title: Hidden Markov Model-Based CNV Detection Algorithms for Illumina Genotyping Microarrays.
abstract: Somatic alterations in DNA copy number have been well studied in numerous malignancies, yet the role of germline DNA copy number variation in cancer is still emerging. Genotyping microarrays generate allele-specific signal intensities to determine genotype, but may also be used to infer DNA copy number using additional computational approaches. Numerous tools have been developed to analyze Illumina genotype microarray data for copy number variant (CNV) discovery, although commonly utilized algorithms freely available to the public employ approaches based upon the use of hidden Markov models (HMMs). QuantiSNP, PennCNV, and GenoCN utilize HMMs with six copy number states but vary in how transition and emission probabilities are calculated. Performance of these CNV detection algorithms has been shown to be variable between both genotyping platforms and data sets, although HMM approaches generally outperform other current methods. Low sensitivity is prevalent with HMM-based algorithms, suggesting the need for continued improvement in CNV detection methodologies.
authors: Center for Pharmacogenomics and Individualized Therapy, The University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
authors: Eric L
authors: EL
authors: Seiser
authors: Center for Pharmacogenomics and Individualized Therapy, The University of North Carolina at Chapel Hill, Chapel Hill, NC, USA. ; UNC Lineberger Comprehensive Cancer Center, The University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
authors: Federico
authors: F
authors: Innocenti
keywords: copy number variation; genotyping microarray; hidden Markov model
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