Comparison of dynamic programming and evolutionary algorithms for RNA secondary structure prediction

2009 IEEE Congress on Evolutionary Computation

2009

RNA has important structural, functional, and regulatory parts in the cell as well as a critical role in multiple stages of protein synthesis. An RNA molecule's shape largely determines its function in an organic system. Accordingly, computational RNA structural prediction methods are of significant interest. For ab initio cases where only an RNA sequence is known, structure prediction techniques typically employ free energy minimization of a given RNA molecule via a thermodynamic model. Unfortunately, the minimum free energy structure is rarely the native structure. This is thought to be due to errors in the experimentally determined thermodynamic model parameters.RnaPredict is an evolutionary algorithm designed for the prediction of RNA secondary structure; it currently utilizes the stacking-energy thermodynamic models INN and INN-HB. The effect of an enhanced model, efn2, on RnaPredict is investigated. The efn2 model significantly improved the sensitivity and specificity of the majority of structures evaluated.

Section: Introductionmentioning

confidence: 99%

Impact of an enhanced thermodynamic model on RnaPredict, an evolutionary algorithm for RNA secondary structure prediction

2009 IEEE Congress on Evolutionary Computation

2009

“…Algorithm performance was compared to the Nussinov DP algorithm [16] in [17]. Recent research includes the creation of a hybrid algorithm through the combination of RnaPredict with a clustering algorithm [18].…”

mentioning

confidence: 99%

Expanded study of efn2 thermodynamic model performance on RnaPredict, an evolutionary algorithm for RNA folding

2010 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology

2010

The shape that organic molecules such as biopolymers form within organic systems largely determines the function said molecules perform. RNA is a biopolymer that plays a central part in several stages of protein synthesis, and also has structural, functional, and regulatory roles in the cell. In an ab initio case most common structure prediction techniques employ minimization of the free energy of a given RNA molecule via a thermodynamic model. RnaPredict is an evolutionary algorithm for RNA folding. This paper compares the performance of an advanced thermodynamic model, efn2, against the stacking-energy thermodynamic models INN and INN-HB on a test set containing 24 sequences from 4 rRNA subtypes. The prediction accuracy of efn2 is demonstrated on a majority of test sequences. A comparison is also made with the mfold prediction algorithm which demonstrated RnaPredict's comparable performance.

“…Later, Deschênes and Wiese [5] have shown that RnaPredict, using two stacking based thermodynamic models, can predict certain RNA secondary structures with very high accuracy and also outperform a dynamic programming algorithm [6]. Our specific work builds on [11,10], in which a serial simulation of a distributed version of RnaPredict was implemented that predicted the secondary structure of RNA molecules from input RNA sequences.…”

Section: Introductionmentioning

confidence: 99%

The impact of pseudorandom number quality on P-RnaPredict , a parallel genetic algorithm for RNA secondary structure prediction

Proceedings of the 7th Annual Conference on Genetic and Evolutionary Computation

Deschenes

et al. 2005

This paper presents a parallel version of RnaPredict, a genetic algorithm (GA) for RNA secondary structure prediction. The research presented here builds on previous work and examines the impact of three different pseudorandom number generators (PRNGs) on the GA's performance. The three generators tested are the C standard library PRNG RAND, a parallelized multiplicative congruential generator (MCG), and a parallelized Mersenne Twister (MT). A fully parallel version of RnaPredict using the Message Passing Interface (MPI) was implemented. The PRNG comparison tests were performed with known structures that are 118, 122, 543, and 556 nucleotides in length. The effects of the PRNGs are investigated and the predicted structures are compared to known structures.