Please use this identifier to cite or link to this item: https://doi.org/10.1089/cmb.2008.0233
Title: Importance sampling of word patterns in DNA and protein sequences
Authors: Chan, H.P. 
Zhang, N.R.
Chen, L.H.Y. 
Keywords: importance sampling
Monte Carlo
motifs
palindromes
position-specific weight matrices
Issue Date: 1-Dec-2010
Citation: Chan, H.P., Zhang, N.R., Chen, L.H.Y. (2010-12-01). Importance sampling of word patterns in DNA and protein sequences. Journal of Computational Biology 17 (12) : 1697-1709. ScholarBank@NUS Repository. https://doi.org/10.1089/cmb.2008.0233
Abstract: Monte Carlo methods can provide accurate p-value estimates of word counting test statistics and are easy to implement. They are especially attractive when an asymptotic theory is absent or when either the search sequence or the word pattern is too short for the application of asymptotic formulae. Naive direct Monte Carlo is undesirable for the estimation of small probabilities because the associated rare events of interest are seldom generated. We propose instead efficient importance sampling algorithms that use controlled insertion of the desired word patterns on randomly generated sequences. The implementation is illustrated on word patterns of biological interest: palindromes and inverted repeats, patterns arising from position-specific weight matrices (PSWMs), and co-occurrences of pairs of motifs. © Mary Ann Liebert, Inc.
Source Title: Journal of Computational Biology
URI: http://scholarbank.nus.edu.sg/handle/10635/103403
ISSN: 10665277
DOI: 10.1089/cmb.2008.0233
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