Abstract
BackgroundThe design of long oligonucleotides for spotted DNA microarrays requires detailed attention to ensure their optimal performance in the hybridization process. The main challenge is to select an optimal oligonucleotide element that represents each genetic locus/gene in the genome and is unique, devoid of internal structures and repetitive sequences and its Tm is uniform with all other elements on the microarray. Currently, all of the publicly available programs for DNA long oligonucleotide microarray selection utilize various combinations of cutoffs in which each parameter (uniqueness, Tm, and secondary structure) is evaluated and filtered individually. The use of the cutoffs can, however, lead to information loss and to selection of suboptimal oligonucleotides, especially for genomes with extreme distribution of the GC content, a large proportion of repetitive sequences or the presence of large gene families with highly homologous members.ResultsHere we present the program OligoRankPick which is using a weighted rank-based strategy to select microarray oligonucleotide elements via an integer weighted linear function. This approach optimizes the selection criteria (weight score) for each gene individually, accommodating variable properties of the DNA sequence along the genome. The designed algorithm was tested using three microbial genomes Escherichia coli, Saccharomyces cerevisiae and the human malaria parasite species Plasmodium falciparum. In comparison to other published algorithms OligoRankPick provides significant improvements in oligonucleotide design for all three genomes with the most significant improvements observed in the microarray design for P. falciparum whose genome is characterized by large fluctuations of GC content, and abundant gene duplications.ConclusionOligoRankPick is an efficient tool for the design of long oligonucleotide DNA microarrays which does not rely on direct oligonucleotide exclusion by parameter cutoffs but instead optimizes all parameters in context of each other. The weighted rank-sum strategy utilized by this algorithm provides high flexibility of oligonucleotide selection which accommodates extreme variability of DNA sequence properties along genomes of many organisms.
Highlights
The design of long oligonucleotides for spotted DNA microarrays requires detailed attention to ensure their optimal performance in the hybridization process
Each score is transformed into a rank and a weighted rank-sum is calculated for each oligonucleotide using the weighted optimization strategy
In our approach the transformations of score values calculated for each parameter into rank numbers allows us to uniformly assesses and adjust the contribution of different parameters for the optimal oligonucleotide selection
Summary
The design of long oligonucleotides for spotted DNA microarrays requires detailed attention to ensure their optimal performance in the hybridization process. The main challenge is to select an optimal oligonucleotide element that represents each genetic locus/gene in the genome and is unique, devoid of internal structures and repetitive sequences and its Tm is uniform with all other elements on the microarray. For the selection of the optimal oligonucleotide candidates, four major parameters are being evaluated: (i) uniqueness which analyzes other possible cross-hybridization targets in the genome; (ii) sequence complexity which evaluates the presence of short nucleotide repeats; (iii) melting temperature (Tm) or GC content which ensures a uniform hybridization efficiency across the microarray; and (iv) level of internal secondary structures which helps to avoid all possible self-binding interference with the specific target hybridization. In principle each of these properties can be calculated individually for every potential oligonucleotide candidate, the main challenge that remains is to derive a selection strategy that combines these parameters and selects the most optimal oligonucleotide representative for a given genetic locus/gene
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