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Graph | Name | Retrieved From | View |
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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: 935a78f1aff757f977de4e3672aefead3b23606b |
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VIRTUS.SE.singlevirus.cwl
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https://github.com/yyoshiaki/VIRTUS.git
Path: workflow/VIRTUS.SE.singlevirus.cwl Branch/Commit ID: 49faf55f97c8f3084b426d2db6640519d6f2ce71 |
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rnaseq-pe-dutp.cwl
Runs RNA-Seq BioWardrobe basic analysis with strand specific pair-end data file. |
https://github.com/Barski-lab/workflows.git
Path: workflows/rnaseq-pe-dutp.cwl Branch/Commit ID: 12edfc2207507e53c6b5bb21e50decb5535a12f7 |
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Filter differentially expressed genes from DESeq for Tag Density Profile Analyses
Filters differentially expressed genes from DESeq for Tag Density Profile Analyses ================================================================================== Tool filters output from DESeq pipeline run for genes to create a file with regions of interest for Tag Density Profile Analyses. |
https://github.com/datirium/workflows.git
Path: workflows/filter-deseq-for-heatmap.cwl Branch/Commit ID: 935a78f1aff757f977de4e3672aefead3b23606b |
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Nanopore assembly workflow
**Workflow for sequencing with ONT Nanopore data, from basecalled reads to (meta)assembly and binning**<br> - Workflow Nanopore Quality - Kraken2 taxonomic classification of FASTQ reads - Flye (de-novo assembly) - Medaka (assembly polishing) - metaQUAST (assembly quality reports) **When Illumina reads are provided:** - Workflow Illumina Quality: https://workflowhub.eu/workflows/336?version=1 - Assembly polishing with Pilon<br> - Workflow binnning https://workflowhub.eu/workflows/64?version=11 - Metabat2 - CheckM - BUSCO - GTDB-Tk **All tool CWL files and other workflows can be found here:**<br> Tools: https://git.wur.nl/unlock/cwl/-/tree/master/cwl<br> Workflows: https://git.wur.nl/unlock/cwl/-/tree/master/cwl/workflows<br> The dependencies are either accessible from https://unlock-icat.irods.surfsara.nl (anonymous,anonymous)<br> and/or<br> By using the conda / pip environments as shown in https://git.wur.nl/unlock/docker/-/blob/master/kubernetes/scripts/setup.sh<br> |
https://git.wur.nl/unlock/cwl.git
Path: cwl/workflows/workflow_nanopore_assembly.cwl Branch/Commit ID: b9097b82e6ab6f2c9496013ce4dd6877092956a0 |
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Generate genome indices for Bismark
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 folder name. |
https://github.com/datirium/workflows.git
Path: workflows/bismark-indices.cwl Branch/Commit ID: 9bf0aa495735f8081bb5870cb32fc898b9e6eb22 |
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bam-bedgraph-bigwig.cwl
Workflow converts input BAM file into bigWig and bedGraph files. Input BAM file should be sorted by coordinates (required by `bam_to_bedgraph` step). If `split` input is not provided use true by default. Default logic is implemented in `valueFrom` field of `split` input inside `bam_to_bedgraph` step to avoid possible bug in cwltool with setting default values for workflow inputs. `scale` has higher priority over the `mapped_reads_number`. The last one is used to calculate `-scale` parameter for `bedtools genomecov` (step `bam_to_bedgraph`) only in a case when input `scale` is not provided. All logic is implemented inside `bedtools-genomecov.cwl`. `bigwig_filename` defines the output name only for generated bigWig file. `bedgraph_filename` defines the output name for generated bedGraph file and can influence on generated bigWig filename in case when `bigwig_filename` is not provided. All workflow inputs and outputs don't have `format` field to avoid format incompatibility errors when workflow is used as subworkflow. |
https://github.com/datirium/workflows.git
Path: tools/bam-bedgraph-bigwig.cwl Branch/Commit ID: e99e80a2c19682d59947bde04a892d7b6d90091c |
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indexing_bed
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https://gitlab.bsc.es/lrodrig1/structuralvariants_poc.git
Path: structuralvariants/cwl/abstract_operations/subworkflows/indexing_bed.cwl Branch/Commit ID: aaecc710f76be9285288a975cc786e354bc9bedf |
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trim-rnaseq-se-dutp.cwl
Runs RNA-Seq dUTP BioWardrobe basic analysis with strand specific single-end data file. |
https://github.com/Barski-lab/workflows.git
Path: workflows/trim-rnaseq-se-dutp.cwl Branch/Commit ID: 12edfc2207507e53c6b5bb21e50decb5535a12f7 |
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Metagenomics workflow
Workflow pilon assembly polishing Steps: - BBmap (Read mapping to assembly) - Pilon |
https://git.wur.nl/unlock/cwl.git
Path: cwl/workflows/workflow_pilon_mapping.cwl Branch/Commit ID: b9097b82e6ab6f2c9496013ce4dd6877092956a0 |