Accelerated probabilistic inference of RNA structure evolution

Ian Holmes

doi:10.1186/1471-2105-6-73

Abstract

BackgroundPairwise stochastic context-free grammars (Pair SCFGs) are powerful tools for evolutionary analysis of RNA, including simultaneous RNA sequence alignment and secondary structure prediction, but the associated algorithms are intensive in both CPU and memory usage. The same problem is faced by other RNA alignment-and-folding algorithms based on Sankoff's 1985 algorithm. It is therefore desirable to constrain such algorithms, by pre-processing the sequences and using this first pass to limit the range of structures and/or alignments that can be considered.ResultsWe demonstrate how flexible classes of constraint can be imposed, greatly reducing the computational costs while maintaining a high quality of structural homology prediction. Any score-attributed context-free grammar (e.g. energy-based scoring schemes, or conditionally normalized Pair SCFGs) is amenable to this treatment. It is now possible to combine independent structural and alignment constraints of unprecedented general flexibility in Pair SCFG alignment algorithms. We outline several applications to the bioinformatics of RNA sequence and structure, including Waterman-Eggert N-best alignments and progressive multiple alignment. We evaluate the performance of the algorithm on test examples from the RFAM database.ConclusionA program, Stemloc, that implements these algorithms for efficient RNA sequence alignment and structure prediction is available under the GNU General Public License.

Highlights

Pairwise stochastic context-free grammars (Pair Stochastic Context-Free Grammars (SCFGs)) are powerful tools for evolutionary analysis of RNA, including simultaneous RNA sequence alignment and secondary structure prediction, but the associated algorithms are intensive in both CPU and memory usage
Whereas the structural signal encoded in a single RNA gene is rather weak and may be barely distinguishable from the secondary structure of a random sequence [6], the covariation signal increases with every additional sequence considered
The purpose of this paper is to report our progress on general pairwise constrained versions of Sankoff's algorithm

Summary

Introduction

Pairwise stochastic context-free grammars (Pair SCFGs) are powerful tools for evolutionary analysis of RNA, including simultaneous RNA sequence alignment and secondary structure prediction, but the associated algorithms are intensive in both CPU and memory usage. One can view the alignment and structure prediction as a combined problem, to be solved simultaneously This is the approach taken in this paper, and by earlier programs such as FOLDALIGN [10], DYNALIGN [11], CARNAC [12], QRNA [9] and our dart library, introduced in a previous paper [13] and extended here. In this framework, fixing of the alignment can be viewed as (page number not for citation purposes)

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