Identification of common motifs in unaligned DNA sequences: application to Escherichia coli Lrp regulon

A hybrid method for the exact planted (l, d) motif finding problem and its parallelization

Background

Given a set of DNA sequences s₁, ..., s_t, the (l, d) motif problem is to find an l-length motif sequence M , not necessary existing in any of the input sequences, such that for each sequence s_i, 1 ≤ i ≤ t, there is at least one subsequence differing with at most d mismatches from M. Many exact algorithms have been developed to solve the motif finding problem in the last three decades. However, the problem is still challenging and its solution is limited to small values of l and d.

Results

In this paper we present a new efficient method to improve the performance of the exact algorithms for the motif finding problem. Our method is composed of two main steps: First, we process q ≤ t sequences to find candidate motifs. Second, the candidate motifs are searched in the remaining sequences. For both steps, we use the best available algorithms. Our method is a hybrid one, because it integrates currently existing algorithms to achieve the best running time. In this paper, we show how the optimal value of q is determined to achieve the best running time. Our experimental results show that there is about 24% speed-up achieved by our method compared to the best existing algorithm. Furthermore, we also present a parallel version of our method running on shared memory architecture. Our experiments show that the performance of our algorithm scales linearly with the number of processors. Using the parallel version, we were able to solve the (21, 8) challenging instance using 8 processors in 20.42 hours instead of 6.68 days of the serial version.

Conclusions

Our method speeds up the solution of the exact motif problem. Our method is generic, because it can accommodate any new faster algorithm based on traditional methods. We expect that our method will help to discover longer motifs. The software we developed is available for free for academic research at http://www.nubios.nileu.edu.eg/tools/hymotif.

Algorithms for challenging motif problems

Pevzner and Sze(19) have introduced the Planted (l,d)-Motif Problem to find similar patterns (motifs) in sequences which represent the promoter regions of co-regulated genes, where l is the length of the motif and d is the maximum Hamming distance around the similar patterns. Many algorithms have been developed to solve this motif problem. However, these algorithms either have long running times or do not guarantee the motif can be found. In this paper, we introduce new algorithms to solve this motif problem. Our algorithms can find motifs in reasonable time for not only the challenging (9, 2), (11, 3), (15, 5)-motif problems but for even longer motifs, say (20, 7), (30, 11) and (40, 15), which have never been seriously attempted by other researchers because of the large time and space required. Besides, our algorithms can be extended to find more complicated motifs structure called cis-regulatory modules (CRM).

Discovery of novel transcription factor binding sites by statistical overrepresentation

Understanding the complex and varied mechanisms that regulate gene expression is an important and challenging problem. A fundamental sub-problem is to identify DNA binding sites for unknown regulatory factors, given a collection of genes believed to be co-regulated. We discuss a computational method that identifies good candidates for such binding sites. Unlike local search techniques such as expectation maximization and Gibbs samplers that may not reach a global optimum, the method discussed enumerates all motifs in the search space, and is guaranteed to produce the motifs with greatest z-scores. We discuss the results of validation experiments in which this algorithm was used to identify candidate binding sites in several well studied regulons of Saccharomyces cerevisiae, where the most prominent transcription factor binding sites are largely known. We then discuss the results on gene families in the functional and mutant phenotype catalogs of S.cerevisiae, where the algorithm suggests many promising novel transcription factor binding sites. The program is available at http://bio.cs.washington.edu/software.html.

GeneMarkS: a self-training method for prediction of gene starts in microbial genomes. Implications for finding sequence motifs in regulatory regions

Improving the accuracy of prediction of gene starts is one of a few remaining open problems in computer prediction of prokaryotic genes. Its difficulty is caused by the absence of relatively strong sequence patterns identifying true translation initiation sites. In the current paper we show that the accuracy of gene start prediction can be improved by combining models of protein-coding and non-coding regions and models of regulatory sites near gene start within an iterative Hidden Markov model based algorithm. The new gene prediction method, called GeneMarkS, utilizes a non-supervised training procedure and can be used for a newly sequenced prokaryotic genome with no prior knowledge of any protein or rRNA genes. The GeneMarkS implementation uses an improved version of the gene finding program GeneMark.hmm, heuristic Markov models of coding and non-coding regions and the Gibbs sampling multiple alignment program. GeneMarkS predicted precisely 83.2% of the translation starts of GenBank annotated Bacillus subtilis genes and 94.4% of translation starts in an experimentally validated set of Escherichia coli genes. We have also observed that GeneMarkS detects prokaryotic genes, in terms of identifying open reading frames containing real genes, with an accuracy matching the level of the best currently used gene detection methods. Accurate translation start prediction, in addition to the refinement of protein sequence N-terminal data, provides the benefit of precise positioning of the sequence region situated upstream to a gene start. Therefore, sequence motifs related to transcription and translation regulatory sites can be revealed and analyzed with higher precision. These motifs were shown to possess a significant variability, the functional and evolutionary connections of which are discussed.

Use of an in vivo titration method to study a global regulator: effect of varying Lrp levels on expression of gltBDF in Escherichia coli

Most studies of global regulatory proteins are performed in vitro or involve phenotypic comparisons between wild-type and mutant strains. We report the use of strains in which the gene for the leucine-responsive regulatory protein (lrp) is transcribed from isopropyl-beta-D-thiogalactopyranoside (IPTG)-inducible promoters for the purpose of continuously varying the in vivo concentration of Lrp. To obtain a broad range of Lrp concentrations, strains were employed that contained the lrp fusion either in the chromosome (I. C. Blomfield, P. J. Calie, K. J. Eberhardt, M. S. McClain, and B. I. Eisenstein, J. Bacteriol. 175:27-36, 1993) or on a multicopy plasmid. Western blot (immunoblot) analysis with polyclonal antiserum to Lrp confirmed that Lrp levels could be varied more than 70-fold by growing the strains in glucose minimal 3-(N-morpholino)propanesulfonic acid (MOPS) medium containing different amounts of IPTG. Expression of an Lrp-regulated gltB::lacZ operon fusion was measured over this range of Lrp concentrations. beta-Galactosidase activity rose with increasing Lrp levels up to the level of Lrp found in wild-type strains, at which point expression is maximal. The presence of leucine in the medium increased the level of Lrp necessary to achieve half-maximal expression of the gltB::lacZ fusion, as predicted by earlier in vitro studies (B. R. Ernsting, J. W. Denninger, R. M. Blumenthal, and R. G. Matthews, J. Bacteriol. 175:7160-7169, 1993). Interestingly, levels of Lrp greater than those in wild-type cells interfered with activation of gltB::lacZ expression. The growth rate of cultures correlated with the intracellular Lrp concentration: levels of Lrp either lower or higher than wild-type levels resulted in significantly slower growth rates. Thus, the level of Lrp in the cell appears to be optimal for rapid growth in minimal medium, and the gltBDF control region is designed to give maximal expression at this Lrp level.