Discriminative Mutation Chains in Virus Sequences

Influenza viruses mutate frequently and new mutations may emerge while old mutations disappear over a period of time. In addition, some mutations may be dominant in one sub-population but not in the other. Discovering such mutations can help to customize vaccines to increase the effectiveness for targeted group of people. In this paper, we study the problem of mining discriminative mutation chains from two influenza A virus protein datasets, D1 and D2, such that the mutations are frequent and significant in one dataset but infrequent and insignificant in the other dataset. We present an efficient algorithm called DMMiner to discover discriminative mutation chains. Experiments results on the real world influenza A virus protein datasets reveal that DMMiner is able to find interesting discriminative mutation chains involving the H1N1 2009 influenza A virus as well as region-specific mutations involving H5N1.