Modeling splicing sites with pairwise correlations.
BACKGROUND
A new method for finding subtle patterns in sequences is introduced. It approximates the multiple correlations among residuals with pair-wise correlations, with the learning cost O(m(2)n) where n is the number of training sequences, each of length m. The method suits to model splicing sites in human DNA, which are reported to have higher-order dependencies.


RESULTS
By computational experiments, the prediction accuracy of our model was shown to surpass that of previously reported Markov models for the prediction of acceptor sites in human.


BACKGROUND
The C++ source code is available on request from the authors.
Arita
Masanori
M
Computational Biology Research Center (CBRC), National Institute of Advanced Industrial Science and Technology (AIST), Tokyo, Japan. m-arita@aist.go.jp
Tsuda
Koji
K
Asai
Kiyoshi
K
D000465:Algorithms; D020029:Base Pairing / Q000235:genetics; D002874:Chromosome Mapping / Q000379:methods*; D003198:Computer Simulation; D015894:Genome, Human; D006801:Humans; D008957:Models, Genetic; D015233:Models, Statistical; D022821:RNA Splice Sites / Q000235:genetics*; D016415:Sequence Alignment / Q000379:methods*; D017422:Sequence Analysis, DNA / Q000379:methods*; D012689:Sequence Homology, Nucleic Acid; D012984:Software; D013223:Statistics as Topic
D022821:RNA Splice Sites