Explore Workflows

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

https://github.com/NAL-i5K/Organism_Onboarding.git

Path: flow_dispatch/2working_files/workflow.cwl

Branch/Commit ID: 0ecf492419ddaa015f14a163381948c53b3deea6

https://github.com/common-workflow-language/cwltool.git

Path: cwltool/schemas/v1.0/v1.0/count-lines4-wf.cwl

Branch/Commit ID: 03af16c9df2ee77485d4ab092cd64ae096d2e71c

https://github.com/mskcc/roslin-variant.git

Path: setup/cwl/module-1.cwl

Branch/Commit ID: 3ec29bfdcbc444c6599e484640cf83a7c5d91aaa

The original [BioWardrobe's](https://biowardrobe.com) [PubMed ID:26248465](https://www.ncbi.nlm.nih.gov/pubmed/26248465) **RNA-Seq** basic analysis for a **paired-end** experiment. A corresponded input [FASTQ](http://maq.sourceforge.net/fastq.shtml) file has to be provided. Current workflow should be used only with the paired-end RNA-Seq data. It performs the following steps: 1. Use STAR to align reads from input FASTQ files according to the predefined reference indices; generate unsorted BAM file and alignment statistics file 2. Use fastx_quality_stats to analyze input FASTQ files and generate quality statistics files 3. Use samtools sort to generate coordinate sorted BAM(+BAI) file pair from the unsorted BAM file obtained on the step 1 (after running STAR) 4. Generate BigWig file on the base of sorted BAM file 5. Map input FASTQ files to predefined rRNA reference indices using Bowtie to define the level of rRNA contamination; export resulted statistics to file 6. Calculate isoform expression level for the sorted BAM file and GTF/TAB annotation file using GEEP reads-counting utility; export results to file

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

Path: workflows/rnaseq-pe-dutp.cwl

Branch/Commit ID: d6ec0dee61ef65a110e10141bde1a79332a64ab0

**Workflow for Illumina paired read quality control, trimming and filtering.**<br /> Multiple paired datasets will be merged into single paired dataset.<br /> Summary: - FastQC on raw data files<br /> - fastp for read quality trimming<br /> - BBduk for phiX and (optional) rRNA filtering<br /> - Kraken2 for taxonomic classification of reads (optional)<br /> - BBmap for (contamination) filtering using given references (optional)<br /> - FastQC on filtered (merged) data<br /> **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> WorkflowHub: https://workflowhub.eu/projects/16/workflows?view=default

https://git.wur.nl/unlock/cwl.git

Path: cwl/workflows/workflow_illumina_quality.cwl

Branch/Commit ID: b9097b82e6ab6f2c9496013ce4dd6877092956a0

https://github.com/common-workflow-language/cwltool.git

Path: tests/wf/811.cwl

Branch/Commit ID: aec33fcfa3459a90cbba8c88ebb991be94d21429