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Constraint-based strategy for pairwise RNA secondary structure prediction
2009
Constraint-based strategy for pairwise RNA secondary structure prediction
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
In proceedings
Olivier Perriquet, Pedro Barahona
L. Seabra Lopes, Nuno Lau
Progress in Artificial Intelligence
Lecture Notes in Artificial Intelligence
Springer
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5816
86-97
978-3-642-04685-8
-
-
-
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Olivier Perriquet and Pedro Barahona, Constraint-based strategy for pairwise RNA secondary structure prediction, in: L. Seabra Lopes and Nuno Lau (eds), Progress in Artificial Intelligence, Lecture Notes in Artificial Intelligence, Springer, Vol. 5816, ISBN 978-3-642-04685-8, Pag. 86-97, 2009.
<a href="/people/members/view.php?code=5ea29c73f46cfe62280b03c53abb361f" class="author">Olivier Perriquet</a> and <a href="/people/members/view.php?code=7e27bc13fad97e99cd21ea6914d55659" class="author">Pedro Barahona</a>, <b>Constraint-based strategy for pairwise RNA secondary structure prediction</b>, in: L. Seabra Lopes and Nuno Lau (eds), <u>Progress in Artificial Intelligence</u>, Lecture Notes in Artificial Intelligence, <a href="http://www.springer.com" title="Link to external entity..." target="_blank" class="publisher">Springer</a>, Vol. 5816, ISBN 978-3-642-04685-8, Pag. 86-97, 2009.
@inproceedings {Constraint-based strategy for pairwise RNA secondary structure prediction, author = {Olivier Perriquet and Pedro Barahona}, editor = {L. Seabra Lopes and Nuno Lau}, title = {Constraint-based strategy for pairwise RNA secondary structure prediction}, booktitle = {Progress in Artificial Intelligence}, series = {Lecture Notes in Artificial Intelligence}, publisher = {Springer}, volume = {5816}, pages = {86-97}, isbn = {978-3-642-04685-8}, 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}, year = {2009}, }
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