RNA secondary structure prediction using stochastic context-free grammars and evolutionary history.

Knudsen, Bjarne; Hein, Jotun

doi:10.1093/bioinformatics/15.6.446

Cited by 303 publications

(246 citation statements)

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“…A third phylogenetic approach is adopted by Pfold [76,77], which combines an evolutionary model of RNA sequences with a probabilistic model of secondary structures. These are used to calculate a phylogenetic tree and a consensus structure from a multiple alignment.…”

Section: Including Phylogenetic Informationmentioning

confidence: 99%

Computational methods in noncoding RNA research

2007

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Non protein-coding RNAs (ncRNAs) are a research hotspot in bioinformatics. Recent discoveries have revealed new ncRNA families performing a variety of roles, from gene expression regulation to catalytic activities. It is also believed that other families are still to be unveiled. Computational methods developed for protein coding genes often fail when searching for ncRNAs. Noncoding RNAs functionality is often heavily dependent on their secondary structure, which makes gene discovery very different from protein coding RNA genes. This motivated the development of specific methods for ncRNA research. This article reviews the main approaches used to identify ncRNAs and predict secondary structure.

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Section: Including Phylogenetic Informationmentioning

confidence: 99%

Computational methods in noncoding RNA research

2007

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“…In fact, when using SCFG based approaches, the main focus of attention is laid on the typical structural composition of foldings and free energies are disregarded. An example for a popular SCFG based prediction tool for RNA secondary structure is Pfold [KH99,KH03]. As there is no lab-based prior to the grammar parameters like the Turner model for MFE and PF approaches, the corresponding distribution has to be derived from a collection of real-life RNA data (RNA sequences with known secondary structures) when using probabilistic 1 approaches to RNA structure pre-diction.…”

Section: Introductionmentioning

confidence: 99%

“…Early probabilistic approaches such as [KH99] seem to have chosen the structure of their SCFG rather arbitrarily; at least, there is almost no discussion about the motivation for the choice of the productions. This problem has first been addressed in [DE04] where nine different SCFGs have been evaluated in connection with RNA secondary structure prediction.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of a sophisticated SCFG design for RNA secondary structure prediction

Nebel

Scheid

2011

Theory Biosci.

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Motivation: Predicting secondary structures of RNA molecules is one of the fundamental problems of and thus a challenging task in computational structural biology. Over the past decades, mainly two different approaches have been considered to compute predictions of RNA secondary structures from a single sequence: the first one relies on physics-based and the other on probabilistic RNA models. Particularly, the free energy minimization (MFE) approach is usually considered the most popular and successful method. Moreover, based on the paradigm-shifting work by McCaskill which proposes the computation of partition functions (PFs) and base pair probabilities based on thermodynamics, several extended partition function algorithms, statistical sampling methods and clustering techniques have been invented over the last years. However, the accuracy of the corresponding algorithms is limited by the quality of underlying physics-based models, which include a vast number of thermodynamic parameters and are still incomplete. The competing probabilistic approach is based on stochastic context-free grammars (SCFGs) or corresponding generalizations, like conditional log-linear models (CLLMs). These methods abstract from free energies and instead try to learn about the structural behavior of the molecules by learning (a manageable number of) probabilistic parameters from trusted RNA structure databases. In this work, we introduce and evaluate a sophisticated SCFG design that mirrors state-of-the-art physics-based RNA structure prediction procedures by distinguishing between all features of RNA that imply different energy rules. This SCFG actually serves as the foundation for a statistical sampling algorithm for RNA secondary structures of a single sequence that represents a probabilistic counterpart to the sampling extension of the PF approach. Furthermore, some new ways to derive meaningful structure predictions from generated sample sets are presented. They are used to compare the predictive accuracy of our model to that of other probabilistic and energy-based prediction methods. Results: Particularly, comparisons to lightweight SCFGs and corresponding CLLMs for RNA structure prediction indicate that more complex SCFG designs might yield higher accuracy but eventually require more comprehensive and pure training sets. Investigations on both the accuracies of predicted foldings and the overall quality of generated sample sets (especially on an abstraction level, called abstract shapes of generated structures, that is relevant for biologists) yield the conclusion that the Boltzmann distribution of the PF sampling approach is more centered than the ensemble distribution induced by the sophisticated SCFG model, which implies a greater structural diversity within generated samples. In general, neither of the two distinct ensemble distributions is more adequate than the other and the corresponding results obtained by statistical sampling can be expected to bare fundamental differences, such that the method to be preferred f...

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“…Then, in order to predict a secondary structure, one computes the most likely conformation for the given sequence of bases. The algorithm of Knudsen and Hein [30] is one prominent member of this class using stochastic context-free languages (SCFGs). The grammar that they use is much less complex than free-energy based models, but their method works very well, see [21,16].…”

Section: Introductionmentioning

confidence: 99%

Predicting RNA secondary structures with pseudoknots by MCMC sampling

Metzler

Nebel

2007

J. Math. Biol.

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The most probable secondary structure of an RNA molecule, given the nucleotide sequence, can be computed efficiently if a stochastic context-free grammar (SCFG) is used as the prior distribution of the secondary structure. The structures of some RNA molecules contain so-called pseudoknots. Allowing all possible configurations of pseudoknots is not compatible with context-free grammar models and makes the search for an optimal secondary structure NP-complete.We suggest a probabilistic model for RNA secondary structures with pseudoknots and present a Markov-chain Monte-Carlo Method for sampling RNA structures according to their posterior distribution for a given sequence. We favor Bayesian sampling over optimization methods in this context, because it makes the uncertainty of RNA structure predictions assessable. We demonstrate the benefit of our method in examples with tmRNA and also with simulated data.McQFold, an implementation of our method, is freely available from

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RNA secondary structure prediction using stochastic context-free grammars and evolutionary history.

Cited by 303 publications

References 0 publications

Computational methods in noncoding RNA research

Computational methods in noncoding RNA research

Evaluation of a sophisticated SCFG design for RNA secondary structure prediction

Predicting RNA secondary structures with pseudoknots by MCMC sampling

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