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 Akito Taneda Advances and Applications in Bioinformatics and Chemistry , 2010, Abstract: Akito TanedaGraduate School of Science and Technology, Hirosaki University, Hirosaki, JapanAbstract: Artificially synthesized RNA molecules have recently come under study since such molecules have a potential for creating a variety of novel functional molecules. When designing artificial RNA sequences, secondary structure should be taken into account since functions of noncoding RNAs strongly depend on their structure. RNA inverse folding is a methodology for computationally exploring the RNA sequences folding into a user-given target structure. In the present study, we developed a multi-objective genetic algorithm, MODENA (Multi-Objective DEsign of Nucleic Acids), for RNA inverse folding. MODENA explores the approximate set of weak Pareto optimal solutions in the objective function space of 2 objective functions, a structure stability score and structure similarity score. MODENA can simultaneously design multiple different RNA sequences at 1 run, whose lowest free energies range from a very stable value to a higher value near those of natural counterparts. MODENA and previous RNA inverse folding programs were benchmarked with 29 target structures taken from the Rfam database, and we found that MODENA can successfully design 23 RNA sequences folding into the target structures; this result is better than those of the other benchmarked RNA inverse folding programs. The multi-objective genetic algorithm gives a useful framework for a functional biomolecular design. Executable files of MODENA can be obtained at http://rna.eit.hirosaki-u.ac.jp/modena/.Keywords: multi-objective genetic algorithm, secondary structure, RNA sequence design, Rfam
 Quantitative Biology , 2011, Abstract: We extend an hypergraph representation, introduced by Finkelstein and Roytberg, to unify dynamic programming algorithms in the context of RNA folding with pseudoknots. Classic applications of RNA dynamic programming energy minimization, partition function, base-pair probabilities...) are reformulated within this framework, giving rise to very simple algorithms. This reformulation allows one to conceptually detach the conformation space/energy model -- captured by the hypergraph model -- from the specific application, assuming unambiguity of the decomposition. To ensure the latter property, we propose a new combinatorial methodology based on generating functions. We extend the set of generic applications by proposing an exact algorithm for extracting generalized moments in weighted distribution, generalizing a prior contribution by Miklos and al. Finally, we illustrate our full-fledged programme on three exemplary conformation spaces (secondary structures, Akutsu's simple type pseudoknots and kissing hairpins). This readily gives sets of algorithms that are either novel or have complexity comparable to classic implementations for minimization and Boltzmann ensemble applications of dynamic programming.
 Hengwu Li Journal of Computers , 2010, DOI: 10.4304/jcp.5.7.1100-1104 Abstract: Existing polynomial time algorithms can handle only limited types of pseudoknots, or have too high time or space to predict long sequences. In this paper a heuristic algorithm is presented to maximize stems and predict arbitrary pseudoknots with O(n^3) time and O(n) space for a large scale of 5000 bases. Compared with maximum weighted matching algorithm, our algorithm reduce space complexity from O(n^2) to O(n); and the experimental results show that its sensitivity is improved from 80% to 87.8%, and specificity is increased from 53.7% to 75.9%. Compared with genetic algorithm with the accuracy of 83.3% and simulated annealing algorithm with the accuracy of 79.7%, our algorithm increases the predicted accuracy to 87.5%.
 PLOS ONE , 2008, DOI: 10.1371/journal.pone.0002799 Abstract: Transfer RNA (tRNA) molecules play vital roles during protein synthesis. Their acceptor arms are aminoacylated with specific amino acid residues while their anticodons delimit codon specificity. The history of these two functions has been generally linked in evolutionary studies of the genetic code. However, these functions could have been differentially recruited as evolutionary signatures were left embedded in tRNA molecules. Here we built phylogenies derived from the sequence and structure of tRNA, we forced taxa into monophyletic groups using constraint analyses, tested competing evolutionary hypotheses, and generated timelines of amino acid charging and codon discovery. Charging of Sec, Tyr, Ser and Leu appeared ancient, while specificities related to Asn, Met, and Arg were derived. The timelines also uncovered an early role of the second and then first codon bases, identified codons for Ala and Pro as the most ancient, and revealed important evolutionary take-overs related to the loss of the long variable arm in tRNA. The lack of correlation between ancestries of amino acid charging and encoding indicated that the separate discoveries of these functions reflected independent histories of recruitment. These histories were probably curbed by co-options and important take-overs during early diversification of the living world.
 Physics , 1998, Abstract: We describe a dynamic programming algorithm for predicting optimal RNA secondary structure, including pseudoknots. The algorithm has a worst case complexity of ${\cal O}(N^6)$ in time and ${\cal O}(N^4)$ in storage. The description of the algorithm is complex, which led us to adopt a useful graphical representation (Feynman diagrams) borrowed from quantum field theory. We present an implementation of the algorithm that generates the optimal minimum energy structure for a single RNA sequence, using standard RNA folding thermodynamic parameters augmented by a few parameters describing the thermodynamic stability of pseudoknots. We demonstrate the properties of the algorithm by using it to predict structures for several small pseudoknotted and non-pseudoknotted RNAs. Although the time and memory demands of the algorithm are steep, we believe this is the first algorithm to be able to fold optimal (minimum energy) pseudoknotted RNAs with the accepted RNA thermodynamic model.
