Characterization and visualization of RNA secondary structure Boltzmann ensemble via information theory
Abstract Background The nearest neighbor model and associated dynamic programming algorithms allow for the efficient estimation of the RNA secondary structure Boltzmann ensemble. However because a given RNA secondary structure only contains a fraction of the possible helices that could form from a g...
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doaj-c711403e3c7e4674905efc0b7aec9d962020-11-25T02:10:08ZengBMCBMC Bioinformatics1471-21052018-03-0119111010.1186/s12859-018-2078-5Characterization and visualization of RNA secondary structure Boltzmann ensemble via information theoryLuan Lin0Wilson H. McKerrow1Bryce Richards2Chukiat Phonsom3Charles E. Lawrence4Center for Devices and Radiological Health, U.S. Food and Drug AdministrationDivision of Applied Mathematics, Brown UniversitySoftware Engineer, GoogleDepartment of Mathematics, University of Southern CaliforniaDivision of Applied Mathematics, Brown UniversityAbstract Background The nearest neighbor model and associated dynamic programming algorithms allow for the efficient estimation of the RNA secondary structure Boltzmann ensemble. However because a given RNA secondary structure only contains a fraction of the possible helices that could form from a given sequence, the Boltzmann ensemble is multimodal. Several methods exist for clustering structures and finding those modes. However less focus is given to exploring the underlying reasons for this multimodality: the presence of conflicting basepairs. Information theory, or more specifically mutual information, provides a method to identify those basepairs that are key to the secondary structure. Results To this end we find most informative basepairs and visualize the effect of these basepairs on the secondary structure. Knowing whether a most informative basepair is present tells us not only the status of the particular pair but also provides a large amount of information about which other pairs are present or not present. We find that a few basepairs account for a large amount of the structural uncertainty. The identification of these pairs indicates small changes to sequence or stability that will have a large effect on structure. Conclusion We provide a novel algorithm that uses mutual information to identify the key basepairs that lead to a multimodal Boltzmann distribution. We then visualize the effect of these pairs on the overall Boltzmann ensemble.http://link.springer.com/article/10.1186/s12859-018-2078-5RNARNA secondary structureNearest neighbor modelInformation theoryMutual information |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Luan Lin Wilson H. McKerrow Bryce Richards Chukiat Phonsom Charles E. Lawrence |
spellingShingle |
Luan Lin Wilson H. McKerrow Bryce Richards Chukiat Phonsom Charles E. Lawrence Characterization and visualization of RNA secondary structure Boltzmann ensemble via information theory BMC Bioinformatics RNA RNA secondary structure Nearest neighbor model Information theory Mutual information |
author_facet |
Luan Lin Wilson H. McKerrow Bryce Richards Chukiat Phonsom Charles E. Lawrence |
author_sort |
Luan Lin |
title |
Characterization and visualization of RNA secondary structure Boltzmann ensemble via information theory |
title_short |
Characterization and visualization of RNA secondary structure Boltzmann ensemble via information theory |
title_full |
Characterization and visualization of RNA secondary structure Boltzmann ensemble via information theory |
title_fullStr |
Characterization and visualization of RNA secondary structure Boltzmann ensemble via information theory |
title_full_unstemmed |
Characterization and visualization of RNA secondary structure Boltzmann ensemble via information theory |
title_sort |
characterization and visualization of rna secondary structure boltzmann ensemble via information theory |
publisher |
BMC |
series |
BMC Bioinformatics |
issn |
1471-2105 |
publishDate |
2018-03-01 |
description |
Abstract Background The nearest neighbor model and associated dynamic programming algorithms allow for the efficient estimation of the RNA secondary structure Boltzmann ensemble. However because a given RNA secondary structure only contains a fraction of the possible helices that could form from a given sequence, the Boltzmann ensemble is multimodal. Several methods exist for clustering structures and finding those modes. However less focus is given to exploring the underlying reasons for this multimodality: the presence of conflicting basepairs. Information theory, or more specifically mutual information, provides a method to identify those basepairs that are key to the secondary structure. Results To this end we find most informative basepairs and visualize the effect of these basepairs on the secondary structure. Knowing whether a most informative basepair is present tells us not only the status of the particular pair but also provides a large amount of information about which other pairs are present or not present. We find that a few basepairs account for a large amount of the structural uncertainty. The identification of these pairs indicates small changes to sequence or stability that will have a large effect on structure. Conclusion We provide a novel algorithm that uses mutual information to identify the key basepairs that lead to a multimodal Boltzmann distribution. We then visualize the effect of these pairs on the overall Boltzmann ensemble. |
topic |
RNA RNA secondary structure Nearest neighbor model Information theory Mutual information |
url |
http://link.springer.com/article/10.1186/s12859-018-2078-5 |
work_keys_str_mv |
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