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Peak picking NMR spectral data using non-negative matrix factorization
BACKGROUND: Simple peak-picking algorithms, such as those based on lineshape fitting, perform well when peaks are completely resolved in multidimensional NMR spectra, but often produce wrong intensities and frequencies for overlapping peak clusters. For example, NOESY-type spectra have considerable...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2014
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3931316/ https://www.ncbi.nlm.nih.gov/pubmed/24511909 http://dx.doi.org/10.1186/1471-2105-15-46 |
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author | Tikole, Suhas Jaravine, Victor Rogov, Vladimir Dötsch, Volker Güntert, Peter |
author_facet | Tikole, Suhas Jaravine, Victor Rogov, Vladimir Dötsch, Volker Güntert, Peter |
author_sort | Tikole, Suhas |
collection | PubMed |
description | BACKGROUND: Simple peak-picking algorithms, such as those based on lineshape fitting, perform well when peaks are completely resolved in multidimensional NMR spectra, but often produce wrong intensities and frequencies for overlapping peak clusters. For example, NOESY-type spectra have considerable overlaps leading to significant peak-picking intensity errors, which can result in erroneous structural restraints. Precise frequencies are critical for unambiguous resonance assignments. RESULTS: To alleviate this problem, a more sophisticated peaks decomposition algorithm, based on non-negative matrix factorization (NMF), was developed. We produce peak shapes from Fourier-transformed NMR spectra. Apart from its main goal of deriving components from spectra and producing peak lists automatically, the NMF approach can also be applied if the positions of some peaks are known a priori, e.g. from consistently referenced spectral dimensions of other experiments. CONCLUSIONS: Application of the NMF algorithm to a three-dimensional peak list of the 23 kDa bi-domain section of the RcsD protein (RcsD-ABL-HPt, residues 688-890) as well as to synthetic HSQC data shows that peaks can be picked accurately also in spectral regions with strong overlap. |
format | Online Article Text |
id | pubmed-3931316 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2014 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-39313162014-03-04 Peak picking NMR spectral data using non-negative matrix factorization Tikole, Suhas Jaravine, Victor Rogov, Vladimir Dötsch, Volker Güntert, Peter BMC Bioinformatics Methodology Article BACKGROUND: Simple peak-picking algorithms, such as those based on lineshape fitting, perform well when peaks are completely resolved in multidimensional NMR spectra, but often produce wrong intensities and frequencies for overlapping peak clusters. For example, NOESY-type spectra have considerable overlaps leading to significant peak-picking intensity errors, which can result in erroneous structural restraints. Precise frequencies are critical for unambiguous resonance assignments. RESULTS: To alleviate this problem, a more sophisticated peaks decomposition algorithm, based on non-negative matrix factorization (NMF), was developed. We produce peak shapes from Fourier-transformed NMR spectra. Apart from its main goal of deriving components from spectra and producing peak lists automatically, the NMF approach can also be applied if the positions of some peaks are known a priori, e.g. from consistently referenced spectral dimensions of other experiments. CONCLUSIONS: Application of the NMF algorithm to a three-dimensional peak list of the 23 kDa bi-domain section of the RcsD protein (RcsD-ABL-HPt, residues 688-890) as well as to synthetic HSQC data shows that peaks can be picked accurately also in spectral regions with strong overlap. BioMed Central 2014-02-11 /pmc/articles/PMC3931316/ /pubmed/24511909 http://dx.doi.org/10.1186/1471-2105-15-46 Text en Copyright © 2014 Tikole 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. 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 | Methodology Article Tikole, Suhas Jaravine, Victor Rogov, Vladimir Dötsch, Volker Güntert, Peter Peak picking NMR spectral data using non-negative matrix factorization |
title | Peak picking NMR spectral data using non-negative matrix factorization |
title_full | Peak picking NMR spectral data using non-negative matrix factorization |
title_fullStr | Peak picking NMR spectral data using non-negative matrix factorization |
title_full_unstemmed | Peak picking NMR spectral data using non-negative matrix factorization |
title_short | Peak picking NMR spectral data using non-negative matrix factorization |
title_sort | peak picking nmr spectral data using non-negative matrix factorization |
topic | Methodology Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3931316/ https://www.ncbi.nlm.nih.gov/pubmed/24511909 http://dx.doi.org/10.1186/1471-2105-15-46 |
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