Application of decision tree classifier for single nucleotide polymorphism discovery from next-generation sequencing data

Abstract

Single Nucleotide Polymorphism (SNP) is the most abundant form of genetic variation and proven to be advantageous in diverse genetic-related studies. However, accurate determination of true SNPs from next-generation sequencing (NGS) data is a challenging task due to high error rates of NGS. To overcome this problem, we applied a machine learning method using C4.5 decision tree algorithm to discover SNPs from whole-genome NGS data. In addition, we conducted random undersampling to deal with the imbalanced data. The result shows that the proposed method is able to identify most of the true SNPs with more than 90% recall, but still suffers from a high rate of false-positives.