Explore Workflows

https://github.com/arvados/bh20-seq-resource.git

Path: workflows/pangenome-generate/pangenome-generate.cwl

Branch/Commit ID: fdb1b012fc04ee07f401541e181e28fe442c9454

https://github.com/nal-i5k/organism_onboarding.git

Path: flow_dispatch/2other_species/workflow.cwl

Branch/Commit ID: 0ecf492419ddaa015f14a163381948c53b3deea6

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.

https://github.com/icgc-tcga-pancancer/pcawg-oxog-filter.git

Path: zip_and_index_vcf.cwl

Branch/Commit ID: 123a3151d35f98e442e703d903dc3e1d72f3c4b0

This is a subworkflow - this is not meant to be run as a stand-alone workflow!

https://github.com/icgc-tcga-pancancer/pcawg-oxog-filter.git

Path: oxog_sub_wf.cwl

Branch/Commit ID: 123a3151d35f98e442e703d903dc3e1d72f3c4b0

Workflow runs homer-make-tag-directory.cwl tool using scatter for the following inputs - bam_file - fragment_size - total_reads `dotproduct` is used as a `scatterMethod`, so one element will be taken from each array to construct each job: 1) bam_file[0] fragment_size[0] total_reads[0] 2) bam_file[1] fragment_size[1] total_reads[1] ... N) bam_file[N] fragment_size[N] total_reads[N] `bam_file`, `fragment_size` and `total_reads` arrays should have the identical order.

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

Path: tools/heatmap-prepare.cwl

Branch/Commit ID: 799575ce58746813f066a665adeacdda252d8cab

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

Path: subworkflows/allele-process-reference.cwl

Branch/Commit ID: e627079d8431e4f1f1c7531af1ca2e7dcc684b90

https://github.com/mscheremetjew/workflow-is-cwl.git

Path: workflows/TranscriptomeAssembly-wf.paired-end.cwl

Branch/Commit ID: e9bbe2917384efc75ba067db23612bc8e22f3f06

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/Barski-lab/workflows.git

Path: tools/bam-bedgraph-bigwig.cwl

Branch/Commit ID: 64e85970dbecba89c3380ab285c108d221e76fe6

https://gitlab.bsc.es/lrodrig1/structuralvariants_poc.git

Path: structuralvariants/cwl/abstract_operations/subworkflows/indexing_bed.cwl

Branch/Commit ID: 82e533a98a763a258bd841ed0032c79445478d56

What is MAnorm? -------------- MAnorm is a robust model for quantitative comparison of ChIP-Seq data sets of TFs (transcription factors) or epigenetic modifications and you can use it for: * Normalization of two ChIP-seq samples * Quantitative comparison (differential analysis) of two ChIP-seq samples * Evaluating the overlap enrichment of the protein binding sites(peaks) * Elucidating underlying mechanisms of cell-type specific gene regulation How MAnorm works? ---------------- MAnorm uses common peaks of two samples as a reference to build the rescaling model for normalization, which is based on the empirical assumption that if a chromatin-associated protein has a substantial number of peaks shared in two conditions, the binding at these common regions will tend to be determined by similar mechanisms, and thus should exhibit similar global binding intensities across samples. The observed differences on common peaks are presumed to reflect the scaling relationship of ChIP-Seq signals between two samples, which can be applied to all peaks. What do the inputs mean? ---------------- ### General **Experiment short name/Alias** * short name for you experiment to identify among the others **ChIP-Seq SE sample 1** * previously analyzed ChIP-Seq single-read experiment to be used as Sample 1 **ChIP-Seq SE sample 2** * previously analyzed ChIP-Seq single-read experiment to be used as Sample 2 **Genome** * Reference genome to be used for gene assigning ### Advanced **Reads shift size for sample 1** * This value is used to shift reads towards 3' direction to determine the precise binding site. Set as half of the fragment length. Default 100 **Reads shift size for sample 2** * This value is used to shift reads towards 5' direction to determine the precise binding site. Set as half of the fragment length. Default 100 **M-value (log2-ratio) cutoff** * Absolute M-value (log2-ratio) cutoff to define biased (differential binding) peaks. Default: 1.0 **P-value cutoff** * P-value cutoff to define biased peaks. Default: 0.01 **Window size** * Window size to count reads and calculate read densities. 2000 is recommended for sharp histone marks like H3K4me3 and H3K27ac, and 1000 for TFs or DNase-seq. Default: 2000

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

Path: workflows/manorm-se.cwl

Branch/Commit ID: b1a5dabeeeb9079b30b2871edd9c9034a1e00c1c