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

Author

Istiadi, Muhammad Abrar ; Kusuma, Wisnu Ananta ; Tasma, I. Made

Author_Institution

Dept. of Comput. Sci., Bogor Agric. Univ., Bogor, Indonesia

fYear

2014

Firstpage

339

Lastpage

343

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.

Keywords

bioinformatics; decision trees; genetics; learning (artificial intelligence); pattern classification; sampling methods; C4.5 decision tree algorithm; SNP; decision tree classifier; false-positive rate; machine learning method; next-generation sequencing data; random undersampling; single nucleotide polymorphism discovery; whole-genome NGS data; Bioinformatics; Biological cells; Computational modeling; Genomics; Sequential analysis; Support vector machines; C4.5; decision tree; next-generation sequencing; single nucleotide polymorphism;

fLanguage

English

Publisher

ieee

Conference_Titel

Advanced Computer Science and Information Systems (ICACSIS), 2014 International Conference on

Type

conf

DOI

10.1109/ICACSIS.2014.7065832

Filename

7065832