SEARCHPATTOOL: a new method for mining the most specific frequent patterns for binding sites with application to prokaryotic DNA sequences
1 Research Technology Branch, National Institute of Allergy and Infectious Diseases, National Institutes of Health, 9000 Rockville Pike, Blg50/R5505, Bethesda, MD 20892, USA
2 Biostatistics Research Branch, Office of Clinical Research, National Institute of Allergy and Infectious Diseases, National Institutes of Health, 6700B Rockledge Dr. MSC 7609, Bethesda, MD 20892-7609, USA
BMC Bioinformatics 2007, 8:354 doi:10.1186/1471-2105-8-354Published: 20 September 2007
Computational methods to predict transcription factor binding sites (TFBS) based on exhaustive algorithms are guaranteed to find the best patterns but are often limited to short ones or impose some constraints on the pattern type. Many patterns for binding sites in prokaryotic species are not well characterized but are known to be large, between 16–30 base pairs (bp) and contain at least 2 conserved bases. The length of prokaryotic species promoters (about 400 bp) and our interest in studying a small set of genes that could be a cluster of co-regulated genes from microarray experiments led to the development of a new exhaustive algorithm targeting these large patterns.
We present Searchpattool, a new method to search for and select the most specific (conservative) frequent patterns. This method does not impose restrictions on the density or the structure of the pattern. The best patterns (motifs) are selected using several statistics, including a new application of a z-score based on the number of matching sequences. We compared Searchpattool against other well known algorithms on a Bacillus subtilis group of 14 input sequences and found that in our experiments Searchpattool always performed the best based on performance scores.
Searchpattool is a new method for pattern discovery relative to transcription factor binding sites for species or genes with short promoters. It outputs the most specific significant patterns and helps the biologist to choose the best candidates.