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Background  Genome sequencing provides a powerful tool for pathogen detection and can help resolve outbreaks that pose public safety and health risks. Mapping of DNA reads to genomes plays a fundamental role in this approach, where accurate alignment and classification of sequencing data is crucial. Standard mapping methods crudely treat bases as independent from their neighbors. Accuracy might be improved by using higher order paired hidden Markov models (HMMs), which model neighbor effects, but introduce design and implementation issues that have typically made them impractical for read mapping applications. We present a variable-order paired HMM that we term VarHMM, which addresses central issues involved with higher order modeling for sequence alignment.    Results  Compared with existing alignment methods, VarHMM is able to model higher order distributions and quantify alignment probabilities with greater detail and accuracy. In a series of comparison tests, in which Ion Torrent sequenced DNA was mapped to similar bacterial strains, VarHMM consistently provided better strain discrimination than any of the other alignment methods that we compared with.    Conclusions  Our results demonstrate the advantages of higher ordered probability distribution modeling and also suggest that further development of such models would benefit read mapping in a range of other applications as well.    Electronic supplementary material  The online version of this article (doi:10.1186/s12859-017-1710-0) contains supplementary material, which is available to authorized users.

Variable-order sequence modeling improves bacterial strain discrimination for Ion Torrent DNA reads