Identification of regions in multiple sequence alignments thermodynamically suitable for targeting by consensus oligonucleotides: application to HIV genome

BACKGROUND: Computer programs for the generation of multiple sequence alignments such as "Clustal W" allow detection of regions that are most conserved among many sequence variants. However, even for regions that are equally conserved, their potential utility as hybridization targets varies. Mismatches in sequence variants are more disruptive in some duplexes than in others. Additionally, the propensity for self-interactions amongst oligonucleotides targeting conserved regions differs and the structure of target regions themselves can also influence hybridization efficiency. There is a need to develop software that will employ thermodynamic selection criteria for finding optimal hybridization targets in related sequences. RESULTS: A new scheme and new software for optimal detection of oligonucleotide hybridization targets common to families of aligned sequences is suggested and applied to aligned sequence variants of the complete HIV-1 genome. The scheme employs sequential filtering procedures with experimentally determined thermodynamic cut off points: 1) creation of a consensus sequence of RNA or DNA from aligned sequence variants with specification of the lengths of fragments to be used as oligonucleotide targets in the analyses; 2) selection of DNA oligonucleotides that have pairing potential, greater than a defined threshold, with all variants of aligned RNA sequences; 3) elimination of DNA oligonucleotides that have self-pairing potentials for intra- and inter-molecular interactions greater than defined thresholds. This scheme has been applied to the HIV-1 genome with experimentally determined thermodynamic cut off points. Theoretically optimal RNA target regions for consensus oligonucleotides were found. They can be further used for improvement of oligo-probe based HIV detection techniques. CONCLUSIONS: A selection scheme with thermodynamic thresholds and software is presented in this study. The package can be used for any purpose where there is a need to design optimal consensus oligonucleotides capable of interacting efficiently with hybridization targets common to families of aligned RNA or DNA sequences. Our thermodynamic approach can be helpful in designing consensus oligonucleotides with consistently high affinity to target variants in evolutionary related genes or genomes

Closest string with outliers

Background: Given n strings s1, …, sn each of length ℓ and a nonnegative integer d, the CLOSEST STRING problem asks to find a center string s such that none of the input strings has Hamming distance greater than d from s. Finding a common pattern in many – but not necessarily all – input strings is an important task that plays a role in many applications in bioinformatics. Results: Although the closest string model is robust to the oversampling of strings in the input, it is severely affected by the existence of outliers. We propose a refined model, the CLOSEST STRING WITH OUTLIERS (CSWO) problem, to overcome this limitation. This new model asks for a center string s that is within Hamming distance d to at least n – k of the n input strings, where k is a parameter describing the maximum number of outliers. A CSWO solution not only provides the center string as a representative for the set of strings but also reveals the outliers of the set. We provide fixed parameter algorithms for CSWO when d and k are parameters, for both bounded and unbounded alphabets. We also show that when the alphabet is unbounded the problem is W[1]-hard with respect to n – k, ℓ, and d. Conclusions: Our refined model abstractly models finding common patterns in several but not all input strings

Design of primers for PCR amplification of highly variable genomes

A program to aid in the search of primers for specific polymerase chain reaction (PCR) amplification of highly variable genomes is presented. It involves the derivation of variability profiles to identify optimal regions for PCR amplification, taking into account stability of DNA-primer hybrids. An application of the program to foot-and-mouth disease virus diagnosis is presented. © 1993 Oxford University Press

Combinatorial and Probabilistic Approaches to Motif Recognition

Short substrings of genomic data that are responsible for biological processes, such as gene expression, are referred to as motifs. Motifs with the same function may not entirely match, due to mutation events at a few of the motif positions. Allowing for non-exact occurrences significantly complicates their discovery. Given a number of DNA strings, the motif recognition problem is the task of detecting motif instances in every given sequence without knowledge of the position of the instances or the pattern shared by these substrings. We describe a novel approach to motif recognition, and provide theoretical and experimental results that demonstrate its efficiency and accuracy. Our algorithm, MCL-WMR, builds an edge-weighted graph model of the given motif recognition problem and uses a graph clustering algorithm to quickly determine important subgraphs that need to be searched further for valid motifs. By considering a weighted graph model, we narrow the search dramatically to smaller problems that can be solved with significantly less computation. The Closest String problem is a subproblem of motif recognition, and it is NP-hard. We give a linear-time algorithm for a restricted version of the Closest String problem, and an efficient polynomial-time heuristic that solves the general problem with high probability. We initiate the study of the smoothed complexity of the Closest String problem, which in turn explains our empirical results that demonstrate the great capability of our probabilistic heuristic. Important to this analysis is the introduction of a perturbation model of the Closest String instances within which we provide a probabilistic analysis of our algorithm. The smoothed analysis suggests reasons why a well-known fixed parameter tractable algorithm solves Closest String instances extremely efficiently in practice. Although the Closest String model is robust to the oversampling of strings in the input, it is severely affected by the existence of outliers. We propose a refined model, the Closest String with Outliers problem, to overcome this limitation. A systematic parameterized complexity analysis accompanies the introduction of this problem, providing a surprising insight into the sensitivity of this problem to slightly different parameterizations. Through the application of probabilistic and combinatorial insights into the Closest String problem, we develop sMCL-WMR, a program that is much faster than its predecessor MCL-WMR. We apply and adapt sMCL-WMR and MCL-WMR to analyze the promoter regions of the canola seed-coat. Our results identify important regions of the canola genome that are responsible for specific biological activities. This knowledge may be used in the long-term aim of developing crop varieties with specific biological characteristics, such as being disease-resistant