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Single Species Gene Finding

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Comparative Gene Finding

Part of the book series: Computational Biology ((COBO,volume 11))

Abstract

A gene finding software typically consists of a main algorithm that serves as an umbrella for a large number of rather complex submodels. The submodels represent various features of a gene, such as exons, introns, and splice site models. Each submodel scores the probability, or likelihood, that each given sequence region constitute the corresponding gene feature, and then these scores are passed on up to the main algorithm. The main algorithm integrates the scores and parses the input sequence into a set of gene predictions. This chapter covers a five of the most commonly used mathematical models used as main algorithms in single species gene finding. The models are hidden Markov models, generalized hidden Markov models, interpolated Markov models, neural networks, and decision trees. Each model is described in algorithmic detail, and each model section is finished off by exemplifying a gene finder that uses the model in question.

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Authors and Affiliations

  1. Dept. Mathematical Sciences, Chalmers University of Technology, Eklandgatan 86, 412 96, Göteborg, Sweden

    Dr. Marina Axelson-Fisk

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  1. Dr. Marina Axelson-Fisk
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Correspondence to Marina Axelson-Fisk .

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Axelson-Fisk, M. (2010). Single Species Gene Finding. In: Comparative Gene Finding. Computational Biology, vol 11. Springer, London. https://doi.org/10.1007/978-1-84996-104-2_2

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  • DOI: https://doi.org/10.1007/978-1-84996-104-2_2

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  • Publisher Name: Springer, London

  • Print ISBN: 978-1-84996-103-5

  • Online ISBN: 978-1-84996-104-2

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