Modeling Alternate RNA Structures in Genomic Sequences

Saffarian, Azadeh; Giraud, Mathieu; Touzet, Hélène

doi:10.1089/cmb.2014.0272

Cited by 3 publications

(2 citation statements)

References 14 publications

(10 reference statements)

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“…Standard 2D structure prediction algorithms aim to find thermodynamically stable 2D tRNA structures that minimize free energy given the primary tRNA sequence. This is a straightforward search/optimization task, and it is largely tractable within the tRNA sequences domain, assuming standard complementary base pairings hold (see [ 29 ] for a recent example). The resulting 2D structure is represented by a familiar tRNA “cloverleaf” consisting of the helix (stem) and the loop regions—the former subject to canonical Watson–Crick and GU pairing rules—the latter, less constrained.…”

Section: Discussion and Future Directionsmentioning

confidence: 99%

Intrinsic Properties of tRNA Molecules as Deciphered via Bayesian Network and Distribution Divergence Analysis

Branciamore

Gogoshin

Giulio

et al. 2018

Life

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The identity/recognition of tRNAs, in the context of aminoacyl tRNA synthetases (and other molecules), is a complex phenomenon that has major implications ranging from the origins and evolution of translation machinery and genetic code to the evolution and speciation of tRNAs themselves to human mitochondrial diseases to artificial genetic code engineering. Deciphering it via laboratory experiments, however, is difficult and necessarily time- and resource-consuming. In this study, we propose a mathematically rigorous two-pronged in silico approach to identifying and classifying tRNA positions important for tRNA identity/recognition, rooted in machine learning and information-theoretic methodology. We apply Bayesian Network modeling to elucidate the structure of intra-tRNA-molecule relationships, and distribution divergence analysis to identify meaningful inter-molecule differences between various tRNA subclasses. We illustrate the complementary application of these two approaches using tRNA examples across the three domains of life, and identify and discuss important (informative) positions therein. In summary, we deliver to the tRNA research community a novel, comprehensive methodology for identifying the specific elements of interest in various tRNA molecules, which can be followed up by the corresponding experimental work and/or high-resolution position-specific statistical analyses.

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Section: Discussion and Future Directionsmentioning

confidence: 99%

Intrinsic Properties of tRNA Molecules as Deciphered via Bayesian Network and Distribution Divergence Analysis

Branciamore

Gogoshin

Giulio

et al. 2018

Life

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show abstract

“…There are many strategies for alternative structure prediction (reviewed in [24, 25]). Structural entropy of the RNA folding landscape [26, 27], graph-based representations [28], Markov-state models [29], abstract shapes [30–34], energy-band-based sampling [35] base-pair-distance-based alternative structure prediction [36, 37], and the more recent probability-corrected sampling [38] are just a few examples of using various modeling techniques to explore the RNA folding landscape.…”

Section: Introductionmentioning

confidence: 99%

Structural prediction of RNA switches using conditional base-pair probabilities

Manzourolajdad

Spouge

2019

PLoS ONE

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An RNA switch triggers biological functions by toggling between two conformations. RNA switches include bacterial riboswitches, where ligand binding can stabilize a bound structure. For RNAs with only one stable structure, structural prediction usually just requires a straightforward free energy minimization, but for an RNA switch, the prediction of a less stable alternative structure is often computationally costly and even problematic. The current sampling-clustering method predicts stable and alternative structures by partitioning structures sampled from the energy landscape into two clusters, but it is very time-consuming. Instead, we predict the alternative structure of an RNA switch from conditional probability calculations within the energy landscape. First, our method excludes base pairs related to the most stable structure in the energy landscape. Then, it detects stable stems (“seeds”) in the remaining landscape. Finally, it folds an alternative structure prediction around a seed. While having comparable riboswitch classification performance, the conditional-probability computations had fewer adjustable parameters, offered greater predictive flexibility, and were more than one thousand times faster than the sampling step alone in sampling-clustering predictions, the competing standard. Overall, the described approach helps traverse thermodynamically improbable energy landscapes to find biologically significant substructures and structures rapidly and effectively.

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Alignment distance of regular tree languages

Han

2019

Theoretical Computer Science

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Modeling Alternate RNA Structures in Genomic Sequences

Cited by 3 publications

References 14 publications

Intrinsic Properties of tRNA Molecules as Deciphered via Bayesian Network and Distribution Divergence Analysis

Intrinsic Properties of tRNA Molecules as Deciphered via Bayesian Network and Distribution Divergence Analysis

Structural prediction of RNA switches using conditional base-pair probabilities

Alignment distance of regular tree languages

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