Abstract
BackgroundPathogen detection using DNA microarrays has the potential to become a fast and comprehensive diagnostics tool. However, since pathogen detection chips currently utilize random primers rather than specific primers for the RT-PCR step, bias inherent in random PCR amplification becomes a serious problem that causes large inaccuracies in hybridization signals.ResultsIn this paper, we study how the efficiency of random PCR amplification affects hybridization signals. We describe a model that predicts the amplification efficiency of a given random primer on a target viral genome. The prediction allows us to filter false-negative probes of the genome that lie in regions of poor random PCR amplification and improves the accuracy of pathogen detection. Subsequently, we propose LOMA, an algorithm to generate random primers that have good amplification efficiency. Wet-lab validation showed that the generated random primers improve the amplification efficiency significantly.ConclusionThe blind use of a random primer with attached universal tag (random-tagged primer) in a PCR reaction on a pathogen sample may not lead to a successful amplification. Thus, the design of random-tagged primers is an important consideration when performing PCR.
Highlights
Pathogen detection using DNA microarrays has the potential to become a fast and comprehensive diagnostics tool
Generating the Tag of a Random Primer using LOMA Amplification failure may occur if there are many regions of the target genome where the tagged random primer cannot bind
We propose LOMA (Least Occurrence Merging Algorithm), a more deterministic and faster algorithm to generate an efficient tag for a target genome va
Summary
Pathogen detection using DNA microarrays has the potential to become a fast and comprehensive diagnostics tool. Since pathogen detection chips currently utilize random primers rather than specific primers for the RT-PCR step, bias inherent in random PCR amplification becomes a serious problem that causes large inaccuracies in hybridization signals. Pathogen detection has become an important part of research in diagnostics and drug discovery. To this day, the accurate and sensitive detection of infectious disease agents is still thwarted with difficulties. Detection tools are typically designed from sequence information stored in public databases. As some viruses mutate or recombine, their sequence information may become inaccurate. Sequence information for novel pathogens such as severe acute respiratory syndrome (SARS). Detection tools will most probably fail when such scenarios happen
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