Algorithms for Extracting Structured Motifs Using a Suffix Tree with an Application to Promoter and Regulatory Site Consensus Identification

Abstract

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 > or = 1 "boxes" (each box corresponding to one part of the structured motif), 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 motifs. 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 N2n 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.

Efficient Extraction of Structured Motifs Using Box-Links

“…Algorithms for structured motifs extraction [6] address the extraction of consensus motifs that appear together in a well-ordered and regularly spaced manner. A structured motif can be described as an ordered collection of p ≥ 1 boxes, a maximum allowed error (substitutions) for each box, and an interval of distance for each pair of consecutive boxes.…”

“…No constraint, and therefore no statistical value is put on the distances separating them. This paper is based on the only previous algorithm that is able to identify motifs composed of any number of boxes -structured motifs [6]. There are two central problems concerning motifs in sequences: localization and extraction [13].…”

“…The goal of the structured motif localization is to find the positions in a sequence of the occurrences of a given structured motif [14]. The structured motif extraction aims to identify de novo structured motifs in a set of input sequences [6]. In this paper, we address the structured motif extraction problem.…”

“…Like the previous algorithm [6], this new exact algorithm infers all structured motifs which match a minimum number of input sequences. The latter number is called quorum.…”

Lossless Filter for Finding Long Multiple Approximate Repetitions Using a New Data Structure, the Bi-factor Array

“…The efficiency of the filter relies on an original data structure, the bi-factor array, that is also introduced in this paper, and on a labelling of the seeds similar to the one employed in [8]. This new data structure can be used to speed up other tasks such as the inference of structured motifs [18] or for improving other filters [14].…”