ISV (Intepretation of Structural Variants)

Structural variants are regional genome mutations that have the potential to affect the phenotype of an individual. The prediction of their pathogenicity is an intricate problem.

Here we show that the pathogenicity of Copy Number Variants (CNVs) is encoded in features describing the CNV as a whole (such as number of gene elements, regulatory regions or known overlapped CNVs).

With carefully selected model hyperparameters, one can achieve more than 99% accuracy for both copy number loss and copy number gain variants.

---

Annotation and Prediction of novel CNVs

The annotation, prediction of pathogenicity as well as calculation of shap values for novel cnvs can be easily done using the isv python package: https://pypi.org/project/isv/

or as a command line tool. Follow instructions at https://github.com/tsladecek/isv_package for more info

---

Project directory Structure

• The data/ folder contains raw data that was used for training the models and for the evaluation
  • in the root of data/ folder one can find annotated cnvs from clinvar, separated into several datasets (train, validation, test, test-long, test-multiple) for each cnv type (loss and gain)
  • annotations by annotsv and classifycnv are inside of separate directories under the same name
  • annotated gnomad dataset, microdeletions and microduplications, genome cnvs and five studied syndromes are under directory data/evaludation_data
• The results/ directory contains trained models with their gridsearch results as well as other tables
• The plots/ directory contains main and supplementary figures
• The scripts/ directory contains scripts for generating the results and training of models
• The rules/ directory contains snakemake definitions, which are collected by the Snakefile at the root of the repository

---

To reproduce entire workflow, except of the circos plot and stains plot, run:

1. Create conda environment

• we recommend using conda for best chance of reproducibility. The versions of all of the packages are listed in the environment.yml file
• The scripts will likely only work on a linux machine

conda env create --file environment.yml 

conda activate ISV

2. Train models and generate all plots and tables

snakemake --cores <number of cores>

---

To reproduce the circos plot and the stains comparison plot see scripts/plots/cnvs_circular.Rmd and scripts/plots/chromosome_stains_study.Rmd

These scripts are written in R language and require tidyverse and circlize packages to be installed.

---

If you mention or use the ISV tool, please cite our article

https://www.nature.com/articles/s41598-021-04505-z