Bioinformatics, Vol 15, 362-369, Copyright © 1999 by Oxford University Press
U Ohler, S Harbeck, H Niemann, E Noth and MG Reese
MOTIVATION: We describe a new content-based approach for the detection of
promoter regions of eukaryotic protein encoding genes. Our system is based
on three interpolated Markov chains (IMCs) of different order which are
trained on coding, non-coding and promoter sequences. It was recently shown
that the interpolation of Markov chains leads to stable parameters and
improves on the results in microbial gene finding (Salzberg et al., Nucleic
Acids Res., 26, 544-548, 1998). Here, we present new methods for an
automated estimation of optimal interpolation parameters and show how the
IMCs can be applied to detect promoters in contiguous DNA sequences. Our
interpolation approach can also be employed to obtain a reliable scoring
function for human coding DNA regions, and the trained models can easily be
incorporated in the general framework for gene recognition systems.
RESULTS: A 5-fold cross- validation evaluation of our IMC approach on a
representative sequence set yielded a mean correlation coefficient of 0.84
(promoter versus coding sequences) and 0.53 (promoter versus non-coding
sequences). Applied to the task of eukaryotic promoter region
identification in genomic DNA sequences, our classifier identifies 50% of
the promoter regions in the sequences used in the most recent review and
comparison by Fickett and Hatzigeorgiou ( Genome Res., 7, 861-878, 1997),
while having a false-positive rate of 1/849 bp.
ARTICLES
Interpolated markov chains for eukaryotic promoter recognition
University of Erlangen-Nuremberg, Martensstrasse 3, D-91058 Erlangen, Germany. ohler@informatik.uni-erlangen.de
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