RNAG: a new Gibbs sampler for predicting RNA secondary structure for unaligned sequences

Wei, Donglai; Alpert, Lauren V.; Lawrence, Charles E.

doi:10.1093/bioinformatics/btr421

Title: RNAG: a new Gibbs sampler for predicting RNA secondary structure for unaligned sequences

Journal Article · Sun Jul 24 00:00:00 EDT 2011 · Bioinformatics

DOI:https://doi.org/10.1093/bioinformatics/btr421· OSTI ID:1625276

Wei, Donglai ^[1]; Alpert, Lauren V. ^[2]; Lawrence, Charles E. ^[2]

Brown Univ., Providence, RI (United States). Dept. of Mathematics
Brown Univ., Providence, RI (United States). Division of Applied Mathematics. Center for Computational Molecular Biology

Motivation: RNA secondary structure plays an important role in the function of many RNAs, and structural features are often key to their interaction with other cellular components. Thus, there has been considerable interest in the prediction of secondary structures for RNA families. In this article, we present a new global structural alignment algorithm, RNAG, to predict consensus secondary structures for unaligned sequences. It uses a blocked Gibbs sampling algorithm, which has a theoretical advantage in convergence time. This algorithm iteratively samples from the conditional probability distributions P(Structure | Alignment) and P(Alignment | Structure). Not surprisingly, there is considerable uncertainly in the high-dimensional space of this difficult problem, which has so far received limited attention in this field. We show how the samples drawn from this algorithm can be used to more fully characterize the posterior space and to assess the uncertainty of predictions. Results: Our analysis of three publically available datasets showed a substantial improvement in RNA structure prediction by RNAG over extant prediction methods. Additionally, our analysis of 17 RNA families showed that the RNAG sampled structures were generally compact around their ensemble centroids, and at least 11 families had at least two well-separated clusters of predicted structures. In general, the distance between a reference structure and our predicted structure was large relative to the variation among structures within an ensemble. Availability: The Perl implementation of the RNAG algorithm and the data necessary to reproduce the results described in Sections 3.1 and 3.2 are available at http://ccmbweb.ccv.brown.edu/rnag.html

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Research Organization:: Brown Univ., Providence, RI (United States)

Sponsoring Organization:: USDOE Office of Science (SC)

Grant/Contract Number:: FG02-04ER63942

OSTI ID:: 1625276

Journal Information:: Bioinformatics, Vol. 27, Issue 18; ISSN 1367-4803

Publisher:: Oxford University PressCopyright Statement

Country of Publication:: United States

Language:: English

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CompaRNA: a server for continuous benchmarking of automated methods for RNA secondary structure prediction Puton, Tomasz; Kozlowski, Lukasz P.; Rother, Kristian M. Nucleic Acids Research, Vol. 41, Issue 7 https://doi.org/10.1093/nar/gkt101	journal	February 2013
Fighting against uncertainty: An essential issue in bioinformatics Hamada, Michiaki arXiv https://doi.org/10.48550/arxiv.1305.3655	preprint	January 2013
RNA secondary structure prediction from multi-aligned sequences Hamada, Michiaki arXiv https://doi.org/10.48550/arxiv.1307.1934	preprint	January 2013

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