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

Abstract

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.

Keywords

• Hidden Markov model
• next-generation sequencing
• aberration detection
• tumor impurity
• aneuploidy