Explore Workflows

View already parsed workflows here or click here to add your own

Graph Name Retrieved From View
workflow graph Bisulfite QC tools

https://github.com/apaul7/cancer-genomics-workflow.git

Path: definitions/subworkflows/bisulfite_qc.cwl

Branch/Commit ID: low-vaf
workflow graph Exome QC workflow

https://github.com/fgomez02/analysis-workflows.git

Path: definitions/subworkflows/qc_exome.cwl

Branch/Commit ID: No_filters_detect_variants
workflow graph mutect parallel workflow

https://github.com/apaul7/cancer-genomics-workflow.git

Path: definitions/subworkflows/mutect.cwl

Branch/Commit ID: low-vaf
workflow graph EMG assembly for paired end Illumina

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

Path: workflows/emg-pipeline-v4-assembly-metaSPAdes.cwl

Branch/Commit ID: master
workflow graph ST520105.cwl

https://github.com/Marco-Salvi/cwl-test.git

Path: wf5201/ST520105.cwl

Branch/Commit ID: main
workflow graph Motif Finding with HOMER with random background regions

Motif Finding with HOMER with random background regions --------------------------------------------------- HOMER contains a novel motif discovery algorithm that was designed for regulatory element analysis in genomics applications (DNA only, no protein). It is a differential motif discovery algorithm, which means that it takes two sets of sequences and tries to identify the regulatory elements that are specifically enriched in on set relative to the other. It uses ZOOPS scoring (zero or one occurrence per sequence) coupled with the hypergeometric enrichment calculations (or binomial) to determine motif enrichment. HOMER also tries its best to account for sequenced bias in the dataset. It was designed with ChIP-Seq and promoter analysis in mind, but can be applied to pretty much any nucleic acids motif finding problem. Here is how we generate background for Motifs Analysis ------------------------------------- 1. Take input file with regions in a form of “chr\" “start\" “end\" 2. Sort and remove duplicates from this regions file 3. Extend each region in 20Kb into both directions 4. Merge all overlapped extended regions 5. Subtract not extended regions from the extended ones 6. Randomly distribute not extended regions within the regions that we got as a result of the previous step 7. Get fasta file from these randomly distributed regions (from the previous step). Use it as background For more information please refer to: ------------------------------------- [Official documentation](http://homer.ucsd.edu/homer/motif/)

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

Path: workflows/homer-motif-analysis.cwl

Branch/Commit ID: 7ced5a5259dbd8b3fc64456beaeffd44f4a24081
workflow graph qiime2 identify differentially abundant features

Differential abundance testing with ANCOM from https://docs.qiime2.org/2018.4/tutorials/moving-pictures/

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

Path: packed/qiime2-step2-deblur.cwl

Branch/Commit ID: qiime2-workflow-paired

Packed ID: qiime2-09-ancom.cwl
workflow graph assembly-2.cwl

https://github.com/EBI-Metagenomics/pipeline-v5.git

Path: workflows/conditionals/assembly/assembly-2.cwl

Branch/Commit ID: master
workflow graph WES GATK4 Preprocessing

Whole Exome Sequence analysis GATK4 Preprocessing

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

Path: workflows/exomeseq-gatk4-preprocessing.cwl

Branch/Commit ID: master
workflow graph Unaligned BAM to BQSR and VCF

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

Path: workflows/hello/exome_alignment_packed.cwl

Branch/Commit ID: master

Packed ID: workflow.cwl_2