M. Winslett (Ed.): SSDBM 2009, LNCS 5566, pp. 200–216, 2009. © Springer-Verlag Berlin Heidelberg 2009 Covariant Evolutionary Event Analysis for Base Interaction Prediction Using a Relational Database Management System for RNA Weijia Xu 1 , Stuart Ozer 2 , and Robin R. Gutell 3 1 Texas Advanced Computing Center, The University of Texas at Austin, Austin, Texas, USA xwj@tacc.utexas.edu 2 One Microsoft Way Redmond, WA., Seattle, Washington, USA stuarto@microsoft.com 3 Center of Computational Biology and Bioinformatics The University of Texas at Austin, Austin, Texas, USA Robin.gutell@icmb.utexas.edu Abstract. With an increasingly large amount of sequences properly aligned, comparative sequence analysis can accurately identify not only common struc- tures formed by standard base pairing but also new types of structural elements and constraints. However, traditional methods are too computationally expen- sive to perform well on large scale alignment and less effective with the sequences from diversified phylogenetic classifications. We propose a new ap- proach that utilizes coevolutional rates among pairs of nucleotide positions using phylogenetic and evolutionary relationships of the organisms of aligned sequences. With a novel data schema to manage relevant information within a relational database, our method, implemented with a Microsoft SQL Server 2005, showed 90% sensitivity in identifying base pair interactions among 16S ribosomal RNA sequences from Bacteria, at a scale 40 times bigger and 50% better sensitivity than a previous study. The results also indicated covariation signals for a few sets of cross-strand base stacking pairs in secondary structure helices, and other subtle constraints in the RNA structure. Keywords: Biological database, Bioinformatics, Sequence Analysis, RNA. 1 Introduction Comparative sequence analysis has been successfully utilized to identify RNA structures that are common to different families of properly aligned RNA sequences. Here we present enhance the capabilities of relational database management for com- parative sequence analysis through extended data schema and integrative analysis routines. The novel data schema establishes the foundation that analyzes multiple dimensions of RNA sequence, sequence alignment, different aspects of 2D and 3D RNA structure information and phylogenetic/evolution information. The integrative analysis routines are unique, scale to large volumes of data, and provide better accuracy and performance. With these database enhancements, we details the