Inferring copy number and genotype in tumour exome data

• Published in: BMC genomics Vol. 15; no. 1; p. 732
• Main Authors: Amarasinghe, Kaushalya C, Li, Jason, Hunter, Sally M, Ryland, Georgina L, Cowin, Prue A, Campbell, Ian G, Halgamuge, Saman K
• Format: Journal Article
• Language: English
• Published: London BioMed Central 28.08.2014; BioMed Central Ltd; Springer Nature B.V
• Subjects: Algorithms; Analysis; Animal Genetics and Genomics; Bioinformatics; Biomedical and Life Sciences; Cancer; Chromosome Aberrations; Chromosomes; Colleges & universities; Computational Biology - methods; Deoxyribonucleic acid; DNA; DNA Copy Number Variations; DNA sequencing; Exome; Female; Genes; Genetic aspects; Genomes; Genomics; Genomics - methods; Genotype; Genotype & phenotype; Genotypes; Genotyping Techniques; High-Throughput Nucleotide Sequencing; Human and rodent genomics; Humans; Laboratories; Life Sciences; Loss of Heterozygosity; Markov chains; Markov processes; Methodology; Methodology Article; Methods; Microarrays; Microbial Genetics and Genomics; Neoplasms - genetics; Noise; Nucleotide sequencing; Ovarian cancer; Ovarian Neoplasms - genetics; Plant Genetics and Genomics; Polymorphism, Single Nucleotide; Polyploidy; Proteomics; Ratios; Reproducibility of Results; Sensitivity and Specificity; Tumors; Discrete Wavelet Transformation; Single Nucleotide Polymorphism Locus; Hide State; Hide Markov Model; Whole Exome Sequencing
• ISSN: 1471-2164; 1471-2164
• DOI: 10.1186/1471-2164-15-732

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 data are freely available under GNU license (GPLv3) at http://adtex.sourceforge.net/ .