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

• Published in: IEEE access Vol. 6; pp. 64096 - 64106
• Main Authors: Yu, Zhenhua, Li, Ao, Zou, Liang, Sun, Xuehong, Wang, Minghui, Zhang, Peng
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2018; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: aberration detection; Algorithms; aneuploidy; Bias; Bioinformatics; Genomes; Genomics; Hidden Markov model; Hidden Markov models; Impurities; Markov chains; Next generation networking; next-generation sequencing; Sequential analysis; tumor impurity; Tumors
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2018.2877253

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.