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We present McGenus, an algorithm to predict RNA secondary structures withpseudoknots. The method is based on a classification of RNA structuresaccording to their topological genus. McGenus can treat sequences of up to 1000bases and performs an advanced stochastic search of their minimum free energystructure allowing for non trivial pseudoknot topologies. Specifically, McGenusemploys a multiple Markov chain scheme for minimizing a general scoringfunction which includes not only free energy contributions for pair stacking,loop penalties, etc. but also a phenomenological penalty for the genus of thepairing graph. The good performance of the stochastic search strategy wassuccessfully validated against TT2NE which uses the same free energyparametrization and performs exhaustive or partially exhaustive structuresearch, albeit for much shorter sequences (up to 200 bases). Next, the methodwas applied to other RNA sets, including an extensive tmRNA database, yieldingresults that are competitive with existing algorithms. Finally, it is shownthat McGenus highlights possible limitations in the free energy scoringfunction. The algorithm is available as a web-server athttp://ipht.cea.fr/rna/mcgenus.php .

McGenus: A Monte Carlo algorithm to predict RNA secondary structures with pseudoknots