%0 Journal Article
%T A response to Yu et al. "A forward-backward fragment assembling algorithm for the identification of genomic amplification and deletion breakpoints using high-density single nucleotide polymorphism (SNP) array", BMC Bioinformatics 2007, 8: 145
%A Oscar M Rueda
%A Ramon Diaz-Uriarte
%J BMC Bioinformatics
%D 2007
%I BioMed Central
%R 10.1186/1471-2105-8-394
%X We rerun the analysis in Yu et al. using appropriate settings for both the Markov Chain Monte Carlo iterations and the reference level. Additionally, to show how easy it is to obtain answers to additional specific questions, we have added a new analysis targeted specifically to the detection of breakpoints.The reanalysis shows that the performance of our method is comparable to that of the other methods analyzed. In addition, we can provide probabilities of a given spot being a breakpoint, something unique among the methods examined.Markov Chain Monte Carlo methods require using a sufficient number of iterations before they can be assumed to yield samples from the distribution of interest. Running our method with too small a number of iterations cannot be representative of its performance. Moreover, our analysis shows how our original approach can be easily adapted to answer specific additional questions (e.g., identify edges).The recent paper by Yu et al. [1] proposes a new method for the analysis (segmentation) of high density single nucleotide polymorphism (SNP) arrays to detect copy number changes (CNAs) in genomic DNA. Their approach has been designed to be highly sensitive for edge detection and is tailored to SNP arrays. In their paper, Yu et al. compare the performance of their approach with that of several alternative methods initially developed for the analysis of array-CGH (aCGH) data. The methods compared include Circular Binary Segmentation [2], GLAD [3], CGHseg [4] ¨C though not in the original formulation of their authors, as the recommended adaptive penalization of [4] is not used¨C, and three Hidden Markov Model approaches: a homogenous one [5], the non-homogeneous one of Marioni et al. [6], and RJaCGH, our own non-homogeneous HMM using Markov Chain Monte Carlo (MCMC) with Reversible Jump [7].The different aCGH technologies, from BAC-based aCGH to oligonucleotide aCGH (oaCGH), including Affymetrix SNP arrays, differ in several ways, both in terms of c
%U http://www.biomedcentral.com/1471-2105/8/394