Markov Model Variants for Appraisal of Coding Potential in Plant DNA

  • Michael E. Sparks
  • Volker Brendel
  • Karin S. Dorman
Conference paper

DOI: 10.1007/978-3-540-72031-7_36

Part of the Lecture Notes in Computer Science book series (LNCS, volume 4463)
Cite this paper as:
Sparks M.E., Brendel V., Dorman K.S. (2007) Markov Model Variants for Appraisal of Coding Potential in Plant DNA. In: Măndoiu I., Zelikovsky A. (eds) Bioinformatics Research and Applications. ISBRA 2007. Lecture Notes in Computer Science, vol 4463. Springer, Berlin, Heidelberg

Abstract

Markov chain models are commonly used for content-based appraisal of coding potential in genomic DNA. The ability of these models to distinguish coding from non-coding sequences depends on the method of parameter estimation, the validity of the estimated parameters for the species of interest, and the extent to which oligomer usage characterizes coding potential. We assessed performances of Markov chain models in two model plant species, Arabidopsis and rice, comparing canonical fixed-order, χ2-interpolated, and top-down and bottom-up deleted interpolated Markov models. All methods achieved comparable identification accuracies, with differences usually within statistical error. Because classification performance is related to G+C composition, we also considered a strategy where training and test data are first partitioned by G+C content. All methods demonstrated considerable gains in accuracy under this approach, especially in rice. The methods studied were implemented in the C programming language and organized into a library, IMMpractical, distributed under the GNU LGPL.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Michael E. Sparks
    • 1
  • Volker Brendel
    • 1
    • 2
  • Karin S. Dorman
    • 1
    • 2
  1. 1.Department of Genetics, Development and Cell Biology, Iowa State University, Ames, IA 50011USA
  2. 2.Department of Statistics, Iowa State University, Ames, IA 50011USA

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