Biomedical subjects
Publications and source records attributed to M Tompa.
This paper introduces the first integrated algorithm designed to discover novel motifs in heterogeneous sequence data, which is comprised of coregulated genes from a single genome together with the orthologs of these genes from other genomes. Results are presented for regulons in yeasts, worms, and mammals.
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The advent of the DNA microarray technology has brought with it the exciting possibility of simultaneously observing the expression levels of all genes in an organism. One such microarray technology, called "oligo arrays", manufactures short single strands of DNA (called probes) onto a glass surface using photolithography. An altered or missed step in such a manufacturing protocol can adversely affect all probes using this failed step, and is in general impossible to disentangle from experimental variation when using such a defective array. The idea of designing special quality control probes to detect a failed step was first formulated by Hubbell and Pevzner. We consider an alternative formulation of this problem and use a combinatorial design approach to solve it. Our results improve over prior work in guaranteeing coverage of all protocol steps and in being able to tolerate a greater number of unreliable probe intensities.
The identification of sequence motifs is a fundamental method for suggesting good candidates for biologically functional regions such as promoters, splice sites, binding sites, etc. We investigate the following approach to identifying motifs: given a collection of orthologous sequences from multiple species related by a known phylogenetic tree, search for motifs that are well conserved (according to a parsimony measure) in the species. We present an exact algorithm for solving this problem. We then discuss experimental results on finding promoters of the rbcS gene for a family of 10 plants, on finding promoters of the adh gene for 12 Drosophila species, and on finding promoters of several chloroplast encoded genes.
Understanding the mechanisms that determine the regulation of gene expression is an important and challenging problem. A fundamental subproblem is to identify DNA-binding sites for unknown regulatory factors, given a collection of genes believed to be coregulated, and given the noncoding DNA sequences near those genes. We present an enumerative statistical method for identifying good candidates for such transcription factor binding sites. Unlike local search techniques such as Expectation Maximization and Gibbs samplers that may not reach a global optimum, the method proposed here is guaranteed to produce the motifs with greatest z-scores. We discuss the results of experiments in which this algorithm was used to locate candidate binding sites in several well studied pathways of S. cerevisiae, as well as gene clusters from some of the hybridization microarray experiments.
Given a sequence of real numbers ("scores"), we present a practical linear time algorithm to find those nonoverlapping, contiguous subsequences having greatest total scores. This improves on the best previously known algorithm, which requires quadratic time in the worst case. The problem arises in biological sequence analysis, where the high-scoring subsequences correspond to regions of unusual composition in a nucleic acid or protein sequence. For instance, Altschul, Karlin, and others have used this approach to identify transmembrane regions, DNA binding domains, and regions of high charge in proteins.
This is an investigation of methods for finding short motifs that only occur in a fraction of the input sequences. Unlike local search techniques that may not reach a global optimum, the method proposed here is guaranteed to produce the motifs with greatest z-scores. This method is illustrated for the Ribosome Binding Site Problem, which is to identify the short mRNA 5' untranslated sequence that is recognized by the ribosome during initiation of protein synthesis. Experiments were performed to solve this problem for each of fourteen sequenced prokaryotes, by applying the method to the full complement of genes from each. One of the interesting results of this experimentation is evidence that the recognized sequence of the thermophilic archaea A. fulgidus, M. jannaschii, M. thermoautotrophicum, and P. horikoshii may be somewhat different than the well known Shine-Dalgarno sequence.