Ydo Wexler

Finding approximate tandem repeats in genomic sequences.

An efficient algorithm is presented for detecting approximate tandem repeats in genomic sequences. The algorithm is based on a flexible statistical model which allows a wide range of definitions of approximate tandem repeats. The ideas and methods underlying the algorithm are described and its effectiveness on genomic data is demonstrated.

A study of accessible motifs and RNA folding complexity.

mRNA molecules are folded in the cells and therefore many of their substrings may actually be inaccessible to protein and microRNA binding. The need to apply an accessibility criterion to the task of genome-wide mRNA motif discovery raises the challenge of overcoming the core O(n(3)) factor imposed by the time complexity of the currently best known algorithms for RNA secondary structure prediction. We speed up the dynamic programming algorithms that are standard for RNA folding prediction. Our new approach significantly reduces the computations without sacrificing the optimality of the results, yielding an expected time complexity of O(n(2) psi(n)), where psi(n) is shown to be constant on average under standard polymer folding models. A benchmark analysis confirms that in practice the runtime ratio between the previous approach and the new algorithm indeed grows linearly with increasing sequence size. The fast new RNA folding algorithm is utilized for genome-wide discovery of accessible cis-regulatory motifs in data sets of ribosomal densities and decay rates of S. cerevisiae genes and to the mining of exposed binding sites of tissue-specific microRNAs in A. thaliana.

A Faster Algorithm for RNA Co-folding

The current pairwise RNA (secondary) structural alignment algorithms are based on Sankoffs dynamic programming algorithm from 1985. Sankoffs algorithm requires O(N6) time and O(N4) space, where Ndenotes the length of the compared sequences, and thus its applicability is very limited. The current literature offers many heuristics for speeding up Sankoffs alignment process, some making restrictive assumptions on the length or the shape of the RNA substructures. We show how to speed up Sankoffs algorithm in practice via non-heuristic methods, without compromising optimality. Our analysis shows that the expected time complexity of the new algorithm is O(N4?(N)), where ?(N) converges to O(N), assuming a standard polymer folding model which was supported by experimental analysis. Hence our algorithm speeds up Sankoffs algorithm by a linear factor on average. In simulations, our algorithm speeds up computation by a factor of 3-12 for sequences of length 25-250. Availability: Code and data sets are available, upon request.

Estimating genome-wide IBD sharing from SNP data via an efficient hidden Markov model of LD with application to gene mapping

Motivation: Association analysis is the method of choice for studying complex multifactorial diseases. The premise of this method is that affected persons contain some common genomic regions with similar SNP alleles and such areas will be found in this analysis. An important disadvantage of GWA studies is that it does not distinguish between genomic areas that are inherited from a common ancestor [identical by descent (IBD)] and areas that are identical merely by state [identical by state (IBS)]. Clearly, areas that can be marked with higher probability as IBD and have the same correlation with the disease status of identical areas that are more probably only IBS, are better candidates to be causative, and yet this distinction is not encoded in standard association analysis. Results: We develop a factorial hidden Markov model-based algorithm for computing genome-wide IBD sharing. The algorithm accepts as input SNP data of measured individuals and estimates the probability of IBD at each locus for every pair of individuals. For two g-degree relatives, when g≥8, the computation yields a precision of IBD tagging of over 50% higher than previous methods for 95% recall. Our algorithm uses a first-order Markovian model for the linkage disequilibrium process and employs a reduction of the state space of the inheritance vector from being exponential in g to quadratic. The higher accuracy along with the reduced time complexity marks our method as a feasible means for IBD mapping in practical scenarios. Availability: A software implementation, called IBDMAP, is freely available at http://bioinfo.cs.technion.ac.il/IBDmap. Contact: sberco@gmail.com

Speeding up HMM algorithms for genetic linkage analysis via chain reductions of the state space

