Explore Workflows
View already parsed workflows here or click here to add your own
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WGS and MT analysis for fastq files
rna / protein - qc, preprocess, filter, annotation, index, abundance |
![]() Path: CWL/Workflows/wgs-noscreen-fastq.workflow.cwl Branch/Commit ID: 9aba38fd1569287b7256ace7163ac84320909f8a |
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scatter-valuefrom-wf3.cwl#main
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![]() Path: v1.0/v1.0/scatter-valuefrom-wf3.cwl Branch/Commit ID: 4d06b9efd26c5813c13684ebcc95547bb75ddfcc Packed ID: main |
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WGS QC workflow mouse
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![]() Path: definitions/subworkflows/qc_wgs_mouse.cwl Branch/Commit ID: 3034168d652bfa930ba09af20e473a4564a8010d |
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WGS QC workflow nonhuman
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![]() Path: definitions/subworkflows/qc_wgs_nonhuman.cwl Branch/Commit ID: 2decd55996b912feb48be5db1b052aa3274ee405 |
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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: 17a4a68b20e0af656e09714c1f39fe761b518686 |
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DiffBind - Differential Binding Analysis of ChIP-Seq Peak Data
Differential Binding Analysis of ChIP-Seq Peak Data --------------------------------------------------- DiffBind processes ChIP-Seq data enriched for genomic loci where specific protein/DNA binding occurs, including peak sets identified by ChIP-Seq peak callers and aligned sequence read datasets. It is designed to work with multiple peak sets simultaneously, representing different ChIP experiments (antibodies, transcription factor and/or histone marks, experimental conditions, replicates) as well as managing the results of multiple peak callers. For more information please refer to: ------------------------------------- Ross-Innes CS, Stark R, Teschendorff AE, Holmes KA, Ali HR, Dunning MJ, Brown GD, Gojis O, Ellis IO, Green AR, Ali S, Chin S, Palmieri C, Caldas C, Carroll JS (2012). “Differential oestrogen receptor binding is associated with clinical outcome in breast cancer.” Nature, 481, -4. |
![]() Path: workflows/diffbind.cwl Branch/Commit ID: ad948b2691ef7f0f34de38f0102c3cd6f5182b29 |
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qiime2 DADA2 detect/correct paired sequence data
Option 1: DADA2 from https://docs.qiime2.org/2018.4/tutorials/moving-pictures/ |
![]() Path: packed/qiime2-step2-dada2-paired.cwl Branch/Commit ID: ef08cb00bd55b4c712645d171dbc691e01ed6165 Packed ID: qiime2-03-dada2-paired.cwl |
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Filter single sample sv vcf from paired read callers(Manta/Smoove)
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![]() Path: definitions/subworkflows/sv_paired_read_caller_filter.cwl Branch/Commit ID: 10870aefd20469e728969269ff3c54b3b8339a18 |
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scatter GATK HaplotypeCaller over intervals
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![]() Path: definitions/subworkflows/gatk_haplotypecaller_iterator.cwl Branch/Commit ID: 2decd55996b912feb48be5db1b052aa3274ee405 |
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umi duplex alignment workflow
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![]() Path: definitions/subworkflows/duplex_alignment.cwl Branch/Commit ID: 844c10a4466ab39c02e5bfa7a210c195b8efa77a |