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

Abstract

Background: 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.

Results: 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.

Conclusion: 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.

Figure 1

Heatmap of probe signal intensity for a RSV B sample following random RT-PCR. Red regions correspond to probes that did not have signal intensities above threshold. As probe signal intensity increases, the heatmap changes from red to orange to yellow to white.

Figure 2

Flowchart of LOMA. Flowchart of LOMA with n = 17, k = 9 and T = 0.

Figure 3

Application of AES on a RSV sample. An RSV patient sample was amplified separately using primer A1, primer A2 and primer A3. Hybridization signals of probes after amplification by each primer are shown as a heatmap. The probes that have detectable signals above threshold are shown in orange/yellow in the corresponding heatmaps. The graph below the heatmaps shows our AES prediction for the three primers: A1 (orange line), primer A2 (pink line) and primer A3 (dark blue line). Our AES predictions closely matches the actual hybridization results, ie primer A3 performs slightly better than primer A2 but both A3 and A2 performs significantly better than A1 on RSV.

Figure 4

Application of AES on a HMPV sample. An HMPV patient sample was amplified separately using primer A1, primer A2 and primer A3. Hybridization signals of probes after amplification by each primer are shown as a heatmap. The probes that have detectable signals above threshold are shown in orange/yellow in the corresponding heatmaps. The graph below the heatmaps shows our AES prediction for the three primers: A1 (orange line), primer A2 (pink line) and primer A3 (dark blue line). Our AES predictions closely matches the actual hybridization results, ie primer A3 performs slightly better than primer A2 but both A3 and A2 performs significantly better than A1 on HMPV.

Figure 5

Design of multiple random-tagged primers to amplify a target genome g. Original primer p has a region with low AES on g. We design additional primer q such that it has high AES in that region.

Figure 6

RT-PCR binding process. RT-PCR binding process of a pair of random primers on a target virus sequence v_a.