Basecalling using hidden Markov models

Abstract

In this paper we propose hidden Markov models to model electropherograms from DNA sequencing equipment and perform basecalling. We model the state emission densities using artificial neural networks, and modify the Baum–Welch reestimation procedure to perform training. Moreover, we develop a method that exploits consensus sequences to label training data, thus minimizing the need for hand labeling. We propose the same method for locating an electropherogram in a longer DNA sequence. We also perform a careful study of the basecalling errors and propose alternative HMM topologies that might further improve performance. Our results demonstrate the potential of these models. Based on these results, we conclude by suggesting further research directions.