Installation

MCNV2 is an R package that provides a Shiny application and Python-backed tools for Mendelian CNV validation and annotation.

Python is used for CNV annotation and Mendelian classification. bedtools is required for genomic overlap calculations.

Note

MCNV2 has been tested on macOS and Linux. On Windows, we recommend using WSL (Windows Subsystem for Linux).

1) Install MCNV2 (R)

if (!require("devtools")) install.packages("devtools")

devtools::install_github(
  "JacquemontLab/MCNV2-Mendelian-CNV-Validation",
  dependencies = TRUE
)

Alternative: Install from local tarball

install.packages("/path/to/MCNV2_0.1.0.tar.gz", repos = NULL, type = "source")

2) Python requirements

MCNV2 uses a Python virtual environment to ensure reproducibility.

Create and configure the environment:

library(MCNV2)
# Creates 'r-MCNV2' virtualenv and installs dependencies (polars, etc.)
MCNV2::setup_python_env(envname = "r-MCNV2")

Dependencies are defined in: inst/python/requirements.txt

Activate the environment:

library(reticulate)
use_virtualenv("r-MCNV2", required = TRUE)

Verify configuration:

py_config()

Optional: check that key packages are available:

py_run_string("import polars; print(polars.__version__)")

3) Install bedtools

bedtools is required for genomic overlap calculations.

macOS (Homebrew):

brew install bedtools

Ubuntu/Debian:

sudo apt-get install bedtools

Conda (any platform):

conda install -c bioconda bedtools

Verify installation:

bedtools --version

4) Launch MCNV2

After completing the installation:

library(reticulate)
use_virtualenv("r-MCNV2", required = TRUE)

library(MCNV2)
MCNV2::launch(
  bedtools_path = Sys.which("bedtools"),
  results_dir = "~/mcnv2_results"
)

This will open the interactive Shiny application for CNV validation and annotation.

Tip

For batch processing and reproducible pipelines, see the CLI tutorial.