Algorithms for extracting structured motifs using a suffix tree with an application to promoter and regulatory site consensus identification

This paper introduces two exact algorithms for extracting conserved structured motifs from a set of DNA sequences. Structured motifs may be described as an ordered collection of p greater than or equal to 1 boxes (each box corresponding to one part of the structured moth), p substitution rates (one for each box) and p - 1 intervals of distance (one for each pair of successive boxes in the collection). The contents of the boxes - that is, the motifs themselves - are unknown at the start of the algorithm. This is precisely what the algorithms are meant to find. A suffix tree is used for finding such moths, The algorithms are efficient enough to be able to infer site consensi, such as, for instance, promoter sequences or regulatory sites, from a set of unaligned sequences corresponding to the noncoding regions upstream from all genes of a genome. In particular, both algorithms time complexity scales linearly with N(2)n where n is the average length of the sequences and N their number. An application to the identification of promoter and regulatory consensus sequences in bacterial genomes is shown.