Large-scale evolutionary surveillance of the 2009 H1N1 influenza A virus using resequencing arrays

Lee, Charlie Wah Heng; Koh, Chee Wee; Chan, Yang Sun; Aw, Pauline Poh Kim; Loh, Kuan Hon; Han, Bing Ling; Thien, Pei Ling; Nai, Geraldine Yi Wen; Hibberd, Martin L.; Wong, Christopher W.; Sung, Wing-Kin

Traditional statistical and probabilistic sequence-calling techniques ascertain that a base call is of high confidence if they exceed pre-defined significance or probability thresholds. This approach works well for high-confidence base-calls but is inadequate to extract sufficient information from noisy base-calls. It is also difficult to determine the validity of a mutation call purely based on the distribution of hybridization intensities of its PM and MM probes. In this work, we have described two new hybridization intensity analysis methods that enable us to confidently identify true mutations and recover some noisy base calls. Compared to PBC, EvolSTAR has achieved superior call rates and accuracies, especially in low-concentration samples with high CT values. The robustness of the base calls enables our approach to be a practical large-scale evolutionary surveillance tool.