Using state machines to model the Ion Torrent sequencing process and to improve read error rates

Motivation: The importance of fast and affordable DNA sequencing methods for current day life sciences, medicine and biotechnology is hard to overstate. A major player is Ion Torrent, a pyrosequencing-like technology which produces flowgrams – sequences of incorporation values – which are converted into nucleotide sequences by a base-calling algorithm. Because of its exploitation of ubiquitous semiconductor technology and innovation in chemistry, Ion Torrent has been gaining popularity since its debut in 2011. Despite the advantages, however, Ion Torrent read accuracy remains a significant concern. Results: We present FlowgramFixer, a new algorithm for converting flowgrams into reads. Our key observation is that the incorporation signals of neighboring flows, even after normalization and phase correction, carry considerable mutual information and are important in making the correct base-call. We therefore propose that base-calling of flowgrams should be done on a read-wide level, rather than one flow at a time. We show that this can be done in linear-time by combining a state machine with a Viterbi algorithm to find the nucleotide sequence that maximizes the likelihood of the observed flowgram. FlowgramFixer is applicable to any flowgram-based sequencing platform. We demonstrate FlowgramFixer’s superior performance on Ion Torrent Escherichia coli data, with a 4.8% improvement in the number of high-quality mapped reads and a 7.1% improvement in the number of uniquely mappable reads. Availability: Binaries and source code of FlowgramFixer are freely available at: http://www.cs.tau.ac.il/~davidgo5/flowgramfixer.html. Contact: davidgo5@post.tau.ac.il