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Bioinformatics Research and Applications

Volume 4463 of the series Lecture Notes in Computer Science pp 394-405

Markov Model Variants for Appraisal of Coding Potential in Plant DNA

  • Michael E. SparksAffiliated withDepartment of Genetics, Development and Cell Biology, Iowa State University, Ames, IA 50011
  • , Volker BrendelAffiliated withDepartment of Genetics, Development and Cell Biology, Iowa State University, Ames, IA 50011Department of Statistics, Iowa State University, Ames, IA 50011
  • , Karin S. DormanAffiliated withDepartment of Genetics, Development and Cell Biology, Iowa State University, Ames, IA 50011Department of Statistics, Iowa State University, Ames, IA 50011

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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, 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.