Inferring copy number and genotype in tumour exome data

作者：Kaushalya C. Amarasinghe, Jason Li, Sally M. Hunter, Georgina L. Ryland, Prue A. Cowin, Ian Campbell, Saman Kumara Halgamuge · 发表于：BMC Genomics · 年份：2014 · DOI：10.1186/1471-2164-15-732 · 被引用次数：122 · 研究领域：Cancer Genomics and Diagnostics、Genomic variations and chromosomal abnormalities、Microtubule and mitosis dynamics

BACKGROUND: Using whole exome sequencing to predict aberrations in tumours is a cost effective alternative to whole genome sequencing, however is predominantly used for variant detection and infrequently utilised for detection of somatic copy number variation. RESULTS: We propose a new method to infer copy number and genotypes using whole exome data from paired tumour/normal samples. Our algorithm uses two Hidden Markov Models to predict copy number and genotypes and computationally resolves polyploidy/aneuploidy, normal cell contamination and signal baseline shift. Our method makes explicit detection on chromosome arm level events, which are commonly found in tumour samples. The methods are combined into a package named ADTEx (Aberration Detection in Tumour Exome). We applied our algorithm to a cohort of 17 in-house generated and 18 TCGA paired ovarian cancer/normal exomes and evaluated the performance by comparing against the copy number variations and genotypes predicted using Affymetrix SNP 6.0 data of the same samples. Further, we carried out a comparison study to show that ADTEx outperformed its competitors in terms of precision and F-measure. CONCLUSIONS: Our proposed method, ADTEx, uses both depth of coverage ratios and B allele frequencies calculated from whole exome sequencing data, to predict copy number variations along with their genotypes. ADTEx is implemented as a user friendly software package using Python and R statistical language. Source code and sample dat...