Profiling small RNA reveals multimodal substructural signals in a Boltzmann ensemble

As the biomedical impact of small RNAs grows, so does the need to understand competing structural alternatives for regions of functional interest. Suboptimal structure analysis provides significantly more RNA base pairing information than a single minimum free energy prediction. Yet computational en...

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Main Authors:	Rogers, Emily, Heitsch, Christine E.
Format:	Online
Language:	English
Published:	Oxford University Press 2014
Online Access:	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4267672/

id	pubmed-4267672
recordtype	oai_dc
spelling	pubmed-42676722014-12-23 Profiling small RNA reveals multimodal substructural signals in a Boltzmann ensemble Rogers, Emily Heitsch, Christine E. Methods Online As the biomedical impact of small RNAs grows, so does the need to understand competing structural alternatives for regions of functional interest. Suboptimal structure analysis provides significantly more RNA base pairing information than a single minimum free energy prediction. Yet computational enhancements like Boltzmann sampling have not been fully adopted by experimentalists since identifying meaningful patterns in this data can be challenging. Profiling is a novel approach to mining RNA suboptimal structure data which makes the power of ensemble-based analysis accessible in a stable and reliable way. Balancing abstraction and specificity, profiling identifies significant combinations of base pairs which dominate low-energy RNA secondary structures. By design, critical similarities and differences are highlighted, yielding crucial information for molecular biologists. The code is freely available via http://gtfold.sourceforge.net/profiling.html. Oxford University Press 2014-12-16 2014-11-11 /pmc/articles/PMC4267672/ /pubmed/25392423 http://dx.doi.org/10.1093/nar/gku959 Text en © The Author(s) 2014. Published by Oxford University Press on behalf of Nucleic Acids Research. http://creativecommons.org/licenses/by-nc/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com
repository_type	Open Access Journal
institution_category	Foreign Institution
institution	US National Center for Biotechnology Information
building	NCBI PubMed
collection	Online Access
language	English
format	Online
author	Rogers, Emily Heitsch, Christine E.
spellingShingle	Rogers, Emily Heitsch, Christine E. Profiling small RNA reveals multimodal substructural signals in a Boltzmann ensemble
author_facet	Rogers, Emily Heitsch, Christine E.
author_sort	Rogers, Emily
title	Profiling small RNA reveals multimodal substructural signals in a Boltzmann ensemble
title_short	Profiling small RNA reveals multimodal substructural signals in a Boltzmann ensemble
title_full	Profiling small RNA reveals multimodal substructural signals in a Boltzmann ensemble
title_fullStr	Profiling small RNA reveals multimodal substructural signals in a Boltzmann ensemble
title_full_unstemmed	Profiling small RNA reveals multimodal substructural signals in a Boltzmann ensemble
title_sort	profiling small rna reveals multimodal substructural signals in a boltzmann ensemble
description	As the biomedical impact of small RNAs grows, so does the need to understand competing structural alternatives for regions of functional interest. Suboptimal structure analysis provides significantly more RNA base pairing information than a single minimum free energy prediction. Yet computational enhancements like Boltzmann sampling have not been fully adopted by experimentalists since identifying meaningful patterns in this data can be challenging. Profiling is a novel approach to mining RNA suboptimal structure data which makes the power of ensemble-based analysis accessible in a stable and reliable way. Balancing abstraction and specificity, profiling identifies significant combinations of base pairs which dominate low-energy RNA secondary structures. By design, critical similarities and differences are highlighted, yielding crucial information for molecular biologists. The code is freely available via http://gtfold.sourceforge.net/profiling.html.
publisher	Oxford University Press
publishDate	2014
url	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4267672/
_version_	1613168036295999488

Profiling small RNA reveals multimodal substructural signals in a Boltzmann ensemble

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