Oxfold: kinetic folding of RNA using stochastic context-free grammars and evolutionary information

Anderson, James W.; Haas, Pierre A.; Mathieson, Leigh-Anne; Volynkin, Vladimir; Lyngsø, Rune B.; Tataru, Paula; Hein, Jotun

doi:10.1093/bioinformatics/btt050

Cited by 13 publications

(12 citation statements)

References 26 publications

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“…To better understand how macromolecules fold into their native state, energy landscapes for protein and RNA folding have been intensively studied [ 3 – 8 ]. In the case of RNA secondary structure formation, numerous algorithms have been developed beyond thermodynamic equilibrium structure prediction [ 9 , 10 ], including algorithms (1) to determine optimal or near-optimal folding pathways, [ 6 , 7 , 11 – 13 ], (2) to compute explicit solutions of the master equation for possibly coarse-grained models [ 14 – 18 ], and (3) to simulate stepwise folding from an initial secondary structure to the target minimum free energy (MFE) structure [ 5 , 19 – 24 ]. Nevertheless, RNA secondary structure folding kinetics remains a computationally difficult problem, since it is known that the problem of determining optimal folding pathways is NP-complete [ 25 ].…”

Section: Introductionmentioning

confidence: 99%

Network Properties of the Ensemble of RNA Structures

Clote

Bayegan

2015

PLoS ONE

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We describe the first dynamic programming algorithm that computes the expected degree for the network, or graph G = (V, E) of all secondary structures of a given RNA sequence a = a 1, …, a n. Here, the nodes V correspond to all secondary structures of a, while an edge exists between nodes s, t if the secondary structure t can be obtained from s by adding, removing or shifting a base pair. Since secondary structure kinetics programs implement the Gillespie algorithm, which simulates a random walk on the network of secondary structures, the expected network degree may provide a better understanding of kinetics of RNA folding when allowing defect diffusion, helix zippering, and related conformation transformations. We determine the correlation between expected network degree, contact order, conformational entropy, and expected number of native contacts for a benchmarking dataset of RNAs. Source code is available at http://bioinformatics.bc.edu/clotelab/RNAexpNumNbors.

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Section: Introductionmentioning

confidence: 99%

Network Properties of the Ensemble of RNA Structures

Clote

Bayegan

2015

PLoS ONE

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“…, n} of states, the initial probability distribution π, and the n × n matrix P (t) = (p i,j (t)) of probability transition functions. 1 Letting q t denote the state at (continuous) time t, the probability that the initial state q 0 at time 0 is k is π k , while…”

Section: Markov Processes and Equilibrium Timementioning

confidence: 99%

“…As well, Kinefold can simulate both refolding and co-transcriptional folding pathways. Finally, unlike all the previous simulation results, which depend on thermodynamic free energy parameters [41], the program Oxfold [1] performs kinetic folding of RNA using stochastic context-free grammars and evolutionary information.…”

Section: Introductionmentioning

confidence: 99%

Fast, Approximate Kinetics of RNA Folding

Senter

Clote

2015

Journal of Computational Biology

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In this article, we introduce the software suite Hermes, which provides fast, novel algorithms for RNA secondary structure kinetics. Using the fast Fourier transform to efficiently compute the Boltzmann probability that a secondary structure S of a given RNA sequence has base pair distance x (resp. y) from reference structure A (resp. B), Hermes computes the exact kinetics of folding from A to B in this coarse-grained model. In particular, Hermes computes the mean first passage time from the transition probability matrix by using matrix inversion, and also computes the equilibrium time from the rate matrix by using spectral decomposition. Due to the model granularity and the speed of Hermes, it is capable of determining secondary structure refolding kinetics for large RNA sequences, beyond the range of other methods. Comparative benchmarking of Hermes with other methods indicates that Hermes provides refolding kinetics of accuracy suitable for use in the computational design of RNA, an important area of synthetic biology. Source code and documentation for Hermes are available.

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“…When sufficient phylogenetic data are unavailable, the thermodynamically most stable RNA structure can be de novo predicted using in silico free‐energy minimization approaches (Lorenz et al, ; Reuter & Mathews, ; Zuker, ). These in silico approaches can even explicitly model the kinetics of RNA folding during transcription (Anderson et al, ; Proctor & Meyer, ; Zhao, Zhang, & Chen, ). Whilst in silico predictions can be quite accurate for small and stable RNA domains (Miao et al, , ), their accuracy rapidly decreases with increasing RNA length, meaning that the structures of biologically important RNA molecules, such as long noncoding RNAs (Somarowthu et al, ) or viral genomes (Mauger et al, ; Watts et al, ) may be poorly predicted without experimental data.…”

Section: Introductionmentioning

confidence: 99%

The evolution of RNA structural probing methods: From gels to next‐generation sequencing

et al. 2018

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RNA molecules are important players in all domains of life and the study of the relationship between their multiple flexible states and the associated biological roles has increased in recent years. For several decades, chemical and enzymatic structural probing experiments have been used to determine RNA structure. During this time, there has been a steady improvement in probing reagents and experimental methods, and today the structural biologist community has a large range of tools at its disposal to probe the secondary structure of RNAs in vitro and in cells. Early experiments used radioactive labeling and polyacrylamide gel electrophoresis as read‐out methods. This was superseded by capillary electrophoresis, and more recently by next‐generation sequencing. Today, powerful structural probing methods can characterize RNA structure on a genome‐wide scale. In this review, we will provide an overview of RNA structural probing methodologies from a historical and technical perspective. This article is categorized under: RNA Structure and Dynamics > RNA Structure, Dynamics, and Chemistry RNA Methods > RNA Analyses in vitro and In Silico RNA Methods > RNA Analyses in Cells

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Oxfold: kinetic folding of RNA using stochastic context-free grammars and evolutionary information

Cited by 13 publications

References 26 publications

Network Properties of the Ensemble of RNA Structures

Network Properties of the Ensemble of RNA Structures

Fast, Approximate Kinetics of RNA Folding

The evolution of RNA structural probing methods: From gels to next‐generation sequencing

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