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Evaluating DCA-based method performances for RNA contact prediction by a well-curated data set

RNA molecules play many pivotal roles in a cell that are still not fully understood. Any detailed understanding of RNA function requires knowledge of its three-dimensional structure, yet experimental RNA structure resolution remains demanding. Recent advances in sequencing provide unprecedented amou...

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Autores principales: Pucci, Fabrizio, Zerihun, Mehari B., Peter, Emanuel K., Schug, Alexander
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Cold Spring Harbor Laboratory Press 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7297115/
https://www.ncbi.nlm.nih.gov/pubmed/32276988
http://dx.doi.org/10.1261/rna.073809.119
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author Pucci, Fabrizio
Zerihun, Mehari B.
Peter, Emanuel K.
Schug, Alexander
author_facet Pucci, Fabrizio
Zerihun, Mehari B.
Peter, Emanuel K.
Schug, Alexander
author_sort Pucci, Fabrizio
collection PubMed
description RNA molecules play many pivotal roles in a cell that are still not fully understood. Any detailed understanding of RNA function requires knowledge of its three-dimensional structure, yet experimental RNA structure resolution remains demanding. Recent advances in sequencing provide unprecedented amounts of sequence data that can be statistically analyzed by methods such as direct coupling analysis (DCA) to determine spatial proximity or contacts of specific nucleic acid pairs, which improve the quality of structure prediction. To quantify this structure prediction improvement, we here present a well curated data set of about 70 RNA structures of high resolution and compare different nucleotide–nucleotide contact prediction methods available in the literature. We observe only minor differences between the performances of the different methods. Moreover, we discuss how robust these predictions are for different contact definitions and how strongly they depend on procedures used to curate and align the families of homologous RNA sequences.
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spelling pubmed-72971152021-07-01 Evaluating DCA-based method performances for RNA contact prediction by a well-curated data set Pucci, Fabrizio Zerihun, Mehari B. Peter, Emanuel K. Schug, Alexander RNA Bioinformatics RNA molecules play many pivotal roles in a cell that are still not fully understood. Any detailed understanding of RNA function requires knowledge of its three-dimensional structure, yet experimental RNA structure resolution remains demanding. Recent advances in sequencing provide unprecedented amounts of sequence data that can be statistically analyzed by methods such as direct coupling analysis (DCA) to determine spatial proximity or contacts of specific nucleic acid pairs, which improve the quality of structure prediction. To quantify this structure prediction improvement, we here present a well curated data set of about 70 RNA structures of high resolution and compare different nucleotide–nucleotide contact prediction methods available in the literature. We observe only minor differences between the performances of the different methods. Moreover, we discuss how robust these predictions are for different contact definitions and how strongly they depend on procedures used to curate and align the families of homologous RNA sequences. Cold Spring Harbor Laboratory Press 2020-07 /pmc/articles/PMC7297115/ /pubmed/32276988 http://dx.doi.org/10.1261/rna.073809.119 Text en © 2020 Pucci et al.; Published by Cold Spring Harbor Laboratory Press for the RNA Society http://creativecommons.org/licenses/by-nc/4.0/ This article is distributed exclusively by the RNA Society for the first 12 months after the full-issue publication date (see http://rnajournal.cshlp.org/site/misc/terms.xhtml). After 12 months, it is available under a Creative Commons License (Attribution-NonCommercial 4.0 International), as described at http://creativecommons.org/licenses/by-nc/4.0/.
spellingShingle Bioinformatics
Pucci, Fabrizio
Zerihun, Mehari B.
Peter, Emanuel K.
Schug, Alexander
Evaluating DCA-based method performances for RNA contact prediction by a well-curated data set
title Evaluating DCA-based method performances for RNA contact prediction by a well-curated data set
title_full Evaluating DCA-based method performances for RNA contact prediction by a well-curated data set
title_fullStr Evaluating DCA-based method performances for RNA contact prediction by a well-curated data set
title_full_unstemmed Evaluating DCA-based method performances for RNA contact prediction by a well-curated data set
title_short Evaluating DCA-based method performances for RNA contact prediction by a well-curated data set
title_sort evaluating dca-based method performances for rna contact prediction by a well-curated data set
topic Bioinformatics
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7297115/
https://www.ncbi.nlm.nih.gov/pubmed/32276988
http://dx.doi.org/10.1261/rna.073809.119
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