enhancers

Super-enhancers, consist of clusters of enhancers that are densely occupied by the master regulators and Mediator. Super-enhancers differ from typical enhancers in size, transcription factor density and content, ability to activate transcription, and sensitivity to perturbation. Use to create stitched enhancers, and to separate super-enhancers from typical enhancers using sequencing data (.bam) given a file of previously identified constituent enhancers (.gff)

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

Path: workflows/super-enhancer.cwl

Branch/Commit ID: 564156a9e1cc7c3679a926c479ba3ae133b1bfd4

workflow graph Single-Cell WNN Cluster Analysis

Single-Cell WNN Cluster Analysis Clusters cells by similarity on the basis of both gene expression and chromatin accessibility data from the outputs of the “Single-Cell RNA-Seq Dimensionality Reduction Analysis” and “Single-Cell ATAC-Seq Dimensionality Reduction Analysis” pipelines run sequentially. The results of this workflow are used in the “Single-Cell Manual Cell Type Assignment”, “Single-Cell RNA-Seq Differential Expression Analysis”, “Single-Cell RNA-Seq Trajectory Analysis”, “Single-Cell Differential Abundance Analysis”, “Single-Cell ATAC-Seq Differential Accessibility Analysis”, and “Single-Cell ATAC-Seq Genome Coverage” pipelines.

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

Path: workflows/sc-wnn-cluster.cwl

Branch/Commit ID: 549fac35bf6b8b1c25af0f4f6c3f162c40dc130e

workflow graph MAnorm2 for Normalizing and Comparing ChIP-Seq/ATAC-Seq Samples

MAnorm2 for Normalizing and Comparing ChIP-Seq/ATAC-Seq Samples

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

Path: workflows/manorm2.cwl

Branch/Commit ID: 549fac35bf6b8b1c25af0f4f6c3f162c40dc130e

workflow graph scatter-wf4.cwl#main

https://github.com/common-workflow-language/cwl-v1.2.git

Path: tests/scatter-wf4.cwl

Branch/Commit ID: a5073143db4155e05df8d2e7eb59d9e62acd65a5

Packed ID: main

workflow graph GAT - Genomic Association Tester

GAT: Genomic Association Tester ============================================== A common question in genomic analysis is whether two sets of genomic intervals overlap significantly. This question arises, for example, in the interpretation of ChIP-Seq or RNA-Seq data. The Genomic Association Tester (GAT) is a tool for computing the significance of overlap between multiple sets of genomic intervals. GAT estimates significance based on simulation. Gat implemements a sampling algorithm. Given a chromosome (workspace) and segments of interest, for example from a ChIP-Seq experiment, gat creates randomized version of the segments of interest falling into the workspace. These sampled segments are then compared to existing genomic annotations. The sampling method is conceptually simple. Randomized samples of the segments of interest are created in a two-step procedure. Firstly, a segment size is selected from to same size distribution as the original segments of interest. Secondly, a random position is assigned to the segment. The sampling stops when exactly the same number of nucleotides have been sampled. To improve the speed of sampling, segment overlap is not resolved until the very end of the sampling procedure. Conflicts are then resolved by randomly removing and re-sampling segments until a covering set has been achieved. Because the size of randomized segments is derived from the observed segment size distribution of the segments of interest, the actual segment sizes in the sampled segments are usually not exactly identical to the ones in the segments of interest. This is in contrast to a sampling method that permutes segment positions within the workspace.

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

Path: workflows/gat-run.cwl

Branch/Commit ID: 7ae3b75bbe614e59cdeaba06047234a6c40c0fe9

workflow graph Functional analyis of sequences that match the 16S SSU

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

Path: workflows/16S_taxonomic_analysis.cwl

Branch/Commit ID: 5e8217435bcdd597b2ad236f3e847d13d4c21824

workflow graph functional analysis prediction with InterProScan

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

Path: workflows/functional_analysis.cwl

Branch/Commit ID: 5e8217435bcdd597b2ad236f3e847d13d4c21824