An Evolutionary Approach to the Non-unique Oligonucleotide Probe Selection Problem

Authors:
Lili Wang;Alioune Ngom;Robin Gras;Luis Rueda
Affiliations:
School of Computer Science, 5115 Lambton Tower, University of Windsor, Windsor, Canada N9B 3P4;School of Computer Science, 5115 Lambton Tower, University of Windsor, Windsor, Canada N9B 3P4;School of Computer Science, 5115 Lambton Tower, University of Windsor, Windsor, Canada N9B 3P4;School of Computer Science, 5115 Lambton Tower, University of Windsor, Windsor, Canada N9B 3P4
Venue:
Transactions on Computational Systems Biology X
Year:
2008

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Abstract

In order to accurately measure the gene expression levels in microarray experiments, it is crucial to design unique , highly specific and sensitive oligonucleotide probes for the identification of biological agents such as genes in a sample. Unique probes are difficult to obtain for closely related genes such as the known strains of HIV genes. The non-unique probe selection problem is to select a probe set that is able to uniquely identify targets in a biological sample, while containing a minimal number of probes. This is an NP-hard problem. We define a probe selection function that allows to decide which are the best probes to include in or exclude from a candidate probe set. We then propose a new deterministic greedy heuristic that uses the selection for solving the non-unique probe selection problem. Finally, we combine the selection function with an evolutionary method for finding near minimal non-unique probe sets. When used on benchmark data sets, our greedy method outperforms current greedy heuristics for non-unique probe selection in most instances. Our genetic algorithm also produced excellent results when compared to advanced methods introduced in the literature for the non-unique probe selection problem.