We develop an hidden Markov model (HMM)-based algorithm for computing exact parametric and non-parametric linkage scores in larger pedigrees than was possible before. The algorithm is applicable whenever there are chains of persons in the pedigree with no genetic measurements and with unknown affection status. The algorithm is based on shrinking the state space of the HMM considerably using such chains. In a two g-degree cousins pedigree the reduction drops the state space from being exponential in g to being linear in g. For a Finnish family in which two affected children suffer from a rare cold-inducing sweating syndrome, we were able to reduce the state space by more than five orders of magnitude from 250 to 232. In another pedigree of state-space size of 227, used for a study of pituitary adenoma, the state space reduced by a factor of 8.5 and consequently exact linkage scores can now be computed, rather than approximated. Contact: dang@cs.technion.ac.il Supplementary information: Supplementary data are available at Bioinformatics online.

Similarities and differences of gene expression in yeast stress conditions

UNLABELLED MOTIVATION AND METHODS: All living organisms and the survival of all cells critically depend on their ability to sense and quickly adapt to changes in the environment and to other stress conditions. We study stress response mechanisms in Saccharomyces cerevisiae by identifying genes that, according to very stringent criteria, have persistent co-expression under a variety of stress conditions. This is enabled through a fast clique search method applied to the intersection of several co-expression graphs calculated over the data of Gasch et al. This method exploits the topological characteristics of these graphs. RESULTS We observe cliques in the intersection graphs that are much larger than expected under a null model of changing gene identities for different stress conditions but maintaining the co-expression topology within each one. Persistent cliques are analyzed to identify enriched function as well as enriched regulation by a small number of TFs. These TFs, therefore, characterize a universal and persistent reaction to stress response. We further demonstrate that the vertices (genes) of many cliques in the intersection graphs are co-localized in the yeast genome, to a degree far beyond the random expectation. Co-localization can hypothetically contribute to a quick co-ordinated response. We propose the use of persistent cliques in further study of properties of co-regulation.

Finding approximate tandem repeats in genomic sequences

An efficient algorithm is presented for detecting approximate tandem repeats in genomic sequences. The algorithm is based on a flexible statistical model which allows a wide range of definitions of approximate tandem repeats. The ideas and methods underlying the algorithm are described and examined and its effectiveness on genomic data is demonstrated.

A Faster Algorithm for Simultaneous Alignment and Folding of RNA

The current pairwise RNA (secondary) structural alignment algorithms are based on Sankoffs dynamic programming algorithm from 1985. Sankoffs algorithm requires O(N(6)) time and O(N(4)) space, where N denotes the length of the compared sequences, and thus its applicability is very limited. The current literature offers many heuristics for speeding up Sankoffs alignment process, some making restrictive assumptions on the length or the shape of the RNA substructures. We show how to speed up Sankoffs algorithm in practice via non-heuristic methods, without compromising optimality. Our analysis shows that the expected time complexity of the new algorithm is O(N(4)sigma(N)), where sigma(N) converges to O(N), assuming a standard polymer folding model which was supported by experimental analysis. Hence, our algorithm speeds up Sankoffs algorithm by a linear factor on average. In simulations, our algorithm speeds up computation by a factor of 3-12 for sequences of length 25-250. Code and data sets are available, upon request.

A variational inference procedure allowing internal structure for overlapping clusters and deterministic constraints

We develop a novel algorithm, called VIP*, for structured variational approximate inference. This algorithm extends known algorithms to allow efficient multiple potential updates for overlapping clusters, and overcomes the difficulties imposed by deterministic constraints. The algorithms convergence is proven and its applicability demonstrated for genetic linkage analysis.

Relating Reputation and Money in Online Markets

Reputation in online economic systems is typically quantified using counters that specify positive and negative feedback from past transactions and/or some form of transaction network analysis that aims to quantify the likelihood that a network user will commit a fraudulent transaction. These approaches can be deceiving to honest users from numerous perspectives. We take a radically different approach with the goal of guaranteeing to a buyer that a fraudulent seller cannot disappear from the system with profit following a set of fabricated transactions that total a certain monetary limit. Even in the case of stolen identity, such an adversary cannot produce illegal profit unless a buyer decides to pay over the suggested limit.