Bioinformatics Vol. 16 no. 6 2000
Pages 501-512
© 2000 Oxford University Press
Original Paper |
Phylogenetically enhanced statistical tools for RNA structure prediction
1 Dept. of Applied Mathematics, Box 526,
University of Colorado, Boulder, CO 80309, USA
2 Dept. of Molecular, Cellular and
Developmental Biology, Box 347, University of Colorado, Boulder, CO
80309, USA
3 Dept. of Genetics, Washington University
School of Medicine, 660 S. Euclid, Box 8232, St. Louis, MO 63110,
USA
Received on November 2, 1999
; revised on January 20, 2000
; accepted on January 28, 2000
Motivation: Methods that predict the structure of molecules by looking for statistical correlation have been quite effective. Unfortunately, these methods often disregard phylogenetic information in the sequences they analyze. Here, we present a number of statistics for RNA molecular-structure prediction. Besides common pair-wise comparisons, we consider a few reasonable statistics for base-triple predictions, and present an elaborate analysis of these methods. All these statistics incorporate phylogenetic relationships of the sequences in the analysis to varying degrees, and the different nature of these tests gives a wide choice of statistical tools for RNA structure prediction.
Results: Starting from statistics that incorporate phylogenetic
information only as independent sequence evolution models for each
position of a multiple alignment, and extending this idea to a joint
evolution model of two positions, we enhance the usual purely
statistical methods (e.g. methods based on the Mutual Information
statistic) with the use of phylogenetic information available in the
sequences. In particular, we present a joint model based on the HKY
evolution model, and consequently a 
test of
independence for two positions. A significant part of this work is
devoted to some mathematical analysis of these methods. We tested
these statistics on regions of 16S and 23S rRNA, and tRNA.
Availability: The programs are available upon request.
Contact: slava{at}colorado.edu
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