ReinVar: A model-free paradigm-based reinforcement learning approach to detect copy number variation.

Abstract

Copy number variation (CNV) is one of the most imperative forms of structural variations that can span over the coding and non-coding regulatory regions of an individual's genome. Copy number variations (CNV) can significantly impact the genotype and phenotype traits by altering the gene dosage, consequently affecting the gene expression landscape concerning various cellular functions and are the cause behind complex diseases in an individual. Exceptionally fast advancement in Next Generation Sequencing (NGS) technology has led to massive growth of DNA-seq data, which contains both Whole Genome Sequence (WGS) and targeted Exome Sequence data of various species including H.sapiens, and precise detection of the DNA region affected by CNV enables the copy number profiling of a genome, thereby understanding our genome. This work has proposed a methodology named ReinVar, which can accurately determine and analyze the underlying copy number profile of the whole genome by adopting a model-free reinforcement learning paradigm. The methodology involves a novel approach to model the problem of identifying CNV as a Markov decision process (MDP), followed by determination of CNV under Reinforcement Learning framework. ReinVar also adopted a Map-Reduce programming paradigm to provide a big data solution to address the issue of exponential growth of NGS read sequence data. ReinVar has shown strong performance in detecting CNV gains and losses across diverse ethnic groups, with a high number of shared variant calls. ReinVar's ability to accurately identify both CNV gains and losses, coupled with consistent detection across ethnic groups and strong ROC characteristics, underscores ReinVar's effectiveness as a robust and sensitive CNV detection method.