Inferring copy number and genotype in tumour exome data

BMC Genomics. 2014 Aug 28;15(1):732. 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

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Algorithms
  • Chromosome Aberrations
  • Computational Biology / methods
  • DNA Copy Number Variations*
  • Exome*
  • Female
  • Genomics / methods
  • Genotype*
  • Genotyping Techniques
  • High-Throughput Nucleotide Sequencing
  • Humans
  • Loss of Heterozygosity
  • Neoplasms / genetics*
  • Ovarian Neoplasms / genetics
  • Polymorphism, Single Nucleotide
  • Polyploidy
  • Reproducibility of Results
  • Sensitivity and Specificity