Christine E. Heitsch

Evaluating the accuracy of SHAPE-directed RNA secondary structure predictions

Recent advances in RNA structure determination include using data from high-throughput probing experiments to improve thermodynamic prediction accuracy. We evaluate the extent and nature of improvements in data-directed predictions for a diverse set of 16S/18S ribosomal sequences using a stochastic model of experimental SHAPE data. The average accuracy for 1000 data-directed predictions always improves over the original minimum free energy (MFE) structure. However, the amount of improvement varies with the sequence, exhibiting a correlation with MFE accuracy. Further analysis of this correlation shows that accurate MFE base pairs are typically preserved in a data-directed prediction, whereas inaccurate ones are not. Thus, the positive predictive value of common base pairs is consistently higher than the directed prediction accuracy. Finally, we confirm sequence dependencies in the directability of thermodynamic predictions and investigate the potential for greater accuracy improvements in the worst performing test sequence.

From RNA Secondary Structure to Coding Theory: A Combinatorial Approach

We use combinatorial analysis to transform a special case of the computational problem of designing RNA base sequences with a given minimal free energy secondary structure into a coding theory question. The function of RNA molecules is largely determined by their molecular form, which in turn is significantly related to the base pairings of the secondary structure. Hence, this is crucial initial work in the design of RNA molecules with desired three-dimensional structures and specific functional properties. The biological importance of RNA only continues to grow with the discoveries of many different RNA molecules having vital functions other than mediating the production of proteins from DNA. Furthermore, RNA has the same potential as DNA in terms of nanotechnology and biomolecular computing.

GTfold : a scalable multicore code for RNA secondary structure prediction

The prediction of the correct secondary structures of large RNAs is one of the unsolved challenges of computational molecular biology. Among the major obstacles is the fact that accurate calculations scale as O(n4), so the computational requirements become prohibitive as the length increases. Existing folding programs implement heuristics and approximations to overcome these limitations. We present a new parallel multicore and scalable program called GTfold, which is one to two orders of magnitude faster than the de facto standard programs and achieves comparable accuracy of prediction. Development of GTfold opens up a new path for the algorithmic improvements and application of an improved thermodynamic model to increase the prediction accuracy. In this paper we analyze the algorithms concurrency and describe the parallelism for a shared memory environment such as a symmetric multiprocessor or multicore chip. In a remarkable demonstration, GTfold now optimally folds 11 picornaviral RNA sequences ranging from 7100 to 8200 nucleotides in 8 minutes, compared with the two months it took in a previous study. We are seeing a paradigm shift to multicore chips and parallelism must be explicitly addressed to continue gaining performance with each new generation of systems. We also show that the exact algorithms like internal loop speedup can be implemented with our method in an affordable amount of time. GTfold is freely available as open source from our website.

Finding 3D motifs in ribosomal RNA structures

The identification of small structural motifs and their organization into larger subassemblies is of fundamental interest in the analysis, prediction and design of 3D structures of large RNAs. This problem has been studied only sparsely, as most of the existing work is limited to the characterization and discovery of motifs in RNA secondary structures. We present a novel geometric method for the characterization and identification of structural motifs in 3D rRNA molecules. This method enables the efficient recognition of known 3D motifs, such as tetraloops, E-loops, kink-turns and others. Furthermore, it provides a new way of characterizing complex 3D motifs, notably junctions, that have been defined and identified in the secondary structure but have not been analyzed and classified in three dimensions. We demonstrate the relevance and utility of our approach by applying it to the Haloarcula marismortui large ribosomal unit. Pending the implementation of a dedicated web server, the code accompanying this article, written in JAVA, is available upon request from the contact author.

GTfold: Enabling parallel RNA secondary structure prediction on multi-core desktops

BackgroundAccurate and efficient RNA secondary structure prediction remains an important open problem in computational molecular biology. Historically, advances in computing technology have enabled faster and more accurate RNA secondary structure predictions. Previous parallelized prediction programs achieved significant improvements in runtime, but their implementations were not portable from niche high-performance computers or easily accessible to most RNA researchers. With the increasing prevalence of multi-core desktop machines, a new parallel prediction program is needed to take full advantage of today’s computing technology.FindingsWe present here the first implementation of RNA secondary structure prediction by thermodynamic optimization for modern multi-core computers. We show that GTfold predicts secondary structure in less time than UNAfold and RNAfold, without sacrificing accuracy, on machines with four or more cores.ConclusionsGTfold supports advances in RNA structural biology by reducing the timescales for secondary structure prediction. The difference will be particularly valuable to researchers working with lengthy RNA sequences, such as RNA viral genomes.

