A tutorial on hidden Markov models and selected applications in speech recognition
Readings in speech recognition
Robust Full Bayesian Learning for Radial Basis Networks
Neural Computation
Modelling electropherogram data for DNA sequencing using variable dimension MCMC
ICASSP '00 Proceedings of the Acoustics, Speech, and Signal Processing, 2000. on IEEE International Conference - Volume 06
Sequential methods for DNA sequencing
ICASSP '01 Proceedings of the Acoustics, Speech, and Signal Processing, 200. on IEEE International Conference - Volume 02
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It has been shown that electropherograms of DNA sequences can be modeled with hidden Markov models. Basecalling, the procedure that determines the sequence of bases from the given eletropherogram, can then be performed using the Viterbi algorithm. A training step is required prior to basecalling in order to estimate the HMM parameters. In this paper, we propose a Bayesian approach which employs the Markov chain Monte Carlo (MCMC) method to perform basecalling. Such an approach not only allows one to naturally encode the prior biological knowledge into the basecalling algorithm, it also exploits both the training data and the basecalling data in estimating the HMM parameters, leading to more accurate estimates. Using the recently sequenced genome of the organism Legionella pneumophila we show that the MCMC basecaller outperforms the state-of-the-art basecalling algorithm in terms of total errors while requiring much less training than other proposed statistical basecallers.