Abstract
BackgroundGenome-wide identification of specific oligonucleotides (oligos) is a computationally-intensive task and is a requirement for designing microarray probes, primers, and siRNAs. An artificial neural network (ANN) is a machine learning technique that can effectively process complex and high noise data. Here, ANNs are applied to process the unique subsequence distribution for prediction of specific oligos.ResultsWe present a novel and efficient algorithm, named the integration of ANN and BLAST (IAB) algorithm, to identify specific oligos. We establish the unique marker database for human and rat gene index databases using the hash table algorithm. We then create the input vectors, via the unique marker database, to train and test the ANN. The trained ANN predicted the specific oligos with high efficiency, and these oligos were subsequently verified by BLAST. To improve the prediction performance, the ANN over-fitting issue was avoided by early stopping with the best observed error and a k-fold validation was also applied. The performance of the IAB algorithm was about 5.2, 7.1, and 6.7 times faster than the BLAST search without ANN for experimental results of 70-mer, 50-mer, and 25-mer specific oligos, respectively. In addition, the results of polymerase chain reactions showed that the primers predicted by the IAB algorithm could specifically amplify the corresponding genes. The IAB algorithm has been integrated into a previously published comprehensive web server to support microarray analysis and genome-wide iterative enrichment analysis, through which users can identify a group of desired genes and then discover the specific oligos of these genes.ConclusionThe IAB algorithm has been developed to construct SpecificDB, a web server that provides a specific and valid oligo database of the probe, siRNA, and primer design for the human genome. We also demonstrate the ability of the IAB algorithm to predict specific oligos through polymerase chain reaction experiments. SpecificDB provides comprehensive information and a user-friendly interface.
Highlights
Genome-wide identification of specific oligonucleotides is a computationallyintensive task and is a requirement for designing microarray probes, primers, and small interfering RNA (siRNA)
Several approaches were developed to design unique oligos, such as an information-theoretical method based on maximum entropy, which has been applied to the design of probe sets [12]; a method based on matching the frequency of sequence landscapes, which was used to select optimal oligos for E. coli, S. cerevisiae, and C. elegans [13]; suffix trees, which has been used to select the organismspecific signature oligos [14]; the design of genome-wide specific oligos based on basic local alignment search tool (BLAST) [15]; and the smart filtering technique, which was employed to avoid redundant computation while maintaining accuracy [16]
Construction of unique marker database and the architecture of artificial neural network (ANN) The input vector of the ANN was derived from the density of the unique subsequences (Ud) between 10-mer and 26mer (Figure 1)
Summary
Genome-wide identification of specific oligonucleotides (oligos) is a computationallyintensive task and is a requirement for designing microarray probes, primers, and siRNAs. Several approaches were developed to design unique oligos, such as an information-theoretical method based on maximum entropy, which has been applied to the design of probe sets [12]; a method based on matching the frequency of sequence landscapes, which was used to select optimal oligos for E. coli, S. cerevisiae, and C. elegans [13]; suffix trees, which has been used to select the organismspecific signature oligos [14]; the design of genome-wide specific oligos based on basic local alignment search tool (BLAST) [15]; and the smart filtering technique, which was employed to avoid redundant computation while maintaining accuracy [16]
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