Kay C. Wiese

Review of "The Ten Most Wanted Solutions in Protein Bioinformatics", by Anna Tramontano.

Bioinformatics can be defined as the application of computational tools to manage and analyze biological data, typically at the molecular level. The field of Computational Biology aims at the mathematical or computational modeling of biological processes, such as protein folding or protein-protein interaction. Many books that include the title bioinformatics or computational biology (BCB) actually present a mixture of the two fields. It is sometimes not possible to separate the two as for a single problem such as protein folding both bioinformatics techniques such as similarity search of existing protein databases as well as computational biology is used in the form of mathematically modeling the protein structure via thermodynamic models that are optimized via a computational technique.

A permutation-based genetic algorithm for the RNA folding problem: a critical look at selection strategies, crossover operators, and representation issues.

This paper presents a Genetic Algorithm (GA) to predict the secondary structure of RNA molecules, where the secondary structure is encoded as a permutation. More specifically, the proposed algorithm predicts which specific canonical base pairs will form hydrogen bonds and build helices, also known as stems. Since RNA is involved in both transcription and translation and also has catalytic and structural roles in the cell, determining the structure of RNA is of fundamental importance in helping to determine RNA function. We introduce a GA where a permutation is used to encode the secondary structure of RNA molecules. We discuss results on RNA sequences of lengths 76, 210, 681, and 785 nucleotides and present several improvements to our algorithm. We show that the Keep-Best Reproduction operator has similar benefits as in the traveling salesman problem domain. In addition, a comparison of several crossover operators is provided. We also compare the results of the permutation-based GA with a binary GA, demonstrating the benefits of the newly proposed representation.

jViz.Rna -a java tool for RNA secondary structure visualization

Many tools have been developed for visualization of RNA secondary structures using a variety of techniques and output formats. However, each tool is typically limited to one or two of the visualization models discussed in this paper, supports only a single file format, and is tied to a specific platform. In order for structure prediction researchers to better understand the results of their algorithms and to enable life science researchers to interpret RNA structure easily, it is helpful to provide them with a flexible and powerful tool. jViz.Rna is a multiplatform visualization tool capable of displaying RNA secondary structures encoded in a variety of file formats. The same structure can be viewed using any of the models supported, including linked graph, circle graph, dot plot, and classical structure. Also, the output is dynamic and can easily be further manipulated by the user. In addition, any of the drawings produced can be saved in either the EPS or PNG file formats enabling easy usage in publications and presentations.

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).

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.

SARNA-Predict: Accuracy Improvement of RNA Secondary Structure Prediction Using Permutation-Based Simulated Annealing

Ribonucleic acid (RNA), a single-stranded linear molecule, is essential to all biological systems. Different regions of the same RNA strand will fold together via base pair interactions to make intricate secondary and tertiary structures that guide crucial homeostatic processes in living organisms. Since the structure of RNA molecules is the key to their function, algorithms for the prediction of RNA structure are of great value. In this article, we demonstrate the usefulness of SARNA-Predict, an RNA secondary structure prediction algorithm based on Simulated Annealing (SA). A performance evaluation of SARNA-Predict in terms of prediction accuracy is made via comparison with eight state-of-the-art RNA prediction algorithms: mfold, Pseudoknot(pknotsRE), NUPACK, pknotsRG-mfe, Sfold, HotKnots, ILM, and STAR. These algorithms are from three different classes: heuristic, dynamic programming, and statistical sampling techniques. An evaluation for the performance of SARNA-Predict in terms of prediction accuracy was verified with native structures. Experiments on 33 individual known structures from eleven RNA classes (tRNA, viral RNA, antigenomic HDV, telomerase RNA, tmRNA, rRNA, RNaseP, 5S rRNA, Group I intron 23S rRNA, Group I intron 16S rRNA, and 16S rRNA) were performed. The results presented in this paper demonstrate that SARNA-Predict can out-perform other state-of-the-art algorithms in terms of prediction accuracy. Furthermore, there is substantial improvement of prediction accuracy by incorporating a more sophisticated thermodynamic model (efn2).

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.

Keep-Best Reproduction: A Local Family Competition Selection Strategy and the Environment it Flourishes in

This paper presents a comparison of two genetic algorithms (GAs) for constrained ordering problems. The first GA uses the standard selection strategy of roulette wheel selection and generational replacement (STDS), while the second GA uses an intermediate selection strategy in addition to STDS. This intermediate selection strategy keeps only the superior offspring and replaces the inferior offspring with the superior parent. We call this selection strategy Keep–Best Reproduction (KBR). The effect of recombination alone, mutation alone and both together are studied. We compare the performance of the different selection strategies and discuss the environment that each selection strategy needs to flourish in. Overall, KBR is found to be the selection strategy of choice. We also present empirical evidence that suggests that KBR is more robust than STDS with regard to operator probabilities and works well with smaller population sizes.

SARNA-Predict: A Study of RNA Secondary Structure Prediction Using Different Annealing Schedules

This paper presents an algorithm for RNA secondary structure prediction based on simulated annealing (SA) and also studies the effect of using different types of annealing schedules. SA is known to be effective in solving many different types of minimization problems and for being able to approximate global minima in the solution space. Based on free energy minimization techniques, this permutation-based SA algorithm heuristically searches for the structure with a free energy value close to the minimum free energy DeltaG for that strand, within given constraints. Other contributions of this paper include the use of permutation-based encoding for RNA secondary structure and the swap mutation operator. Also, a detailed study of the convergence behavior of the algorithm is conducted and various annealing schedules are investigated. An evaluation of the performance of the new algorithm in terms of prediction accuracy is made via comparison with the dynamic programming algorithm mfold for thirteen individual known structures from four RNA classes (5S rRNA, Group I intron 23 rRNA, Group I intron 16S rRNA and 16S rRNA). Although dynamic programming algorithms for RNA folding are guaranteed to give the mathematically optimal (minimum energy) structure, the fundamental problem of this approach seems to be that the thermodynamic model is only accurate within 5-10%. Therefore, it is difficult for a single sequence folding algorithm to resolve which of the plausible lowest-energy structure is correct. The new algorithm showed comparable results with mfold and demonstrated a slightly higher specificity