Explore Workflows

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

Graph Name Retrieved From View
workflow graph allele-process-reference.cwl

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

Path: subworkflows/allele-process-reference.cwl

Branch/Commit ID: 378f693ebfb3edf9f589007e366fec1195ec1464
workflow graph Vcf concordance evaluation workflow

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

Path: definitions/subworkflows/vcf_eval_concordance.cwl

Branch/Commit ID: 174f3b239018328cec1d821947438b457552724c
workflow graph umi molecular alignment workflow

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

Path: definitions/subworkflows/molecular_alignment.cwl

Branch/Commit ID: ece70ac30cd87100a70f7dc64d08fa72724e9416
workflow graph EMG QC workflow, (paired end version). Benchmarking with MG-RAST expt.

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

Path: workflows/emg-qc-paired.cwl

Branch/Commit ID: c34db66a79cec3b66a0f1be5e499eef88db5a9ed
workflow graph Unaligned BAM to BQSR

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

Path: definitions/subworkflows/bam_to_bqsr.cwl

Branch/Commit ID: 3b6d0475c80f5e452793a46a38ee188742b86595
workflow graph umi molecular alignment workflow

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

Path: definitions/subworkflows/molecular_qc.cwl

Branch/Commit ID: ece70ac30cd87100a70f7dc64d08fa72724e9416
workflow graph kmer_gc_extract_wnode

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

Path: task_types/tt_kmer_gc_extract_wnode.cwl

Branch/Commit ID: a539d600357a48a558daf43fc41a89aae79f9e86
workflow graph search.cwl#main

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

Path: v1.0/v1.0/search.cwl

Branch/Commit ID: 4fe434e969c93c94b690ba72db295d9d52a6f576

Packed ID: main
workflow graph Exome QC workflow

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

Path: definitions/subworkflows/qc_exome_no_verify_bam.cwl

Branch/Commit ID: 97572e3a088d79f6a4166385f79e79ea77b11470
workflow graph PCA - Principal Component Analysis

Principal Component Analysis --------------- Principal component analysis (PCA) is a statistical procedure that uses an orthogonal transformation to convert a set of observations of possibly correlated variables (entities each of which takes on various numerical values) into a set of values of linearly uncorrelated variables called principal components. The calculation is done by a singular value decomposition of the (centered and possibly scaled) data matrix, not by using eigen on the covariance matrix. This is generally the preferred method for numerical accuracy.

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

Path: workflows/pca.cwl

Branch/Commit ID: 4360fb2e778ecee42e5f78f83b78c65ab3a2b1df