Oligonucleotide Design by Multilevel Optimization

Background: Many molecular biology experiments make use of small RNA or DNA sequences called oligonucleotides. Their success is highly dependent on oligonucleotide design. Several constraints and properties of such oligonucleotides vary among applications such as long oligos for micro-arrays, primer pairs for PCR amplifications and sequencing, siRNA to knock down gene expression. Most of methods proposed in the literature are usualy conceived with a dedicated and specific application in mind. The aim of our work is to specify a general framework to build design applications. Every given algorithm is a building block that can be combined to create a customized oligonucleotide design pipeline. Results: We present a collection of complementary techniques for the election of high quality oligonucleotides for PCR and DNA array experiments. The general pipeline proceeds by successive selection of best candidates on various criteria like minimization of secondary structures, using statistical mechanics approaches, and maximization of specificity. The latter is optimized through performing searches on genome among a short list of finalist candidates. Furthermore, we maintain diversity in the population of candidates to ensure domain exploration. Conclusions: The method of candidate selection we developed yields high-quality oligonucleotides and is implemented in a collection of design applications that is available a