LOMA: a fast method to generate efficient tagged-random primers despite amplification bias of random PCR on pathogens

Pathogen 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.

In 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.

The 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.