Explore Workflows
View already parsed workflows here or click here to add your own
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WGS QC workflow nonhuman
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![]() Path: definitions/subworkflows/qc_wgs_nonhuman.cwl Branch/Commit ID: 0b6e8fd8ead7644cf5398395b76af5cf4011686f |
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FastQC - a quality control tool for high throughput sequence data
FastQC - a quality control tool for high throughput sequence data ===================================== FastQC aims to provide a simple way to do some quality control checks on raw sequence data coming from high throughput sequencing pipelines. It provides a modular set of analyses which you can use to give a quick impression of whether your data has any problems of which you should be aware before doing any further analysis. The main functions of FastQC are: - Import of data from FastQ files (any variant) - Providing a quick overview to tell you in which areas there may be problems - Summary graphs and tables to quickly assess your data - Export of results to an HTML based permanent report - Offline operation to allow automated generation of reports without running the interactive application |
![]() Path: workflows/fastqc.cwl Branch/Commit ID: b957a4f681bf0ca8ebba4e0d0ec3936bf79620c5 |
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extract_gencoll_ids
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![]() Path: task_types/tt_extract_gencoll_ids.cwl Branch/Commit ID: 3384fa5776c183d33bef830696b6edc6ec55a292 |
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bam to trimmed fastqs and biscuit alignments
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![]() Path: definitions/subworkflows/bam_to_trimmed_fastq_and_biscuit_alignments.cwl Branch/Commit ID: 3042812447d9e8889c6118986490e9c9b9b13223 |
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Cut-n-Run pipeline paired-end
Experimental pipeline for Cut-n-Run analysis. Uses mapping results from the following experiment types: - `chipseq-pe.cwl` - `trim-chipseq-pe.cwl` - `trim-atacseq-pe.cwl` Note, the upstream analyses should not have duplicates removed |
![]() Path: workflows/trim-chipseq-pe-cut-n-run.cwl Branch/Commit ID: b957a4f681bf0ca8ebba4e0d0ec3936bf79620c5 |
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tt_hmmsearch_wnode.cwl
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![]() Path: task_types/tt_hmmsearch_wnode.cwl Branch/Commit ID: 001e133e0eedaf0dd8447e3f8b3cc898ec6e3e1d |
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HS Metrics workflow
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![]() Path: definitions/subworkflows/hs_metrics.cwl Branch/Commit ID: ffd73951157c61c1581d346628d75b61cdd04141 |
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blastp_wnode_naming
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![]() Path: task_types/tt_blastp_wnode_naming.cwl Branch/Commit ID: 3384fa5776c183d33bef830696b6edc6ec55a292 |
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umi molecular alignment workflow
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![]() Path: definitions/subworkflows/molecular_qc.cwl Branch/Commit ID: 06d2440d115b446c299b4ce96e8812d2f8df86ec |
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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: 799575ce58746813f066a665adeacdda252d8cab |