Explore Workflows

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

Graph Name Retrieved From View
workflow graph scatter-wf4.cwl#main

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

Path: tests/wf/scatter-wf4.cwl

Branch/Commit ID: ae401a813472ca453a99ad067a5e6fc3bd71112b

Packed ID: main

workflow graph indices-header.cwl

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

Path: metadata/indices-header.cwl

Branch/Commit ID: 10ce6e113f749c7bd725e426445220c3bdc5ddf1

workflow graph inpdir_update_wf.cwl

https://github.com/common-workflow-language/cwl-v1.1.git

Path: tests/inpdir_update_wf.cwl

Branch/Commit ID: 86c46cb397de029e4c91f02cca40fa2b54d22f37

workflow graph mut2.cwl

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

Path: tests/wf/mut2.cwl

Branch/Commit ID: e6c2d955a448225f026a04130443d13661844440

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: ffab5424bb8b5905aecf6f8e2e6387da7f3df562

workflow graph umi duplex alignment fastq workflow

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

Path: definitions/pipelines/umi_duplex_alignment.cwl

Branch/Commit ID: 195b4ab487c939eb32a55d9f78bc1befd100caae

workflow graph count-lines4-wf.cwl

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

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

Branch/Commit ID: e6c2d955a448225f026a04130443d13661844440

workflow graph 811-12.cwl

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

Path: tests/wf/811-12.cwl

Branch/Commit ID: d3c7bd5d6c409e857b98f9034a55952ca95afdb3

workflow graph trim-chipseq-pe.cwl

Runs ChIP-Seq BioWardrobe basic analysis with paired-end input data files.

https://github.com/Barski-lab/workflows.git

Path: workflows/trim-chipseq-pe.cwl

Branch/Commit ID: bc75349ad3a7bdce82b4cd8584501f4d0280bb8d

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: 7eef0294395d83ff0765fce61726a59d71126422