MOTIVATION: Normalization is critical in DNA copy number analysis. We propose a new method to correctly identify two-copy probes from the genome to obtain representative references for normalization in single nucleotide polymorphism arrays. The method is based on a two-state Hidden Markov Model. Unlike most currently available methods in the literature, the proposed method does not need to assume that the percentage of two-copy state probes is dominant in the genome, as long as there do exist two-copy probes. RESULTS: The real data analysis and simulation study show that the proposed algorithm is successful in that (i) it performs as well as the current methods (e.g. CGHnormaliter and popLowess) for samples with dominant two-copy states and outperforms these methods for samples with less dominant two-copy states; (ii) it can identify the copy-neutral loss of heterozygosity; and (iii) it is efficient in terms of the computational time used. AVAILABILITY: R scripts are available at http://publichealth.lsuhsc.edu/PAIR.html.
MOTIVATION: Normalization is critical in DNA copy number analysis. We propose a new method to correctly identify two-copy probes from the genome to obtain representative references for normalization in single nucleotide polymorphism arrays. The method is based on a two-state Hidden Markov Model. Unlike most currently available methods in the literature, the proposed method does not need to assume that the percentage of two-copy state probes is dominant in the genome, as long as there do exist two-copy probes. RESULTS: The real data analysis and simulation study show that the proposed algorithm is successful in that (i) it performs as well as the current methods (e.g. CGHnormaliter and popLowess) for samples with dominant two-copy states and outperforms these methods for samples with less dominant two-copy states; (ii) it can identify the copy-neutral loss of heterozygosity; and (iii) it is efficient in terms of the computational time used. AVAILABILITY: R scripts are available at http://publichealth.lsuhsc.edu/PAIR.html.
Authors: Guillem Rigaill; Philippe Hupé; Anna Almeida; Philippe La Rosa; Jean-Philippe Meyniel; Charles Decraene; Emmanuel Barillot Journal: Bioinformatics Date: 2008-02-05 Impact factor: 6.937
Authors: Christina Curtis; Andy G Lynch; Mark J Dunning; Inmaculada Spiteri; John C Marioni; James Hadfield; Suet-Feung Chin; James D Brenton; Simon Tavaré; Carlos Caldas Journal: BMC Genomics Date: 2009-12-08 Impact factor: 3.969
Authors: Bart P P van Houte; Thomas W Binsl; Hannes Hettling; Walter Pirovano; Jaap Heringa Journal: BMC Genomics Date: 2009-08-26 Impact factor: 3.969
Authors: Manuela Fanciulli; Penny J Norsworthy; Enrico Petretto; Rong Dong; Lorraine Harper; Lavanya Kamesh; Joanne M Heward; Stephen C L Gough; Adam de Smith; Alexandra I F Blakemore; Philippe Froguel; Catherine J Owen; Simon H S Pearce; Luis Teixeira; Loic Guillevin; Deborah S Cunninghame Graham; Charles D Pusey; H Terence Cook; Timothy J Vyse; Timothy J Aitman Journal: Nat Genet Date: 2007-05-21 Impact factor: 38.330