StemCNV-check

About

StemCNV-check is a tool written to simplify copy number variation (CNV) analysis of SNP array data, specifically for quality control of (pluripotent) stem cell lines.

StemCNV-check uses snakemake to run the complete analysis from raw data (.idat) up report generation for all defined samples with a single command. Samples need to be defined in a (tabular) sample table and the workflow settings are defined through a yaml file.

Features

StemCNV-check allows generation of read-to-analyse reports from raw SNP-array data in a single command, where possible hPSC samples are always compared to a (parental) reference sample and results are annotated with additional information to ease interpretation of the data. The report contains and summarises all intermediate analysis steps:

• Summary of quality measures for the sample, including comparison to predefined thresholds
• Sorted list of CNV calls, split by de-novo and reference matching calls
  • Sorting uses the Check-Score from our upcoming manuscript and combines contributions from CNV size and copynumber as well as additions from annotation from overlapping stem cell hotspots, cancer driver genes, predicted dosage sensitive genes and other gene annotations.
  • The top CNVs have detailed images and tables listing overlapping features and genes with link-outs to further resources
  • Sample specific regions of interest are also displayed in a separate section reargdless of CNV status with full details
• Sorted list of SNVs that affect protein sequences, split by de-novo and reference matching variants
  • Sorting takes known stem cell hotspots, sample specific regions of interest, SNP quality and severity of predicted protein changes
• Gnome wide overview plots for visual inspection of large aberrations, including side-by-side comparison to reference sample
• Sample identity comparison based on clustering by SNP-genotypes

Documentation

Please consult our documentation on read-the-docs for detailed instructions on installation, usage, interpretation, troubleshooting and technical implementation of StemCNV-check.

Installation

StemCNV-check requires a linux environment (or WSL on windows) and a working conda (or mamba) installation. Follow the recommended instructions to install conda or mamba.

It is recommended to install StemCNV-check through the bioconda channel. If you do not use conda for other things omitting the environment name and installing into your base environment may be an option.

conda install bioconda::stemcnv-check [-n stemcnv-check]

Usage

For detailed explanations on installation, setup and usage please consult our documentation. In brief:

• Setup your config file and sample table, i.e.: stemcnv-check setup-files
• Once only, use the stemcnv-check make-staticdata to create static data for your specific array
• Start the analysis with the run command: stemcnv-check run

Example data (legacy)

We provide example data, so you can easily test StemCNV-check for yourself, please consult the documentation for details. This data is primarily available through zenodo, however a legacy version through git-lfs also still exists.

Install git lfs and pull test data:

• sudo apt-get install git-lfs
• git lfs fetch
• git lfs checkout

Run the example data:

• cd example_data
• stemcnv-check make-staticdata
• stemcnv-check run