Constraint-based strategy for pairwise RNA secondary structure prediction

Abstract

RNA secondary structure prediction depends on context. When only a few (sometimes putative) RNA homologs are available, one of the most famous approach is based on a set of recursions proposed by Sankoff in 1985. Although this modus operandi insures an algorithmically optimal result, the main drawback lies in its prohibitive time and space complexities. A series of heuristics were developed to face that difficulty and turn the recursions usable. In front of the inescapable intricacy of the question when handling the full thermodynamic model, we come back in the present paper to a biologically simplified model that helps focusing on the algorithmic issues we want to overcome. We expose our ongoing developments by using the constraints framework which we believe is a powerful paradigm for heuristic design. We give evidence that the main heuristics proposed by others (structural and alignment banding, multi-loop restriction) can be refined in order to produce a substantial gain both in time computation and space requirements. A beta implementation of our approach, that we named ARNICA, exemplify that gain on a sample set that remains unaffordable to other methods. The sources and sample tests of ARNICA are available at http://centria.di.fct.unl.pt/~op/arnica.tar.gz © 2009 Springer Berlin Heidelberg.