Explore Workflows
View already parsed workflows here or click here to add your own
Graph | Name | Retrieved From | View |
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Run taxonomic classification, create OTU table and krona visualisation
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![]() Path: workflows/subworkflows/classify-otu-visualise.cwl Branch/Commit ID: master |
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preprocess_vcf.cwl
This workflow will perform preprocessing steps on VCFs for the OxoG/Variantbam/Annotation workflow. |
![]() Path: preprocess_vcf.cwl Branch/Commit ID: develop |
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Bacterial Annotation, pass 2, blastp-based functional annotation (first pass)
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![]() Path: bacterial_annot/wf_bacterial_annot_pass2.cwl Branch/Commit ID: test |
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gdc_dnaseq_main_workflow.cwl
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![]() Path: subworkflows/main/gdc_dnaseq_main_workflow.cwl Branch/Commit ID: master |
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broad-best-practice-data-pre-processing-workflow-4-1-0-0_decomposed.cwl
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![]() Path: broad-best-practice-data-pre-processing-workflow-4-1-0-0_decomposed.cwl Branch/Commit ID: master |
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methylCtools_align_merge_sort_dedup.cwl
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![]() Path: workflows/methylCtools/tools/methylCtools_align_merge_sort_dedup.cwl Branch/Commit ID: main |
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sc_atac_seq_prep_process_init.cwl
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![]() Path: steps/sc_atac_seq_prep_process_init.cwl Branch/Commit ID: develop |
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exomeseq-02-variantdiscovery.cwl
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![]() Path: subworkflows/exomeseq-02-variantdiscovery.cwl Branch/Commit ID: master |
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zip_and_index_vcf.cwl
This is a very simple workflow of two steps. It will zip an input VCF file and then index it. The zipped file and the index file will be in the workflow output. |
![]() Path: zip_and_index_vcf.cwl Branch/Commit ID: 1.0.0 |
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DEPRECATED - Motif Finding with HOMER with target and background regions from peaks
Motif Finding with HOMER with target and background regions from peaks --------------------------------------------------- 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. For more information please refer to: ------------------------------------- [Official documentation](http://homer.ucsd.edu/homer/motif/) |
![]() Path: workflows/homer-motif-analysis-peak.cwl Branch/Commit ID: master |