Large Deviations for Random Trees and the Branching of RNA Secondary Structures

We give a Large Deviation Principle (LDP) with explicit rate function for the distribution of vertex degrees in plane trees, a combinatorial model of RNA secondary structures. We calculate the typical degree distributions based on nearest neighbor free energies, and compare our results with the branching configurations found in two sets of large RNA secondary structures. We find substantial agreement overall, with some interesting deviations which merit further study.

The icosahedral RNA virus as a grotto: organizing the genome into stalagmites and stalactites.

There are two important problems in the assembly of small, icosahedral RNA viruses. First, how does the capsid protein select the viral RNA for packaging, when there are so many other candidate RNA molecules available? Second, what is the mechanism of assembly? With regard to the first question, there are a number of cases where a particular RNA sequence or structure—often one or more stem-loops—either promotes assembly or is required for assembly, but there are others where specific packaging signals are apparently not required. With regard to the assembly pathway, in those cases where stem-loops are involved, the first step is generally believed to be binding of the capsid proteins to these “fingers” of the RNA secondary structure. In the mature virus, the core of the RNA would then occupy the center of the viral particle, and the stem-loops would reach outward, towards the capsid, like stalagmites reaching up from the floor of a grotto towards the ceiling. Those viruses whose assembly does not depend on protein binding to stem-loops could have a different structure, with the core of the RNA lying just under the capsid, and the fingers reaching down into the interior of the virus, like stalactites. We review the literature on these alternative structures, focusing on RNA selectivity and the assembly mechanism, and we propose experiments aimed at determining, in a given virus, which of the two structures actually occurs.

Asymptotic distribution of motifs in a stochastic context-free grammar model of RNA folding

We analyze the distribution of RNA secondary structures given by the Knudsen–Hein stochastic context-free grammar used in the prediction program Pfold. Our main theorem gives relations between the expected number of these motifs—independent of the grammar probabilities. These relations are a consequence of proving that the distribution of base pairs, of helices, and of different types of loops is asymptotically Gaussian in this model of RNA folding. Proof techniques use singularity analysis of probability generating functions. We also demonstrate that these asymptotic results capture well the expected number of RNA base pairs in native ribosomal structures, and certain other aspects of their predicted secondary structures. In particular, we find that the predicted structures largely satisfy the expected relations, although the native structures do not.

Profiling small RNA reveals multimodal substructural signals in a Boltzmann ensemble

As the biomedical impact of small RNAs grows, so does the need to understand competing structural alternatives for regions of functional interest. Suboptimal structure analysis provides significantly more RNA base pairing information than a single minimum free energy prediction. Yet computational enhancements like Boltzmann sampling have not been fully adopted by experimentalists since identifying meaningful patterns in this data can be challenging. Profiling is a novel approach to mining RNA suboptimal structure data which makes the power of ensemble-based analysis accessible in a stable and reliable way. Balancing abstraction and specificity, profiling identifies significant combinations of base pairs which dominate low-energy RNA secondary structures. By design, critical similarities and differences are highlighted, yielding crucial information for molecular biologists. The code is freely available via http://gtfold.sourceforge.net/profiling.html.

New insights from cluster analysis methods for RNA secondary structure prediction.

A widening gap exists between the best practices for RNA secondary structure prediction developed by computational researchers and the methods used in practice by experimentalists. Minimum free energy predictions, although broadly used, are outperformed by methods which sample from the Boltzmann distribution and data mine the results. In particular, moving beyond the single structure prediction paradigm yields substantial gains in accuracy. Furthermore, the largest improvements in accuracy and precision come from viewing secondary structures not at the base pair level but at lower granularity/higher abstraction. This suggests that random errors affecting precision and systematic ones affecting accuracy are both reduced by this ‘fuzzier’ view of secondary structures. Thus experimentalists who are willing to adopt a more rigorous, multilayered approach to secondary structure prediction by iterating through these levels of granularity will be much better able to capture fundamental aspects of RNA base pairing. WIREs RNA 2016, 7:278–294. doi: 10.1002/wrna.1334