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Sparse RNA folding revisited: space-efficient minimum free energy structure prediction
BACKGROUND: RNA secondary structure prediction by energy minimization is the central computational tool for the analysis of structural non-coding RNAs and their interactions. Sparsification has been successfully applied to improve the time efficiency of various structure prediction algorithms while...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2016
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4842305/ https://www.ncbi.nlm.nih.gov/pubmed/27110275 http://dx.doi.org/10.1186/s13015-016-0071-y |
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author | Will, Sebastian Jabbari, Hosna |
author_facet | Will, Sebastian Jabbari, Hosna |
author_sort | Will, Sebastian |
collection | PubMed |
description | BACKGROUND: RNA secondary structure prediction by energy minimization is the central computational tool for the analysis of structural non-coding RNAs and their interactions. Sparsification has been successfully applied to improve the time efficiency of various structure prediction algorithms while guaranteeing the same result; however, for many such folding problems, space efficiency is of even greater concern, particularly for long RNA sequences. So far, space-efficient sparsified RNA folding with fold reconstruction was solved only for simple base-pair-based pseudo-energy models. RESULTS: Here, we revisit the problem of space-efficient free energy minimization. Whereas the space-efficient minimization of the free energy has been sketched before, the reconstruction of the optimum structure has not even been discussed. We show that this reconstruction is not possible in trivial extension of the method for simple energy models. Then, we present the time- and space-efficient sparsified free energy minimization algorithm SparseMFEFold that guarantees MFE structure prediction. In particular, this novel algorithm provides efficient fold reconstruction based on dynamically garbage-collected trace arrows. The complexity of our algorithm depends on two parameters, the number of candidates Z and the number of trace arrows T; both are bounded by [Formula: see text] , but are typically much smaller. The time complexity of RNA folding is reduced from [Formula: see text] to [Formula: see text] ; the space complexity, from [Formula: see text] to [Formula: see text] . Our empirical results show more than 80 % space savings over RNAfold [Vienna RNA package] on the long RNAs from the RNA STRAND database (≥2500 bases). CONCLUSIONS: The presented technique is intentionally generalizable to complex prediction algorithms; due to their high space demands, algorithms like pseudoknot prediction and RNA–RNA-interaction prediction are expected to profit even stronger than “standard” MFE folding. SparseMFEFold is free software, available at http://www.bioinf.uni-leipzig.de/~will/Software/SparseMFEFold. |
format | Online Article Text |
id | pubmed-4842305 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2016 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-48423052016-04-25 Sparse RNA folding revisited: space-efficient minimum free energy structure prediction Will, Sebastian Jabbari, Hosna Algorithms Mol Biol Review Article BACKGROUND: RNA secondary structure prediction by energy minimization is the central computational tool for the analysis of structural non-coding RNAs and their interactions. Sparsification has been successfully applied to improve the time efficiency of various structure prediction algorithms while guaranteeing the same result; however, for many such folding problems, space efficiency is of even greater concern, particularly for long RNA sequences. So far, space-efficient sparsified RNA folding with fold reconstruction was solved only for simple base-pair-based pseudo-energy models. RESULTS: Here, we revisit the problem of space-efficient free energy minimization. Whereas the space-efficient minimization of the free energy has been sketched before, the reconstruction of the optimum structure has not even been discussed. We show that this reconstruction is not possible in trivial extension of the method for simple energy models. Then, we present the time- and space-efficient sparsified free energy minimization algorithm SparseMFEFold that guarantees MFE structure prediction. In particular, this novel algorithm provides efficient fold reconstruction based on dynamically garbage-collected trace arrows. The complexity of our algorithm depends on two parameters, the number of candidates Z and the number of trace arrows T; both are bounded by [Formula: see text] , but are typically much smaller. The time complexity of RNA folding is reduced from [Formula: see text] to [Formula: see text] ; the space complexity, from [Formula: see text] to [Formula: see text] . Our empirical results show more than 80 % space savings over RNAfold [Vienna RNA package] on the long RNAs from the RNA STRAND database (≥2500 bases). CONCLUSIONS: The presented technique is intentionally generalizable to complex prediction algorithms; due to their high space demands, algorithms like pseudoknot prediction and RNA–RNA-interaction prediction are expected to profit even stronger than “standard” MFE folding. SparseMFEFold is free software, available at http://www.bioinf.uni-leipzig.de/~will/Software/SparseMFEFold. BioMed Central 2016-04-23 /pmc/articles/PMC4842305/ /pubmed/27110275 http://dx.doi.org/10.1186/s13015-016-0071-y Text en © Will and Jabbari. 2016 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. |
spellingShingle | Review Article Will, Sebastian Jabbari, Hosna Sparse RNA folding revisited: space-efficient minimum free energy structure prediction |
title | Sparse RNA folding revisited: space-efficient minimum free energy structure prediction |
title_full | Sparse RNA folding revisited: space-efficient minimum free energy structure prediction |
title_fullStr | Sparse RNA folding revisited: space-efficient minimum free energy structure prediction |
title_full_unstemmed | Sparse RNA folding revisited: space-efficient minimum free energy structure prediction |
title_short | Sparse RNA folding revisited: space-efficient minimum free energy structure prediction |
title_sort | sparse rna folding revisited: space-efficient minimum free energy structure prediction |
topic | Review Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4842305/ https://www.ncbi.nlm.nih.gov/pubmed/27110275 http://dx.doi.org/10.1186/s13015-016-0071-y |
work_keys_str_mv | AT willsebastian sparsernafoldingrevisitedspaceefficientminimumfreeenergystructureprediction AT jabbarihosna sparsernafoldingrevisitedspaceefficientminimumfreeenergystructureprediction |