Martin Tompa

Assessing computational tools for the discovery of transcription factor binding sites

The prediction of regulatory elements is a problem where computational methods offer great hope. Over the past few years, numerous tools have become available for this task. The purpose of the current assessment is twofold: to provide some guidance to users regarding the accuracy of currently available tools in various settings, and to provide a benchmark of data sets for assessing future tools.

Finding motifs using random projections

Pevzner and Sze [23] considered a precise version of the motif discovery problem and simultaneously issued an algorithmic challenge: find a motif M of length 15, where each planted instance differs from M in 4 positions. Whereas previous algorithms all failed to solve this (15,4)-motif problem. Pevzner and Sze introduced algorithms that succeeded. However, their algorithms failed to solve the considerably more difficult (14,4)-, (16,5)-, and (18,6)-motif problems. We introduce a novel motif discovery algorithm based on the use of random projections of the inputs substrings. Experiments on simulated data demonstrate that this algorithm performs better than existing algorithms and, in particular, typically solves the difficult (14,4)-, (16,5)-, and (18,6)-motif problems quite efficiently. A probabilistic estimate shows that the small values of d for which the algorithm fails to recover the planted (l, d)-motif are in all likelihood inherently impossible to solve. We also present experimental results on realistic biological data by identifying ribosome binding sites in prokaryotes as well as a number of known transcriptional regulatory motifs in eukaryotes.

Rv3133c/dosR is a transcription factor that mediates the hypoxic response of Mycobacterium tuberculosis.

Unlike many pathogens that are overtly harmful to their hosts, Mycobacterium tuberculosis can persist for years within humans in a clinically latent state. Latency is often linked to hypoxic conditions within the host. Among M. tuberculosis genes induced by hypoxia is a putative transcription factor, Rv3133c/DosR. We performed targeted disruption of this locus followed by transcriptome analysis of wild‐type and mutant bacilli. Nearly all the genes powerfully regulated by hypoxia require Rv3133c/DosR for their induction. Computer analysis identified a consensus motif, a variant of which is located upstream of nearly all M. tuberculosis genes rapidly induced by hypoxia. Further, Rv3133c/DosR binds to the two copies of this motif upstream of the hypoxic response gene alpha‐crystallin. Mutations within the binding sites abolish both Rv3133c/DosR binding as well as hypoxic induction of a downstream reporter gene. Also, mutation experiments with Rv3133c/DosR confirmed sequence‐based predictions that the C‐terminus is responsible for DNA binding and that the aspartate at position 54 is essential for function. Together, these results demonstrate that Rv3133c/DosR is a transcription factor of the two‐component response regulator class, and that it is the primary mediator of a hypoxic signal within M. tuberculosis.

How to share a secret with cheaters

This paper demonstrates that Shamirs scheme [10] is not secure against certain forms of cheating. A small modification to his scheme retains the security and efficiency of the original, is secure against these forms of cheating, and preserves the property that its security does not depend on any unproven assumptions such as the intractability of computing number-theoretic functions.

YMF: a program for discovery of novel transcription factor binding sites by statistical overrepresentation

A fundamental challenge facing biologists is to identify DNA binding sites for unknown regulatory factors, given a collection of genes believed to be coregulated. The program YMF identifies good candidates for such binding sites by searching for statistically overrepresented motifs. More specifically, YMF enumerates all motifs in the search space and is guaranteed to produce those motifs with greatest z-scores. This note describes the YMF web software, available at http://bio.cs.washington.edu/software.html.

PhyME: A probabilistic algorithm for finding motifs in sets of orthologous sequences

BackgroundThis paper addresses the problem of discovering transcription factor binding sites in heterogeneous sequence data, which includes regulatory sequences of one or more genes, as well as their orthologs in other species.ResultsWe propose an algorithm that integrates two important aspects of a motifs significance – overrepresentation and cross-species conservation – into one probabilistic score. The algorithm allows the input orthologous sequences to be related by any user-specified phylogenetic tree. It is based on the Expectation-Maximization technique, and scales well with the number of species and the length of input sequences. We evaluate the algorithm on synthetic data, and also present results for data sets from yeast, fly, and human.ConclusionsThe results demonstrate that the new approach improves motif discovery by exploiting multiple species information.

FootPrinter: a program designed for phylogenetic footprinting

Phylogenetic footprinting is a method for the discovery of regulatory elements in a set of homologous regulatory regions, usually collected from multiple species. It does so by identifying the best conserved motifs in those homologous regions. This note describes web software that has been designed specifically for this purpose, making use of the phylogenetic relationships among the homologous sequences in order to make more accurate predictions. The software is called FootPrinter and is available at http://bio.cs.washington.edu/software.html.

Space-bounded hierarchies and probabilistic computations

Abstract We study three aspects of the power of space-bounded probabilistic Turing machines. First, we give a simple alternative proof of Simons result that space-bounded probabilistic complexity classes are closed under complement. Second, we demonstrate that any language recognizable by an alternating Turing machine in log n space with a constant number of alternations (the log n space “alternation hierarch”) also can be recognized by a log n spacebounded probabilistic Turing machine with small error probability; this is a generalization of Gills result that any language in NSPACE (log n) can be recognized by such a machine. Third, we give a new definition of space-bounded oracle machines, and use it to define a space-bounded “oracle hierarchy” analogous to the original definition of the polynomial time hierarchy. Unlike its polynomial time analogue, the entire log n space “alternation hierarchy” is contained in the second level of the log n space “oracle hierarchy.” However, the entire log n space “oracle hierarchy”is still contained in bounded-error probabilistic space log n.

Two applications of inductive counting for complementation problems

Following the recent independent proofs of Immerman [SIAM J. Comput., 17 (1988), pp. 935–938] and Szelepcsenyi [Bull. European Assoc. Theoret. Comput. Sci., 33 (1987), pp. 96–100] that nondeterministic space-bounded complexity classes are closed under complementation, two further applications of the inductive counting technique are developed. First, an errorless probabilistic algorithm for the undirected graph s-t connectivity problem that runs in

Algorithms for phylogenetic footprinting.

O(\log n)