Fast splice site detection using information content and feature reduction.
BACKGROUND
Accurate identification of splice sites in DNA sequences plays a key role in the prediction of gene structure in eukaryotes. Already many computational methods have been proposed for the detection of splice sites and some of them showed high prediction accuracy. However, most of these methods are limited in terms of their long computation time when applied to whole genome sequence data.


RESULTS
In this paper we propose a hybrid algorithm which combines several effective and informative input features with the state of the art support vector machine (SVM). To obtain the input features we employ information content method based on Shannon's information theory, Shapiro's score scheme, and Markovian probabilities. We also use a feature elimination scheme to reduce the less informative features from the input data.


CONCLUSIONS
In this study we propose a new feature based splice site detection method that shows improved acceptor and donor splice site detection in DNA sequences when the performance is compared with various state of the art and well known methods.
Baten
A K M A
AK
Biomechanical Engineering Research Group, Department of Mechanical Engineering, Melbourne School of Engineering, The University of Melbourne, Victoria 3010, Australia. a.baten@pgrad.unimelb.edu.au
Halgamuge
S K
SK
Chang
B C H
BC
D000465:Algorithms; D001185:Artificial Intelligence; D001483:Base Sequence; D019295:Computational Biology / Q000379:methods*; D004247:DNA / Q000737:chemistry*; D008390:Markov Chains; D008957:Models, Genetic; D015233:Models, Statistical; D010363:Pattern Recognition, Automated / Q000379:methods; D012326:RNA Splicing; D016415:Sequence Alignment / Q000379:methods; D017422:Sequence Analysis, DNA / Q000379:methods; D020539:Sequence Analysis, Protein / Q000379:methods; D012984:Software
D004247:DNA