Phylogenetically enhanced statistical tools for RNA structure prediction

Motivation: Methods that predict the structure of molecules by looking for statistical correlation have been quite effective. Unfortunately, these met hods often disregard phylogenetic information in the sequences they analyze . Here, we present a number of statistics for RNA molecular-structure predi ction. Besides common pair-wise comparisons, we consider a few reasonable s tatistics 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 nat ure of these tests gives a wide choice of statistical tools for RNA structu re prediction. Results: Starting from statistics that incorporate phylogenetic information only as independent sequence evolution models for each position of a multi ple alignment, and extending this idea to a joint evolution model of two po sitions, we enhance the usual purely statistical methods (e.g. methods base d on the Mutual Information statistic) with the use of phylogenetic informa tion available in the sequences. In particular; we present a joint model ba sed on the HKY evolution model, and consequently a chi(2) test of independe nce for two positions. A significant part of this work is devoted to some m athematical analysis of these methods. We tested these statistics on region s of 16S and 23S rRNA, and tRNA.