title: Inferring copy number and genotype in tumour exome data.
abstract: 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/.
authors.forename: Kaushalya C
authors.initials: KC
authors.lastname: Amarasinghe
authors.forename: Jason
authors.initials: J
authors.lastname: Li
authors.forename: Sally M
authors.initials: SM
authors.lastname: Hunter
authors.forename: Georgina L
authors.initials: GL
authors.lastname: Ryland
authors.forename: Prue A
authors.initials: PA
authors.lastname: Cowin
authors.forename: Ian G
authors.initials: IG
authors.lastname: Campbell
authors.affiliation: Optimisation and Pattern Recognition group, Mechanical Engineering Department, Melbourne School of Engineering, The University of Melbourne, Parkville, Victoria 3010, Australia. saman@unimelb.edu.au.
authors.forename: Saman K
authors.initials: SK
authors.lastname: Halgamuge
mesh_terms: D000465:Algorithms; D002869:Chromosome Aberrations; D019295:Computational Biology / Q000379:methods; D056915:DNA Copy Number Variations; D059472:Exome; D005260:Female; D023281:Genomics / Q000379:methods; D005838:Genotype; D060005:Genotyping Techniques; D059014:High-Throughput Nucleotide Sequencing; D006801:Humans; D019656:Loss of Heterozygosity; D009369:Neoplasms / Q000235:genetics*; D010051:Ovarian Neoplasms / Q000235:genetics; D020641:Polymorphism, Single Nucleotide; D011123:Polyploidy; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity