Methods for Designing Reliable Probe Arrays

Recent advances in biosensing technologies have led to applications of biosensor probe arrays for rapid identification of biological agents such as drugs, gene expressions, proteins, cholesterol and fats in an input sample. However, monitoring the simultaneous presence of multiple agents in a sample is still a challenging task. Multiple agents may often attach to the same probes, leading to low specificity. By using microarrays as a specific example, we introduce two methods based on conditional deduction and non-unique probes to detect multiple targets. We introduce three quality metrics, namely: effectiveness, cost and reliability to evaluate different designs of microarrays and propose two ILP/Pseudo-Boolean models for optimizing on these metrics. By applying on various synthetic and real datasets, we demonstrate the importance of these quality metrics in designing microarrays for multiple target detections

A Parsimony Approach to Biological Pathway Reconstruction/Inference for Genomes and Metagenomes

A common biological pathway reconstruction approach—as implemented by many automatic biological pathway services (such as the KAAS and RAST servers) and the functional annotation of metagenomic sequences—starts with the identification of protein functions or families (e.g., KO families for the KEGG database and the FIG families for the SEED database) in the query sequences, followed by a direct mapping of the identified protein families onto pathways. Given a predicted patchwork of individual biochemical steps, some metric must be applied in deciding what pathways actually exist in the genome or metagenome represented by the sequences. Commonly, and straightforwardly, a complete biological pathway can be identified in a dataset if at least one of the steps associated with the pathway is found. We report, however, that this naïve mapping approach leads to an inflated estimate of biological pathways, and thus overestimates the functional diversity of the sample from which the DNA sequences are derived. We developed a parsimony approach, called MinPath (Minimal set of Pathways), for biological pathway reconstructions using protein family predictions, which yields a more conservative, yet more faithful, estimation of the biological pathways for a query dataset. MinPath identified far fewer pathways for the genomes collected in the KEGG database—as compared to the naïve mapping approach—eliminating some obviously spurious pathway annotations. Results from applying MinPath to several metagenomes indicate that the common methods used for metagenome annotation may significantly overestimate the biological pathways encoded by microbial communities

An exact mathematical programming approach to multiple RNA sequence-structure alignment

One of the main tasks in computational biology is the computation of alignments of genomic sequences to reveal their commonalities. In case of DNA or protein sequences, sequence information alone is usually sufficient to compute reliable alignments. RNA molecules, however, build spatial conformations—the secondary structure—that are more conserved than the actual sequence. Hence, computing reliable alignments of RNA molecules has to take into account the secondary structure. We present a novel framework for the computation of exact multiple sequence-structure alignments: We give a graph- theoretic representation of the sequence-structure alignment problem and phrase it as an integer linear program. We identify a class of constraints that make the problem easier to solve and relax the original integer linear program in a Lagrangian manner. Experiments on a recently published benchmark show that our algorithms has a comparable performance than more costly dynamic programming algorithms, and outperforms all other approaches in terms of solution quality with an increasing number of input sequences

Shared probe design and existing microarray reanalysis using PICKY

<p>Abstract</p> <p>Background</p> <p>Large genomes contain families of highly similar genes that cannot be individually identified by microarray probes. This limitation is due to thermodynamic restrictions and cannot be resolved by any computational method. Since gene annotations are updated more frequently than microarrays, another common issue facing microarray users is that existing microarrays must be routinely reanalyzed to determine probes that are still useful with respect to the updated annotations.</p> <p>Results</p> <p><smcaps>PICKY</smcaps> 2.0 can design shared probes for sets of genes that cannot be individually identified using unique probes. <smcaps>PICKY</smcaps> 2.0 uses novel algorithms to track sharable regions among genes and to strictly distinguish them from other highly similar but nontarget regions during thermodynamic comparisons. Therefore, <smcaps>PICKY</smcaps> does not sacrifice the quality of shared probes when choosing them. The latest <smcaps>PICKY</smcaps> 2.1 includes the new capability to reanalyze existing microarray probes against updated gene sets to determine probes that are still valid to use. In addition, more precise nonlinear salt effect estimates and other improvements are added, making <smcaps>PICKY</smcaps> 2.1 more versatile to microarray users.</p> <p>Conclusions</p> <p>Shared probes allow expressed gene family members to be detected; this capability is generally more desirable than not knowing anything about these genes. Shared probes also enable the design of cross-genome microarrays, which facilitate multiple species identification in environmental samples. The new nonlinear salt effect calculation significantly increases the precision of probes at a lower buffer salt concentration, and the probe reanalysis function improves existing microarray result interpretations.</p

Optimal Robust Non-Unique Probe Selection Using Integer Linear Programming

Motivation Besides their prevalent use for analyzing gene expression, microarrays are an efficient tool for biological, medical, and industrial applications due to their ability to assess the presence or absence of biological agents, the targets, in a sample. Given a collection of genetic sequences of targets one faces the challenge of finding short oligonucleotides, the probes, which allow detection of targets in a sample. Each hybridization experiment determines whether the probe binds to its corresponding sequence in the target. Depending on the problem, the experiments are conducted using either unique or non-unique probes and usually assume that only one target is present in the sample. The problem at hand is to compute a design, i.e., a minimal set of probes that allows to infer the targets in the sample from the result of the hybridization experiment. If we allow to test for more than one target in the sample, the design of the probe set becomes difficult in the case of non-unique probes

Non-Unique oligonucleotide probe selection heuristics

The non-unique probe selection problem consists of selecting both unique and nonunique oligonucleotide probes for oligonucleotide microarrays, which are widely used tools to identify viruses or bacteria in biological samples. The non-unique probes, designed to hybridize to at least one target, are used as alternatives when the design of unique probes is particularly difficult for the closely related target genes. The goal of the non-unique probe selection problem is to determine a smallest set of probes able to identify all targets present in a biological sample. This problem is known to be NP-hard. In this thesis, several novel heuristics are presented based on greedy strategy, genetic algorithms and evolutionary strategy respectively for the minimization problem arisen from the non-unique probe selection using the best-known ILP formulation. Experiment results show that our methods are capable of reducing the number of probes required over the state-of-the-art methods

Improving the efficiency of Bayesian Network Based EDAs and their application in Bioinformatics

Estimation of distribution algorithms (EDAs) is a relatively new trend of stochastic optimizers which have received a lot of attention during last decade. In each generation, EDAs build probabilistic models of promising solutions of an optimization problem to guide the search process. New sets of solutions are obtained by sampling the corresponding probability distributions. Using this approach, EDAs are able to provide the user a set of models that reveals the dependencies between variables of the optimization problems while solving them. In order to solve a complex problem, it is necessary to use a probabilistic model which is able to capture the dependencies. Bayesian networks are usually used for modeling multiple dependencies between variables. Learning Bayesian networks, especially for large problems with high degree of dependencies among their variables is highly computationally expensive which makes it the bottleneck of EDAs. Therefore introducing efficient Bayesian learning algorithms in EDAs seems necessary in order to use them for large problems. In this dissertation, after comparing several Bayesian network learning algorithms, we propose an algorithm, called CMSS-BOA, which uses a recently introduced heuristic called max-min parent children (MMPC) in order to constrain the model search space. This algorithm does not consider a fixed and small upper bound on the order of interaction between variables and is able solve problems with large numbers of variables efficiently. We compare the efficiency of CMSS-BOA with the standard Bayesian network based EDA for solving several benchmark problems and finally we use it to build a predictor for predicting the glycation sites in mammalian proteins