Explore Workflows

View already parsed workflows here or click here to add your own

Graph Name Retrieved From View
workflow graph workflow.cwl

https://github.com/reanahub/reana-demo-root6-roofit.git

Path: workflow/cwl/workflow.cwl

Branch/Commit ID: 2b79f1c4aea6981845647b1ba880832288eaeb88
workflow graph Detect DoCM variants

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

Path: definitions/subworkflows/docm_germline.cwl

Branch/Commit ID: 2e0562a5c3cd7aac24af4c622a5ae68a7fb23a71
workflow graph workflow.cwl

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

Path: flow_dispatch/2blat/workflow.cwl

Branch/Commit ID: 0b58c250e8ab7c5efae29443f08ea74316127041
workflow graph qc_uncollapsed_bam

https://github.com/msk-access/qc_generation.git

Path: access_qc__packed.cwl

Branch/Commit ID: 248e7c3edaff48e1b97a7931d66aa3b23ce97f54

Packed ID: qc_uncollapsed_bam.cwl
workflow graph bgzip and index VCF

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

Path: definitions/subworkflows/bgzip_and_index.cwl

Branch/Commit ID: 43c790e2ee6a0f3f42e40fb4d9a9005eb8de747a
workflow graph SSU-from-tablehits.cwl

https://github.com/EBI-Metagenomics/ebi-metagenomics-cwl.git

Path: tools/SSU-from-tablehits.cwl

Branch/Commit ID: b6d3aaf3fa6695061208c6cdca3d7881cc45400d
workflow graph running cellranger mkfastq and count

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

Path: definitions/subworkflows/cellranger_mkfastq_and_count.cwl

Branch/Commit ID: 43c790e2ee6a0f3f42e40fb4d9a9005eb8de747a
workflow graph readme-assembly-workflow.cwl

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

Path: flow_create_readme/readme-assembly-workflow.cwl

Branch/Commit ID: 7198756b4b1519d102178042924671bd677e9b17
workflow graph wgs alignment and germline variant detection

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

Path: definitions/pipelines/germline_wgs.cwl

Branch/Commit ID: 700e73aaed6db1ad538dd27b2e1709f436ad3edb
workflow graph Cellranger aggr - aggregates data from multiple Cellranger runs

Devel version of Single-Cell Cell Ranger Aggregate ================================================== Workflow calls \"cellranger aggr\" command to combine output files from \"cellranger count\" (the molecule_info.h5 file from each run) into a single feature-barcode matrix containing all the data. When combining multiple GEM wells, the barcode sequences for each channel are distinguished by a GEM well suffix appended to the barcode sequence. Each GEM well is a physically distinct set of GEM partitions, but draws barcode sequences randomly from the pool of valid barcodes, known as the barcode whitelist. To keep the barcodes unique when aggregating multiple libraries, we append a small integer identifying the GEM well to the barcode nucleotide sequence, and use that nucleotide sequence plus ID as the unique identifier in the feature-barcode matrix. For example, AGACCATTGAGACTTA-1 and AGACCATTGAGACTTA-2 are distinct cell barcodes from different GEM wells, despite having the same barcode nucleotide sequence. This number, which tells us which GEM well this barcode sequence came from, is called the GEM well suffix. The numbering of the GEM wells will reflect the order that the GEM wells were provided in the \"molecule_info_h5\" and \"gem_well_labels\" inputs. When combining data from multiple GEM wells, the \"cellranger aggr\" pipeline automatically equalizes the average read depth per cell between groups before merging. This approach avoids artifacts that may be introduced due to differences in sequencing depth. It is possible to turn off normalization or change the way normalization is done through the \"normalization_mode\" input. The \"none\" value may be appropriate if you want to maximize sensitivity and plan to deal with depth normalization in a downstream step.

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

Path: workflows/cellranger-aggr.cwl

Branch/Commit ID: 564156a9e1cc7c3679a926c479ba3ae133b1bfd4