Explore Workflows

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

Path: MoveData-workflow.cwl

Branch/Commit ID: 677d79c721ad5f7a7e09b693d7f3fe2da70826e2

https://github.com/genome/analysis-workflows.git

Path: definitions/subworkflows/germline_filter_vcf.cwl

Branch/Commit ID: a59a803e1809a8fbfabca6b8962a8ad66dd01f1d

https://github.com/genome/analysis-workflows.git

Path: definitions/pipelines/detect_variants_nonhuman.cwl

Branch/Commit ID: 35e6b3ef71b4a2a9caba1dbd5dc424a8809bcc0a

Sequence reads are first cleaned from adapters and transformed into fully bisulfite-converted forward (C->T) and reverse read (G->A conversion of the forward strand) versions, before they are aligned to similarly converted versions of the genome (also C->T and G->A converted). Sequence reads that produce a unique best alignment from the four alignment processes against the bisulfite genomes (which are running in parallel) are then compared to the normal genomic sequence and the methylation state of all cytosine positions in the read is inferred. A read is considered to align uniquely if an alignment has a unique best alignment score (as reported by the AS:i field). If a read produces several alignments with the same number of mismatches or with the same alignment score (AS:i field), a read (or a read-pair) is discarded altogether. On the next step we extract the methylation call for every single C analysed. The position of every single C will be written out to a new output file, depending on its context (CpG, CHG or CHH), whereby methylated Cs will be labelled as forward reads (+), non-methylated Cs as reverse reads (-). The output of the methylation extractor is then transformed into a bedGraph and coverage file. The bedGraph counts output is then used to generate a genome-wide cytosine report which reports the number on every single CpG (optionally every single cytosine) in the genome, irrespective of whether it was covered by any reads or not. As this type of report is informative for cytosines on both strands the output may be fairly large (~46mn CpG positions or >1.2bn total cytosine positions in the human genome).

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

Path: workflows/bismark-methylation-se.cwl

Branch/Commit ID: 09267e79fd867aa68a219c69e6db7d8e2e877be2

Motif Finding with HOMER with custom 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. 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-bg.cwl

Branch/Commit ID: 4ab9399a4777610a579ea2c259b9356f27641dcc

**Workflow for Illumina paired read quality control, trimming and filtering.**<br /> Multiple read pairs will be merged into single paired dataset.<br /> Summary: - FastQC on raw data files<br /> - fastp for read quality trimming<br /> - Kraken2 for taxonomic classification of reads<br /> - BBduk for phix and rRNA filtering<br /> - BBmap for (contamination) filtering using given references<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: 2242521957bb07fc589d6bb07046f6a166bc975a

Workflow for Metagenomics from raw reads to annotated bins.<br> Summary - MetaBAT2 (binning) - CheckM (bin completeness and contamination) - GTDB-Tk (bin taxonomic classification) - BUSCO (bin completeness) **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_metagenomics_binning.cwl

Branch/Commit ID: 2242521957bb07fc589d6bb07046f6a166bc975a

**Workflow for nanopore read quality control and contamination filtering.** - FastQC before filtering (read quality control) - Kraken2 taxonomic read classification - Minimap2 read filtering based on given references - FastQC after filtering (read quality control) **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_nanopore_quality.cwl

Branch/Commit ID: 2242521957bb07fc589d6bb07046f6a166bc975a

Wrapped in a workflow SoupX tool for easy access to Cell Ranger pipeline compressed outputs.

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

Path: tools/soupx-subworkflow.cwl

Branch/Commit ID: cbefc215d8286447620664fb47076ba5d81aa47f

https://github.com/ncbi/pgap.git

Path: task_types/tt_extract_gencoll_ids.cwl

Branch/Commit ID: 90a321ecf2d049330bcf0657cc4d764d2c3f42dd