Cargando…
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...
Autores principales: | , , , , |
---|---|
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 |
_version_ | 1782274870829121536 |
---|---|
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. |
format | Online Article Text |
id | pubmed-3694522 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2013 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
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 |
work_keys_str_mv | AT cechpetr automaticworkflowfortheclassificationoflocaldnaconformations AT kukaljaromir automaticworkflowfortheclassificationoflocaldnaconformations AT cernyjiri automaticworkflowfortheclassificationoflocaldnaconformations AT schneiderbohdan automaticworkflowfortheclassificationoflocaldnaconformations AT svozildaniel automaticworkflowfortheclassificationoflocaldnaconformations |