A unifying framework for seed sensitivity and its application to subset seeds

We propose a general approach to compute the seed sensitivity, that can be applied to different definitions of seeds. It treats separately three components of the seed sensitivity problem -- a set of target alignments, an associated probability distribution, and a seed model -- that are specified by distinct finite automata. The approach is then applied to a new concept of subset seeds for which we propose an efficient automaton construction. Experimental results confirm that sensitive subset seeds can be efficiently designed using our approach, and can then be used in similarity search producing better results than ordinary spaced seeds

Greene SCPrimer: a rapid comprehensive tool for designing degenerate primers from multiple sequence alignments

Polymerase chain reaction (PCR) is widely applied in clinical and environmental microbiology. Primer design is key to the development of successful assays and is often performed manually by using multiple nucleic acid alignments. Few public software tools exist that allow comprehensive design of degenerate primers for large groups of related targets based on complex multiple sequence alignments. Here we present a method for designing such primers based on tree building followed by application of a set covering algorithm, and demonstrate its utility in compiling Multiplex PCR primer panels for detection and differentiation of viral pathogens

SimulFold: Simultaneously Inferring RNA Structures Including Pseudoknots, Alignments, and Trees Using a Bayesian MCMC Framework

Computational methods for predicting evolutionarily conserved rather than thermodynamic RNA structures have recently attracted increased interest. These methods are indispensable not only for elucidating the regulatory roles of known RNA transcripts, but also for predicting RNA genes. It has been notoriously difficult to devise them to make the best use of the available data and to predict high-quality RNA structures that may also contain pseudoknots. We introduce a novel theoretical framework for co-estimating an RNA secondary structure including pseudoknots, a multiple sequence alignment, and an evolutionary tree, given several RNA input sequences. We also present an implementation of the framework in a new computer program, called SimulFold, which employs a Bayesian Markov chain Monte Carlo method to sample from the joint posterior distribution of RNA structures, alignments, and trees. We use the new framework to predict RNA structures, and comprehensively evaluate the quality of our predictions by comparing our results to those of several other programs. We also present preliminary data that show SimulFold's potential as an alignment and phylogeny prediction method. SimulFold overcomes many conceptual limitations that current RNA structure prediction methods face, introduces several new theoretical techniques, and generates high-quality predictions of conserved RNA structures that may include pseudoknots. It is thus likely to have a strong impact, both on the field of RNA structure prediction and on a wide range of data analyses

Algorithms in Bioinformatics: Third International Workshop, WABI 2003, Budapest, Hungary, September 15-20, 2003, Proceedings

No abstract available

An Iterative Beam Search Algorithm for Degenerate Primer Selection

Single Nucleotide Polymorphism (SNP) Genotyping is an important molecular genetics process in the early stages of producing results that will be useful in the medical field. Due to inherent complexities in DNA manipulation and analysis, many different methods have been proposed for a standard assay. One of the proposed techniques for performing SNP Genotyping requires amplifying regions of DNA surrounding a large number of SNP loci. In order to automate a portion of this particular method, it is necessary to select a set of primers for the experiment. Selecting these primers can be formulated as the Multiple Degenerate Primer Design (MDPD) problem. In this thesis, we describe an iterative beam-search algorithm, Multiple, It-erative Primer Selector (MIPS), for MDPD. Theoretical and experimental analyses show that this algorithm performs well compared to the limits of degenerate primer design. Furthermore, MIPS outperforms an existing algorithm which was designed for a related degenerate primer selection problem. Further analysis shows that, due to the composition of the human genome, the results from MIPS may not be realized in practice. Consequently, we address the challenges involved in selecting a suitable set of degenerate primers and possible future improvements to the algorithm

Phylogenetic Detection of Recombination with a Bayesian Prior on the Distance between Trees

Genomic regions participating in recombination events may support distinct topologies, and phylogenetic analyses should incorporate this heterogeneity. Existing phylogenetic methods for recombination detection are challenged by the enormous number of possible topologies, even for a moderate number of taxa. If, however, the detection analysis is conducted independently between each putative recombinant sequence and a set of reference parentals, potential recombinations between the recombinants are neglected. In this context, a recombination hotspot can be inferred in phylogenetic analyses if we observe several consecutive breakpoints. We developed a distance measure between unrooted topologies that closely resembles the number of recombinations. By introducing a prior distribution on these recombination distances, a Bayesian hierarchical model was devised to detect phylogenetic inconsistencies occurring due to recombinations. This model relaxes the assumption of known parental sequences, still common in HIV analysis, allowing the entire dataset to be analyzed at once. On simulated datasets with up to 16 taxa, our method correctly detected recombination breakpoints and the number of recombination events for each breakpoint. The procedure is robust to rate and transition∶transversion heterogeneities for simulations with and without recombination. This recombination distance is related to recombination hotspots. Applying this procedure to a genomic HIV-1 dataset, we found evidence for hotspots and de novo recombination

Manifold Learning for Natural Image Sets, Doctoral Dissertation August 2006

The field of manifold learning provides powerful tools for parameterizing high-dimensional data points with a small number of parameters when this data lies on or near some manifold. Images can be thought of as points in some high-dimensional image space where each coordinate represents the intensity value of a single pixel. These manifold learning techniques have been successfully applied to simple image sets, such as handwriting data and a statue in a tightly controlled environment. However, they fail in the case of natural image sets, even those that only vary due to a single degree of freedom, such as a person walking or a heart beating. Parameterizing data sets such as these will allow for additional constraints on traditional computer vision problems such as segmentation and tracking. This dissertation explores the reasons why classical manifold learning algorithms fail on natural image sets and proposes new algorithms for parameterizing this type of data

Parallel evolution strategy for protein threading.

A protein-sequence folds into a specific shape in order to function in its aqueous state. If the primary sequence of a protein is given, what is its three dimensional structure? This is a long-standing problem in the field of molecular biology and it has large implication to drug design and cure. Among several proposed approaches, protein threading represents one of the most promising technique. The protein threading problem (PTP) is the problem of determining the three-dimensional structure of a given but arbitrary protein sequence from a set of known structures of other proteins. This problem is known to be NP-hard and current computational approaches to threading are time-consuming and data-intensive. In this thesis, we proposed an evolution strategy (ES) based approach for protein threading (EST). We also developed two parallel approaches for the PTP problem and both are parallelizations of our novel EST. The first method, we call SQST-PEST (Single Query Single Template Parallel EST) threads a single query against a single template. We use ES to find the best alignment between the query and the template, and ES is parallelized. The second method, we call SQMT-PEST (Single Query Multiple Templates Parallel EST) to allow for threading a single query against multiple templates within reasonable time. We obtained better results than current comparable approaches, as well as significant reduction in execution time.Dept. of Computer Science. Paper copy at Leddy Library: Theses & Major Papers - Basement, West Bldg. / Call Number: Thesis2005 .I85. Source: Masters Abstracts International, Volume: 44-03, page: 1403. Thesis (M.Sc.)--University of Windsor (Canada), 2005

Faculty Publications and Creative Works 2004

Faculty Publications & Creative Works is an annual compendium of scholarly and creative activities of University of New Mexico faculty during the noted calendar year. Published by the Office of the Vice President for Research and Economic Development, it serves to illustrate the robust and active intellectual pursuits conducted by the faculty in support of teaching and research at UNM