Date of this Version
Journal of Computational Biology
The use of Monte Carlo evaluation to compute p-values of pattern counting test statistics is especially attractive when an asymptotic theory is absent or when the search sequence or the word pattern is too short for an asymptotic formula to be accurate. The drawback of applying Monte Carlo simulations directly is its inefficiency when p-values are small, which precisely is the situation of importance. In this paper, we provide a general importance sampling algorithm for efficient Monte Carlo evaluation of small p-values of pattern counting test statistics and apply it on word patterns of biological interest, in particular palindromes and inverted repeats, patterns arising from position specific weight matrices, as well as co-occurrences of pairs of motifs. We also show that our importance sampling technique satisfies a log efficient criterion.
Final publication is available from Mary Ann Liebert, Inc., publishers http://dx.doi.org/10.1089/cmb.2008.0233.
importance sampling, biological sequence analysis, motif analysis
Chan, H., Zhang, N. R., & Chen, L. Y. (2010). Importance Sampling of Word Patterns in DNA and Protein Sequences. Journal of Computational Biology, 17 (12), 1697-1709. http://dx.doi.org/10.1089/cmb.2008.0233
Date Posted: 27 November 2017
This document has been peer reviewed.