Alain Deschênes

RnaPredict—An Evolutionary Algorithm for RNA Secondary Structure Prediction

This paper presents two in-depth studies on RnaPredict, an evolutionary algorithm for RNA secondary structure prediction. The first study is an analysis of the performance of two thermodynamic models, INN and INN-HB. The correlation between the free energy of predicted structures and the sensitivity is analyzed for 19 RNA sequences. Although some variance is shown, there is a clear trend between a lower free energy and an increase in true positive base pairs. With increasing sequence length, this correlation generally decreases. In the second experiment, the accuracy of the predicted structures for these 19 sequences are compared against the accuracy of the structures generated by the mfold dynamic programming algorithm (DPA) and also to known structures. RnaPredict is shown to outperform the minimum free energy structures produced by mfold and has comparable performance when compared to sub-optimal structures produced by mfold.

Using stacking-energies (INN and INN-HB) for improving the accuracy of RNA secondary structure prediction with an evolutionary algorithm - a comparison to known structures

This paper builds on previous research from an EA used to predict secondary structure of RNA molecules. The EA predicts which specific canonical base pairs forms hydrogen bonds and helices. Three new thermodynamic models were integrated into our EA. The first based on a modification to our original base pair model. The last two, INN and INN-HB, add stacking-energies using base pair adjacencies. We have tested RNA sequences of lengths 122, 543, and 1494 nucleotides on a wide variety of operators and parameters settings. The accuracy of the predicted structures is compared to the known structures thus demonstrating the benefits of using stacking-energies in structure prediction. Some other improvements to our EA are also discussed.

Permutation-based RNA secondary structure prediction via a genetic algorithm

This paper presents new results with a permutation-based genetic algorithm (GA) to predict the secondary structure of RNA molecules. More specifically, the proposed algorithm predicts which canonical base pairs forms hydrogen bonds and builds helices, also known as stems. We discuss a GA where a permutation is used to encode the secondary structure of RNA molecules. We have tested RNA sequences of lengths 76, 210, 681, and 785 nucleotides over a wide variety of operators and parameter settings and focus on discussing in depth the results with two crossover operators asymmetric edge recombinations (ASERC) and symmetric edge recombination (SYMERC) that have not been analyzed in this domain previously. We demonstrate that the keep-best reproduction (KBR) operator has similar benefits as in the travelling salesman problem (TSP) domain. We also compare the results of the permutation-based GA with a binary GA, demonstrating the benefits of the newly proposed representation.

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

This work builds on previous research from an EA used to predict secondary structure of RNA molecules. The EA has the goal of predicting which canonical base pairs will form hydrogen bonds and helices. The addition of stacking energies, through INN and INN-HB, to our thermodynamic model has enhanced our predictions. We test three RNA sequences of lengths 118, 543, and 784 nucleotides using a variety of previously successful operators and parameter settings. The accuracy of the predicted structures are compared against those generated by the Nussinov DPA and also to known structures. The EA showed high accuracy of prediction especially on short sequences. On all tested sequences, the EA outperforms the Nussinov DPA.

A distributed genetic algorithm for RNA secondary structure prediction

This paper presents a new coarse-grained distributed genetic algorithm (GA) for the prediction of the secondary structure of RNA molecules, based largely on a serial permutation-based GA. The benefits of the distributed GA over our existing serial GA are analyzed and demonstrated. We also analyze the impact of the keep-best reproduction (KBR) and roulette wheel selection (STDS) GA replacement techniques. Finally, we verify the increase in convergence speed of our distributed GA. Tests was performed on 241 and 785 nucleotide sequences. Overall, the distributed GA is found to improve upon the serial GA performances, with a much more pronounced impact on the STDS selection strategy. There is also a notable acceleration in convergence speed.

A parallel evolutionary algorithm for RNA secondary structure prediction using stacking-energies (INN and INN-HB)

This work presents a coarse-grained distributed genetic algorithm (GA) for RNA secondary structure prediction. This research builds on previous work and contains two new thermodynamic models, INN and INN-HB, which add stacking-energies using base pair adjacencies. Comparison tests were performed against the original serial GA on known structures that are 122, 543, and 784 nucleotides in length on a wide variety of parameter settings. The effects of the new models are investigated, the predicted structures are compared to known structures and the GA is compared against a serial GA with identical models. Both algorithms perform well and are able to predict structures with high accuracy for short sequences.

P-RnaPredict-a parallel evolutionary algorithm for RNA folding: effects of pseudorandom number quality

This paper presents a fully 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 GAs 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 on a 128-node Beowulf cluster. The PRNG comparison tests were performed with known structures whose sequences are 118, 122, 468, 543, and 556 nucleotides in length. The effects of the PRNGs are investigated and the predicted structures are compared to known structures. Results indicate that P-RnaPredict demonstrated good prediction accuracy, particularly so for shorter sequences.

Analysis of Thermodynamic Models and Performance in RnaPredict - An Evolutionary Algorithm for RNA Folding

Two extensive analyzes on RnaPredict, an evolutionary algorithm for RNA folding, are presented here. The first study evaluates the performance of individual nearest neighbor (INN) and individual nearest neighbor-hydrogen bond (INN-HB), two stacking-energy thermodynamic models; the criteria for comparison is the correlation between the prediction accuracy and the free energy of predicted structures for 9 RNA sequences. Despite some variance, a trend between lower free energies and increases in true positive base pairs is apparent. In general, this correlation decreases as the sequence length increases. The second study compares the performance of RnaPredict against the mfold dynamic programming algorithm (DPA) on the same sequences in terms of specificity and sensitivity. The results indicate that RnaPredict has comparable performance to mfold on sub-optimal structures, and outperforms mfolds minimum free energy structures

Comparison of Permutation-Based and Binary Representation in a Genetic Algorithm for RNA Secondary Structure Prediction

RNA is an important molecule as it serves a key role in the translation from the genetic information encoded in DNA in protein synthesis. Computational techniques for RNA folding suffer from combinatorial explosion. In this paper, a genetic algorithm (GA) will be used to attempt to solve the secondary structure prediction of RNA molecules.

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

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 GAs 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.