Hidden Markov models and optimized sequence alignments.

L Smith; L Yeganova; W J Wilbur

doi:10.1016/s1476-9271(02)00096-8

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Hidden Markov models and optimized sequence alignments.

L Smith·L Yeganova·W J Wilbur

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Abstract

We present a formulation of the Needleman-Wunsch type algorithm for sequence alignment in which the mutation matrix is allowed to vary under the control of a hidden Markov process. The fully trainable model is applied to two problems in bioinformatics: the recognition of related gene/protein names and the alignment and scoring of homologous proteins.

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Animals Computational Biology Databases, Genetic Genes Humans MEDLINE Markov Chains Mice Models, Statistical Sequence Alignment Sequence Homology, Nucleic Acid Journal Article

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L Smith, L Yeganova, W J Wilbur. 2003. Hidden Markov models and optimized sequence alignments.. https://doi.org/10.1016/s1476-9271(02)00096-8

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