Identifying Change-Points in Biological Sequences via Sequential Importance Sampling
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The genomes of complex organisms, including the human genome, are highly structured. This structure takes the form of segmental patterns of variation in various properties and may be caused by the division of genomes into regions of distinct function, by the contingent evolutionary processes that gave rise to genomes, or by a combination of both. Whatever the cause, identifying the change-points between segments is potentially important, as a means of discovering the functional components of a genome, understanding the evolutionary processes involved, and fully describing genomic architecture. One property of genomes that is known to display a segmental pattern of variation is GC content. The GC content of a portion of DNA is the proportion of GC pairs that it contains. Sharp changes in GC content can be observed in human and other genomes. Such change-points may be the boundaries of functional elements or may play a structural role. We model genome sequences as a multiple change-point process, that is, a process in which sequential data are separated into segments by an unknown number of change-points, with each segment supposed to have been generated by a different process. We consider a Sequential Importance Sampling approach to change-point modeling using Monte Carlo simulation to find estimates of change-points as well as parameters of the process on each segment. Numerical experiments illustrate the effectiveness of the approach. We obtain estimates for the locations of change-points in artificially generated sequences and compare the accuracy of these estimates to those obtained via Markov chain Monte Carlo and a well-known method, IsoFinder. We also provide examples with real data sets to illustrate the usefulness of this method.
KeywordsComparative genomics Multiple change-point problem
G. Yu. Sofronov and D. P. Kroese acknowledge the support of an Australian Research Council discovery grant (DP0556631). J. M. Keith would like to acknowledge the support of the Australian Research Council discovery grants (DP0452412, DP0556631) and a National Medical and Health Research Council grant “Statistical methods and algorithms for analysis of high-throughput genetics and genomics platforms” (389892).