Constraint-based strategy for pairwise RNA secondary structure prediction

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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. We come back in the present paper to a biologically simplified model that helps focusing on the algorithmic issues we want to overcome and 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
Original languageUnknown
Title of host publicationLecture Notes in Artificial Intelligence
EditorsLS Lopes, N Lau
PublisherSpringer
Pages86-97
Volume5816
ISBN (Print)978-3-642-04685-8
DOIs
Publication statusPublished - 1 Jan 2009
EventEPIA - Fourteenth Portuguese Conference on Artificial Intelligence -
Duration: 1 Jan 2009 → …

Conference

ConferenceEPIA - Fourteenth Portuguese Conference on Artificial Intelligence
Period1/01/09 → …

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