Importance sampling of word patterns in DNA and protein sequences.
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.
Department of Statistics and Applied Probability, National University of Singapore, Singapore, Republic of Singapore.
Hock Peng
HP
Chan
Nancy Ruonan
NR
Zhang
Louis H Y
LH
Chen
D020816:Amino Acid Motifs; D000595:Amino Acid Sequence; D001483:Base Sequence; D055029:Inverted Repeat Sequences; D009010:Monte Carlo Method; D010363:Pattern Recognition, Automated; D056510:Position-Specific Scoring Matrices; D012045:Regulatory Sequences, Nucleic Acid; D017421:Sequence Analysis / Q000379:methods*