Markov Model Variants for Appraisal of Coding Potential in Plant DNA

  • Michael E. Sparks
  • Volker Brendel
  • Karin S. Dorman
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4463)


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, χ 2-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.


Markov Chain Markov Model Intron Sequence Markov Chain Model Chain Order 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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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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