Explore Workflows

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

Graph Name Retrieved From View
workflow graph gather AML trio outputs

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

Path: definitions/pipelines/aml_trio_cle_gathered.cwl

Branch/Commit ID: 889a077a20c0fdb01f4ed97aa4bc40f920c37a1a
workflow graph Cell Ranger Count (RNA+VDJ)

Cell Ranger Count (RNA+VDJ) Quantifies single-cell gene expression, performs V(D)J contigs assembly and clonotype calling of the sequencing data from a single 10x Genomics library in a combined manner. The results of this workflow are primarily used in either “Single-Cell RNA-Seq Filtering Analysis”, “Single-Cell Immune Profiling Analysis”, or “Cell Ranger Aggregate (RNA, RNA+VDJ)” pipelines.

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

Path: workflows/cellranger-multi.cwl

Branch/Commit ID: fa4f172486288a1a9d23864f1d6962d85a453e16
workflow graph cgpRna_workflow.cwl

https://github.com/cancerit/cgpRna.git

Path: cwls/cgpRna_workflow.cwl

Branch/Commit ID: 6d77a181dc077de726bc78a19d30c22399797312
workflow graph count-lines17-wf.cwl

https://github.com/common-workflow-language/cwl-v1.2.git

Path: tests/count-lines17-wf.cwl

Branch/Commit ID: 7d7986a6e852ca6e3239c96d3a05dd536c76c903
workflow graph FastQC - a quality control tool for high throughput sequence data

FastQC - a quality control tool for high throughput sequence data ===================================== FastQC aims to provide a simple way to do some quality control checks on raw sequence data coming from high throughput sequencing pipelines. It provides a modular set of analyses which you can use to give a quick impression of whether your data has any problems of which you should be aware before doing any further analysis. The main functions of FastQC are: - Import of data from FastQ files (any variant) - Providing a quick overview to tell you in which areas there may be problems - Summary graphs and tables to quickly assess your data - Export of results to an HTML based permanent report - Offline operation to allow automated generation of reports without running the interactive application

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

Path: workflows/fastqc.cwl

Branch/Commit ID: fa4f172486288a1a9d23864f1d6962d85a453e16
workflow graph Subworkflow to allow calling different SV callers which require bam files as inputs

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

Path: definitions/subworkflows/single_sample_sv_callers.cwl

Branch/Commit ID: a28a8077a8c4dbf117d16799807483a2532af3f3
workflow graph 1st-workflow.cwl

https://github.com/cnherrera/testCWL.git

Path: 1st-workflow.cwl

Branch/Commit ID: 8db5b6c3262045d221ad8aa2d3461582b7452ba1
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: cbefc215d8286447620664fb47076ba5d81aa47f
workflow graph Restore contig names

Restore the contig names using the map file

 https://github.com/EBI-Metagenomics/emg-viral-pipeline.git

Path: cwl/src/Tools/FastaRename/fasta_restore_swf.cwl

Branch/Commit ID: b0ed3f07c8faced85609287759596ad83e154977
workflow graph lobSTR-workflow.cwl

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

Path: src/test/resources/cwl/lobstr-v1/lobSTR-workflow.cwl

Branch/Commit ID: b58db216abe618f182edc4dbad7c53b7229ba625