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CoFold: an RNA secondary structure prediction method that takes co-transcriptional folding into account

Existing state-of-the-art methods that take a single RNA sequence and predict the corresponding RNA secondary structure are thermodynamic methods. These aim to predict the most stable RNA structure. There exists by now ample experimental and theoretical evidence that the process of structure formati...

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Autores principales: Proctor, Jeff R., Meyer, Irmtraud M.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2013
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3643587/
https://www.ncbi.nlm.nih.gov/pubmed/23511969
http://dx.doi.org/10.1093/nar/gkt174
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author Proctor, Jeff R.
Meyer, Irmtraud M.
author_facet Proctor, Jeff R.
Meyer, Irmtraud M.
author_sort Proctor, Jeff R.
collection PubMed
description Existing state-of-the-art methods that take a single RNA sequence and predict the corresponding RNA secondary structure are thermodynamic methods. These aim to predict the most stable RNA structure. There exists by now ample experimental and theoretical evidence that the process of structure formation matters and that sequences in vivo fold while they are being transcribed. None of the thermodynamic methods, however, consider the process of structure formation. Here, we present a conceptually new method for predicting RNA secondary structure, called CoFold, that takes effects of co-transcriptional folding explicitly into account. Our method significantly improves the state-of-art in terms of prediction accuracy, especially for long sequences of >1000 nt in length.
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spelling pubmed-36435872013-05-03 CoFold: an RNA secondary structure prediction method that takes co-transcriptional folding into account Proctor, Jeff R. Meyer, Irmtraud M. Nucleic Acids Res Methods Online Existing state-of-the-art methods that take a single RNA sequence and predict the corresponding RNA secondary structure are thermodynamic methods. These aim to predict the most stable RNA structure. There exists by now ample experimental and theoretical evidence that the process of structure formation matters and that sequences in vivo fold while they are being transcribed. None of the thermodynamic methods, however, consider the process of structure formation. Here, we present a conceptually new method for predicting RNA secondary structure, called CoFold, that takes effects of co-transcriptional folding explicitly into account. Our method significantly improves the state-of-art in terms of prediction accuracy, especially for long sequences of >1000 nt in length. Oxford University Press 2013-05 2013-03-19 /pmc/articles/PMC3643587/ /pubmed/23511969 http://dx.doi.org/10.1093/nar/gkt174 Text en © The Author(s) 2013. Published by Oxford University Press. http://creativecommons.org/licenses/by-nc/3.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0/), which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Methods Online
Proctor, Jeff R.
Meyer, Irmtraud M.
CoFold: an RNA secondary structure prediction method that takes co-transcriptional folding into account
title CoFold: an RNA secondary structure prediction method that takes co-transcriptional folding into account
title_full CoFold: an RNA secondary structure prediction method that takes co-transcriptional folding into account
title_fullStr CoFold: an RNA secondary structure prediction method that takes co-transcriptional folding into account
title_full_unstemmed CoFold: an RNA secondary structure prediction method that takes co-transcriptional folding into account
title_short CoFold: an RNA secondary structure prediction method that takes co-transcriptional folding into account
title_sort cofold: an rna secondary structure prediction method that takes co-transcriptional folding into account
topic Methods Online
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3643587/
https://www.ncbi.nlm.nih.gov/pubmed/23511969
http://dx.doi.org/10.1093/nar/gkt174
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