Andrew Hendriks

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.

Comparison of P-RnaPredict and mfold---algorithms for RNA secondary structure prediction

MOTIVATION Ribonucleic acid is vital in numerous stages of protein synthesis; it also possesses important functional and structural roles within the cell. The function of an RNA molecule within a particular organic system is principally determined by its structure. The current physical methods available for structure determination are time-consuming and expensive. Hence, computational methods for structure prediction are sought after. The energies involved by the formation of secondary structure elements are significantly greater than those of tertiary elements. Therefore, RNA structure prediction focuses on secondary structure. RESULTS We present P-RnaPredict, a parallel evolutionary algorithm for RNA secondary structure prediction. The speedup provided by parallelization is investigated with five sequences, and a dramatic improvement in speedup is demonstrated, especially with longer sequences. An evaluation of the performance of P-RnaPredict in terms of prediction accuracy is made through comparison with 10 individual known structures from 3 RNA classes (5S rRNA, Group I intron 16S rRNA and 16S rRNA) and the mfold dynamic programming algorithm. P-RnaPredict is able to predict structures with higher true positive base pair counts and lower false positives than mfold on certain sequences. AVAILABILITY P-RnaPredict is available for non-commercial usage. Interested parties should contact Kay C. Wiese (wiese@cs.sfu.ca).

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

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

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 molecules 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.

A Detailed Analysis of Parallel Speedup in P-RnaPredict - An Evolutionary Algorithm for RNA Secondary Structure Prediction

The function of an RNA molecule is primarily established by its physical shape. As current physical structure determination methods are time consuming and expensive, there is great interest in finding computational structure prediction methods. P-RnaPredict is a parallel evolutionary algorithm for RNA secondary structure prediction. Two sets of experiments are performed on 5 known structures from 3 RNA classes (5S rRNA, Group I intron 16S rRNA, and 16S rRNA). The first determines the actual speedup, and the second evaluates the performance of P-RnaPredict through comparison to mfold. P-RnaPredict succeeds in predicting structures with higher true positive base pair counts and lower false positives than mfold on specific sequences.

Algorithms for RNA folding: a comparison of dynamic programming and parallel evolutionary algorithms

This paper presents a comparison of two types of algorithms for RNA secondary structure prediction: an implementation of Nussinovs dynamic programming algorithm (DPA), and P-RnaPredict, a parallel evolutionary algorithm (EA). The research presented here builds on previous work and examines the results from tests of three RNA sequences that are 118, 543, and 784 nucleotides in length. A variety of EA parameter settings were employed based on previous experimentation. Predicted structures were compared to those generated by the Nussinov DPA and to known structures to determine relative accuracy. Results indicate that the EA demonstrated high prediction accuracy and outperformed the Nussinov DPA on all tested sequences

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

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 RnaPredicts comparable performance.