CONTRAfold: RNA secondary structure prediction without physics-based models

Chuong, B.; Woods, Daniel A.; Batzoglou, Serafim

doi:10.1093/bioinformatics/btl246

Cited by 517 publications

(583 citation statements)

References 23 publications

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“…CONTRAfold [34] is a program that uses a Conditional Log Linear Model (CLLM), a generalization of Stochastic Context Free Grammars (SCFGs). This model is marked by three main innovations: discriminative training, flexible parameterization and an accuracy-adjustable optimization.…”

Section: A Probabilistic Modelmentioning

confidence: 99%

“…Therefore, most of the methods that perform a folding space analysis, such as density of states, are based on this approach. Recently, a novel method using probabilistic models, CONTRAfold [34], outperformed the best 28 The target RNAs are assumed to be rRNA or snRNAs. 29 In some miRNAs both sequences can be used into different mRNA targets.…”

Section: Perspectives and Conclusionmentioning

confidence: 99%

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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: A Probabilistic Modelmentioning

confidence: 99%

Section: Perspectives and Conclusionmentioning

confidence: 99%

Computational methods in noncoding RNA research

2007

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“…In particular we have fairly accurate energy values for computing loop-based mfe (Mathews et al, 1999Turner and Mathews, 2010) that are employed by the folding algorithms (Zuker and Stiegler, 1981;Hofacker et al, 1994). More work has been done on loop-energy models in (Mathews, 2004;Do et al, 2006). We plan on a more detailed analysis of the framework proposed here in the context of the MC-model (Parisien and Major, 2008).…”

Section: Introductionmentioning

confidence: 99%

Sequence–structure relations of biopolymers

2016

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Motivation DNA data is transcribed into single-stranded RNA, which folds into specific molecular structures. In this paper we pose the question to what extent sequence- and structure-information correlate. We view this correlation as structural semantics of sequence data that allows for a different interpretation than conventional sequence alignment. Structural semantics could enable us to identify more general embedded ‘patterns’ in DNA and RNA sequences. Results We compute the partition function of sequences with respect to a fixed structure and connect this computation to the mutual information of a sequence–structure pair for RNA secondary structures. We present a Boltzmann sampler and obtain the a priori probability of specific sequence patterns. We present a detailed analysis for the three PDB-structures, 2JXV (hairpin), 2N3R (3-branch multi-loop) and 1EHZ (tRNA). We localize specific sequence patterns, contrast the energy spectrum of the Boltzmann sampled sequences versus those sequences that refold into the same structure and derive a criterion to identify native structures. We illustrate that there are multiple sequences in the partition function of a fixed structure, each having nearly the same mutual information, that are nevertheless poorly aligned. This indicates the possibility of the existence of relevant patterns embedded in the sequences that are not discoverable using alignments. Availability and Implementation The source code is freely available at http://staff.vbi.vt.edu/fenixh/Sampler.zip Supplementary information Supplementary data are available at Bioinformatics online.

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“…Recently, massively feature-rich models empowered by parameter estimation algorithms have been proposed. Despite significant progress in the last three decades, made possible by the work of Turner and others [20] on measuring RNA thermodynamic energy parameters and the work of several groups on novel algorithms [21,22,23,24,25,26,27,28] and machine learning approaches [29,30,31], the RNA structure prediction accuracy has not reached a satisfactory level yet [32].…”

Section: Introductionmentioning

confidence: 99%

Exact Learning of RNA Energy Parameters from Structure

Chitsaz

Aminisharifabad

2014

Lecture Notes in Computer Science

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We consider the problem of exact learning of parameters of a linear RNA energy model from secondary structure data. A necessary and sufficient condition for learnability of parameters is derived, which is based on computing the convex hull of union of translated Newton polytopes of input sequences [1]. The set of learned energy parameters is characterized as the convex cone generated by the normal vectors to those facets of the resulting polytope that are incident to the origin. In practice, the sufficient condition may not be satisfied by the entire training data set; hence, computing a maximal subset of training data for which the sufficient condition is satisfied is often desired. We show that problem is NP-hard in general for an arbitrary dimensional feature space. Using a randomized greedy algorithm, we select a subset of RNA STRAND v2.0 database that satisfies the sufficient condition for separate A-U, C-G, G-U base pair counting model. The set of learned energy parameters includes experimentally measured energies of A-U, C-G, and G-U pairs; hence, our parameter set is in agreement with the Turner parameters.

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CONTRAfold: RNA secondary structure prediction without physics-based models

Abstract: Source code for CONTRAfold is available at http://contra.stanford.edu/contrafold/.

Cited by 517 publications

References 23 publications

Computational methods in noncoding RNA research

Computational methods in noncoding RNA research

Sequence–structure relations of biopolymers

Exact Learning of RNA Energy Parameters from Structure

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