 Quantitative Biology , 2005, DOI: 10.1103/PhysRevE.73.031902 Abstract: We consider the folding of a self-avoiding homopolymer on a lattice, with saturating hydrogen bond interactions. Our goal is to numerically evaluate the statistical distribution of the topological genus of pseudoknotted configurations. The genus has been recently proposed for classifying pseudoknots (and their topological complexity) in the context of RNA folding. We compare our results on the distribution of the genus of pseudoknots, with the theoretical predictions of an existing combinatorial model for an infinitely flexible and stretchable homopolymer. We thus obtain that steric and geometric constraints considerably limit the topological complexity of pseudoknotted configurations, as it occurs for instance in real RNA molecules. We also analyze the scaling properties at large homopolymer length, and the genus distributions above and below the critical temperature between the swollen phase and the compact-globule phase, both in two and three dimensions.
 Akito Taneda Advances and Applications in Bioinformatics and Chemistry , 2011, DOI: http://dx.doi.org/10.2147/AABC.S14335 Abstract: : a multi-objective RNA inverse folding Original Research (3175) Total Article Views Authors: Akito Taneda Published Date December 2010 Volume 2011:4 Pages 1 - 12 DOI: http://dx.doi.org/10.2147/AABC.S14335 Akito Taneda Graduate School of Science and Technology, Hirosaki University, Hirosaki, Japan Abstract: Artificially synthesized RNA molecules have recently come under study since such molecules have a potential for creating a variety of novel functional molecules. When designing artificial RNA sequences, secondary structure should be taken into account since functions of noncoding RNAs strongly depend on their structure. RNA inverse folding is a methodology for computationally exploring the RNA sequences folding into a user-given target structure. In the present study, we developed a multi-objective genetic algorithm, MODENA (Multi-Objective DEsign of Nucleic Acids), for RNA inverse folding. MODENA explores the approximate set of weak Pareto optimal solutions in the objective function space of 2 objective functions, a structure stability score and structure similarity score. MODENA can simultaneously design multiple different RNA sequences at 1 run, whose lowest free energies range from a very stable value to a higher value near those of natural counterparts. MODENA and previous RNA inverse folding programs were benchmarked with 29 target structures taken from the Rfam database, and we found that MODENA can successfully design 23 RNA sequences folding into the target structures; this result is better than those of the other benchmarked RNA inverse folding programs. The multi-objective genetic algorithm gives a useful framework for a functional biomolecular design. Executable files of MODENA can be obtained at http://rna.eit.hirosaki-u.ac.jp/modena/.
 PLOS ONE , 2012, DOI: 10.1371/journal.pone.0052414 Abstract: The diverse landscape of RNA conformational space includes many canyons and crevices that are distant from the lowest minimum free energy valley and remain unexplored by traditional RNA structure prediction methods. A complete description of the entire RNA folding landscape can facilitate identification of biologically important conformations. The Crumple algorithm rapidly enumerates all possible non-pseudoknotted structures for an RNA sequence without consideration of thermodynamics while filtering the output with experimental data. The Crumple algorithm provides an alternative approach to traditional free energy minimization programs for RNA secondary structure prediction. A complete computation of all non-pseudoknotted secondary structures can reveal structures that would not be predicted by methods that sample the RNA folding landscape based on thermodynamic predictions. The free energy minimization approach is often successful but is limited by not considering RNA tertiary and protein interactions and the possibility that kinetics rather than thermodynamics determines the functional RNA fold. Efficient parallel computing and filters based on experimental data make practical the complete enumeration of all non-pseudoknotted structures. Efficient parallel computing for Crumple is implemented in a ring graph approach. Filters for experimental data include constraints from chemical probing of solvent accessibility, enzymatic cleavage of paired or unpaired nucleotides, phylogenetic covariation, and the minimum number and lengths of helices determined from crystallography or cryo-electron microscopy. The minimum number and length of helices has a significant effect on reducing conformational space. Pairing constraints reduce conformational space more than single nucleotide constraints. Examples with Alfalfa Mosaic Virus RNA and Trypanosome brucei guide RNA demonstrate the importance of evaluating all possible structures when pseduoknots, RNA-protein interactions, and metastable structures are important for biological function. Crumple software is freely available at http://adenosine.chem.ou.edu/software.ht？ml.
 Quantitative Biology , 2010, Abstract: We present TT2NE, a new algorithm to predict RNA secondary structures with pseudoknots. The method is based on a classification of RNA structures according to their topological genus. TT2NE guarantees to find the minimum free energy structure irrespectively of pseudoknot topology. This unique proficiency is obtained at the expense of the maximum length of sequence that can be treated but comparison with state-of-the-art algorithms shows that TT2NE is a very powerful tool within its limits. Analysis of TT2NE's wrong predictions sheds light on the need to study how sterical constraints limit the range of pseudoknotted structures that can be formed from a given sequence. An implementation of TT2NE on a public server can be found at http://ipht.cea.fr/rna/tt2ne.php.
 Open Access Library Journal (OALib Journal) , 2016, DOI: 10.4236/oalib.1102293 Abstract: Motif discovery is one of the fundamental problems that have important applications in identifying drug targets and regulatory sites. Regulatory sites on DNA sequence normally correspond to shared conservative sequence patterns among the regulatory regions of correlated genes. These conserved sequence patterns are called motifs. Identifying motifs and corresponding instances is very important, so biologists can investigate the interactions between DNA and proteins, gene regulation, cell development and cell reaction under physiological and pathological conditions. In this work, we developed a motif finding algorithm based on a multi-objective genetic algorithm technique and incorporated the hypergeometric scoring function to enable it discover gapped motifs from organisms with challenging genomic structure such as the malaria parasite. The runtime performance of our resulting algorithm, EMOGAMOD (Extended Multi Objective Genetic Algorithm MOtif Discovery) was evaluated with that of some common motif discovery algorithms and the result was remarkable.
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