Median Modified Wiener Filter for nonlinear adaptive spatial denoising of protein NMR multidimensional spectra

Handle URI:
http://hdl.handle.net/10754/344037
Title:
Median Modified Wiener Filter for nonlinear adaptive spatial denoising of protein NMR multidimensional spectra
Authors:
Cannistraci, Carlo Vittorio; Abbas, Ahmed; Gao, Xin ( 0000-0002-7108-3574 )
Abstract:
Denoising multidimensional NMR-spectra is a fundamental step in NMR protein structure determination. The state-of-the-art method uses wavelet-denoising, which may suffer when applied to non-stationary signals affected by Gaussian-white-noise mixed with strong impulsive artifacts, like those in multi-dimensional NMR-spectra. Regrettably, Wavelet's performance depends on a combinatorial search of wavelet shapes and parameters; and multi-dimensional extension of wavelet-denoising is highly non-trivial, which hampers its application to multidimensional NMR-spectra. Here, we endorse a diverse philosophy of denoising NMR-spectra: less is more! We consider spatial filters that have only one parameter to tune: the window-size. We propose, for the first time, the 3D extension of the median-modified-Wiener-filter (MMWF), an adaptive variant of the median-filter, and also its novel variation named MMWF*. We test the proposed filters and the Wiener-filter, an adaptive variant of the mean-filter, on a benchmark set that contains 16 two-dimensional and three-dimensional NMR-spectra extracted from eight proteins. Our results demonstrate that the adaptive spatial filters significantly outperform their non-adaptive versions. The performance of the new MMWF* on 2D/3D-spectra is even better than wavelet-denoising. Noticeably, MMWF* produces stable high performance almost invariant for diverse window-size settings: this signifies a consistent advantage in the implementation of automatic pipelines for protein NMR-spectra analysis.
KAUST Department:
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Citation:
Median Modified Wiener Filter for nonlinear adaptive spatial denoising of protein NMR multidimensional spectra 2015, 5:8017 Scientific Reports
Publisher:
Nature Publishing Group
Journal:
Scientific Reports
Issue Date:
26-Jan-2015
DOI:
10.1038/srep08017
PubMed ID:
25619991
PubMed Central ID:
PMC4306135
Type:
Article
ISSN:
2045-2322
Sponsors:
This work was supported by the independent group leader starting grant of the Technische Universita¨t Dresden (TUD), and Award No. GRP-CF-2011-19-P-Gao-Huang and a GMSV-OCRF award from King Abdullah University of Science and Technology (KAUST).
Additional Links:
http://www.nature.com/doifinder/10.1038/srep08017
Appears in Collections:
Articles; Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

Full metadata record

DC FieldValue Language
dc.contributor.authorCannistraci, Carlo Vittorioen
dc.contributor.authorAbbas, Ahmeden
dc.contributor.authorGao, Xinen
dc.date.accessioned2015-02-02T07:48:02Z-
dc.date.available2015-02-02T07:48:02Z-
dc.date.issued2015-01-26en
dc.identifier.citationMedian Modified Wiener Filter for nonlinear adaptive spatial denoising of protein NMR multidimensional spectra 2015, 5:8017 Scientific Reportsen
dc.identifier.issn2045-2322en
dc.identifier.pmid25619991en
dc.identifier.doi10.1038/srep08017en
dc.identifier.urihttp://hdl.handle.net/10754/344037en
dc.description.abstractDenoising multidimensional NMR-spectra is a fundamental step in NMR protein structure determination. The state-of-the-art method uses wavelet-denoising, which may suffer when applied to non-stationary signals affected by Gaussian-white-noise mixed with strong impulsive artifacts, like those in multi-dimensional NMR-spectra. Regrettably, Wavelet's performance depends on a combinatorial search of wavelet shapes and parameters; and multi-dimensional extension of wavelet-denoising is highly non-trivial, which hampers its application to multidimensional NMR-spectra. Here, we endorse a diverse philosophy of denoising NMR-spectra: less is more! We consider spatial filters that have only one parameter to tune: the window-size. We propose, for the first time, the 3D extension of the median-modified-Wiener-filter (MMWF), an adaptive variant of the median-filter, and also its novel variation named MMWF*. We test the proposed filters and the Wiener-filter, an adaptive variant of the mean-filter, on a benchmark set that contains 16 two-dimensional and three-dimensional NMR-spectra extracted from eight proteins. Our results demonstrate that the adaptive spatial filters significantly outperform their non-adaptive versions. The performance of the new MMWF* on 2D/3D-spectra is even better than wavelet-denoising. Noticeably, MMWF* produces stable high performance almost invariant for diverse window-size settings: this signifies a consistent advantage in the implementation of automatic pipelines for protein NMR-spectra analysis.en
dc.description.sponsorshipThis work was supported by the independent group leader starting grant of the Technische Universita¨t Dresden (TUD), and Award No. GRP-CF-2011-19-P-Gao-Huang and a GMSV-OCRF award from King Abdullah University of Science and Technology (KAUST).en
dc.language.isoenen
dc.publisherNature Publishing Groupen
dc.relation.urlhttp://www.nature.com/doifinder/10.1038/srep08017en
dc.rightsThis work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License. The images or other third party material in this article are included in the article's Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder in order to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by-nc-sa/4.0/en
dc.subjectComputational biology and bioinformaticsen
dc.subjectStructural biologyen
dc.titleMedian Modified Wiener Filter for nonlinear adaptive spatial denoising of protein NMR multidimensional spectraen
dc.typeArticleen
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Divisionen
dc.identifier.journalScientific Reportsen
dc.identifier.pmcidPMC4306135en
dc.eprint.versionPublisher's Version/PDFen
dc.contributor.institutionBiomedical Cybernetics Group, Biotechnology Center (BIOTEC), Technische Universität Dresden, Tatzberg 47/49, 01307 Dresden, Germanyen
dc.contributor.affiliationKing Abdullah University of Science and Technology (KAUST)en
kaust.authorAbbas, Ahmeden
kaust.authorGao, Xinen
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