Integrated Modeling of GC-Content, Mappability, Tumor Impurity and Aneuploidy for Accurate Detection of Genomic Aberrations

IEEE Access

Minghui Wang¹  Ao Li¹  Liang Zou²  Zhenhua Yu³  Xuehong Sun³  Peng Zhang³ 

[1] Centre for Biomedical Engineering, School of Information Science and Technology, University of Science and Technology of China, Hefei, China;National Engineering Research Center for Agro-Ecological Big Data Analysis and Application, Anhui University, Hefei, China;School of Information Engineering, Ningxia University, Yinchuan, China;

关键词: Hidden Markov model;    next-generation sequencing;    aberration detection;    tumor impurity;    aneuploidy;   

DOI  :  10.1109/ACCESS.2018.2877253

来源: DOAJ

【 摘 要 】

Next-generation sequencing has been widely used in cancer-focused studies for comprehensive landscape of tumor genomes. Detection of genomic aberrations is one of the focal points in this area. Analysis of tumor sequencing data is usually complicated by several critical issues, such as GC-content bias, mappability bias, tumor impurity, and aneuploidy. Efficient computational methods are still in great demand for comprehensively addressing these issues. We introduce GPHMM-SEQ, a novel algorithm for inferring tumor impurity and ploidy as well as detecting copy number alterations and loss of heterozygosity from paired tumor-normal samples. Read depth signals derived from sequencing data are analyzed using a novel hidden Markov model that employs integrated representation of GC-content bias, mappability bias, tumor impurity, and aneuploidy. The evaluation on simulated and real tumor sequencing data demonstrates GPHMM-SEQ has the superior performance compared to existing methods.

【 授权许可】

Unknown