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Automatic workflow for the classification of local DNA conformations

BACKGROUND: A growing number of crystal and NMR structures reveals a considerable structural polymorphism of DNA architecture going well beyond the usual image of a double helical molecule. DNA is highly variable with dinucleotide steps exhibiting a substantial flexibility in a sequence-dependent ma...

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Autores principales: Čech, Petr, Kukal, Jaromír, Černý, Jiří, Schneider, Bohdan, Svozil, Daniel
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2013
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3694522/
https://www.ncbi.nlm.nih.gov/pubmed/23800225
http://dx.doi.org/10.1186/1471-2105-14-205
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author Čech, Petr
Kukal, Jaromír
Černý, Jiří
Schneider, Bohdan
Svozil, Daniel
author_facet Čech, Petr
Kukal, Jaromír
Černý, Jiří
Schneider, Bohdan
Svozil, Daniel
author_sort Čech, Petr
collection PubMed
description BACKGROUND: A growing number of crystal and NMR structures reveals a considerable structural polymorphism of DNA architecture going well beyond the usual image of a double helical molecule. DNA is highly variable with dinucleotide steps exhibiting a substantial flexibility in a sequence-dependent manner. An analysis of the conformational space of the DNA backbone and the enhancement of our understanding of the conformational dependencies in DNA are therefore important for full comprehension of DNA structural polymorphism. RESULTS: A detailed classification of local DNA conformations based on the technique of Fourier averaging was published in our previous work. However, this procedure requires a considerable amount of manual work. To overcome this limitation we developed an automatic classification method consisting of the combination of supervised and unsupervised approaches. A proposed workflow is composed of k-NN method followed by a non-hierarchical single-pass clustering algorithm. We applied this workflow to analyze 816 X-ray and 664 NMR DNA structures released till February 2013. We identified and annotated six new conformers, and we assigned four of these conformers to two structurally important DNA families: guanine quadruplexes and Holliday (four-way) junctions. We also compared populations of the assigned conformers in the dataset of X-ray and NMR structures. CONCLUSIONS: In the present work we developed a machine learning workflow for the automatic classification of dinucleotide conformations. Dinucleotides with unassigned conformations can be either classified into one of already known 24 classes or they can be flagged as unclassifiable. The proposed machine learning workflow permits identification of new classes among so far unclassifiable data, and we identified and annotated six new conformations in the X-ray structures released since our previous analysis. The results illustrate the utility of machine learning approaches in the classification of local DNA conformations.
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spelling pubmed-36945222013-06-28 Automatic workflow for the classification of local DNA conformations Čech, Petr Kukal, Jaromír Černý, Jiří Schneider, Bohdan Svozil, Daniel BMC Bioinformatics Research Article BACKGROUND: A growing number of crystal and NMR structures reveals a considerable structural polymorphism of DNA architecture going well beyond the usual image of a double helical molecule. DNA is highly variable with dinucleotide steps exhibiting a substantial flexibility in a sequence-dependent manner. An analysis of the conformational space of the DNA backbone and the enhancement of our understanding of the conformational dependencies in DNA are therefore important for full comprehension of DNA structural polymorphism. RESULTS: A detailed classification of local DNA conformations based on the technique of Fourier averaging was published in our previous work. However, this procedure requires a considerable amount of manual work. To overcome this limitation we developed an automatic classification method consisting of the combination of supervised and unsupervised approaches. A proposed workflow is composed of k-NN method followed by a non-hierarchical single-pass clustering algorithm. We applied this workflow to analyze 816 X-ray and 664 NMR DNA structures released till February 2013. We identified and annotated six new conformers, and we assigned four of these conformers to two structurally important DNA families: guanine quadruplexes and Holliday (four-way) junctions. We also compared populations of the assigned conformers in the dataset of X-ray and NMR structures. CONCLUSIONS: In the present work we developed a machine learning workflow for the automatic classification of dinucleotide conformations. Dinucleotides with unassigned conformations can be either classified into one of already known 24 classes or they can be flagged as unclassifiable. The proposed machine learning workflow permits identification of new classes among so far unclassifiable data, and we identified and annotated six new conformations in the X-ray structures released since our previous analysis. The results illustrate the utility of machine learning approaches in the classification of local DNA conformations. BioMed Central 2013-06-25 /pmc/articles/PMC3694522/ /pubmed/23800225 http://dx.doi.org/10.1186/1471-2105-14-205 Text en Copyright © 2013 Čech et al.; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/2.0 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Čech, Petr
Kukal, Jaromír
Černý, Jiří
Schneider, Bohdan
Svozil, Daniel
Automatic workflow for the classification of local DNA conformations
title Automatic workflow for the classification of local DNA conformations
title_full Automatic workflow for the classification of local DNA conformations
title_fullStr Automatic workflow for the classification of local DNA conformations
title_full_unstemmed Automatic workflow for the classification of local DNA conformations
title_short Automatic workflow for the classification of local DNA conformations
title_sort automatic workflow for the classification of local dna conformations
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3694522/
https://www.ncbi.nlm.nih.gov/pubmed/23800225
http://dx.doi.org/10.1186/1471-2105-14-205
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