The power of detecting enriched patterns: an HMM approach.
The identification of binding sites of transcription factors (TF) and other regulatory regions, referred to as motifs, located in a set of molecular sequences is of fundamental importance in genomic research. Many computational and experimental approaches have been developed to locate motifs. The set of sequences of interest can be concatenated to form a long sequence of length n. One of the successful approaches for motif discovery is to identify statistically over- or under-represented patterns in this long sequence. A pattern refers to a fixed word W over the alphabet. In the example of interest, W is a word in the set of patterns of the motif. Despite extensive studies on motif discovery, no studies have been carried out on the power of detecting statistically over- or under-represented patterns Here we address the issue of how the known presence of random instances of a known motif affects the power of detecting patterns, such as patterns within the motif. Let N(W)(n) be the number of possibly overlapping occurrences of a pattern W in the sequence that contains instances of a known motif; such a sequence is modeled here by a Hidden Markov Model (HMM). First, efficient computational methods for calculating the mean and variance of N(W)(n) are developed. Second, efficient computational methods for calculating parameters involved in the normal approximation of N(W)(n) for frequent patterns and compound Poisson approximation of N(W)(n) for rare patterns are developed. Third, an easy to use web program is developed to calculate the power of detecting patterns and the program is used to study the power of detection in several interesting biological examples.
Zhai
Zhiyuan
Z
School of Mathematics, Shandong University, Jinan, Shandong, PR China.
Ku
Shih-Yen
SY
Luan
Yihui
Y
Reinert
Gesine
G
Waterman
Michael S
MS
Sun
Fengzhu
F
D001482:Base Composition / Q000235:genetics; D001483:Base Sequence; D018899:CpG Islands / Q000235:genetics; D020407:Internet; D008390:Markov Chains; D009716:Numerical Analysis, Computer-Assisted; D010363:Pattern Recognition, Automated / Q000379:methods*; D016012:Poisson Distribution; D017422:Sequence Analysis, DNA / Q000379:methods*