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http://hdl.handle.net/1942/22784
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DC Field | Value | Language |
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dc.contributor.author | Dittwald, Piotr | - |
dc.contributor.author | Nghia, Vu Trung | - |
dc.contributor.author | Harris, Glenn A. | - |
dc.contributor.author | Caprioli, Richard .M. | - |
dc.contributor.author | Van de Plas, Raf | - |
dc.contributor.author | Laukens, Kris | - |
dc.contributor.author | Gambin, Anna | - |
dc.contributor.author | VALKENBORG, Dirk | - |
dc.date.accessioned | 2016-11-28T13:43:38Z | - |
dc.date.available | 2016-11-28T13:43:38Z | - |
dc.date.issued | 2014 | - |
dc.identifier.citation | EuPA open proteomics, 4, p. 87-100 | - |
dc.identifier.issn | 2212-9685 | - |
dc.identifier.uri | http://hdl.handle.net/1942/22784 | - |
dc.description.abstract | Although physicochemical fractionation techniques play a crucial role in the analysis of complex mixtures, they are not necessarily the best solution to separate specific molecular classes, such as lipids and peptides. Any physical fractionation step such as, for example, those based on liquid chromatography, will introduce its own variation and noise. In this paper we investigate to what extent the high sensitivity and resolution of contemporary mass spectrometers offers viable opportunities for computational separation of signals in full scan spectra. We introduce an automatic method that can discriminate peptide from lipid peaks in full scan mass spectra, based on their isotopic properties. We systematically evaluate which features maximally contribute to a peptide versus lipid classification. The selected features are subsequently used to build a random forest classifier that enables almost perfect separation between lipid and peptide signals without requiring ion fragmentation and classical tandem MS-based identification approaches. The classifier is trained on in silico data, but is also capable of discriminating signals in real world experiments. We evaluate the influence of typical data inaccuracies of common classes of mass spectrometry instruments on the optimal set of discriminant features. Finally, the method is successfully extended towards the classification of individual lipid classes from full scan mass spectral features, based on input data defined by the Lipid Maps Consortium. | - |
dc.description.sponsorship | This research is supported in part by the Polish National Science Center grant 2011/01/B/NZ2/00864 and by the EU through the European Social Fund, contract number UDAPOKL. 04.01.01-00-072/09-00. A.G, D.V. and P.D. gratefully acknowledge the support of the bilateral FWO-PAS grant VS.005.13N/Innovative algorithms to detect protein modifications in mass spectrometry data. P.D. is supported by a START fellowship from the Foundation for Polish Science. K.L. and D.V. acknowledge the support of the SBO grant ‘InSPECtor’ (120025) of the Flemish agency for Innovation by Science and Technology (IWT). R.C., G.H., and R.V. acknowledge support by the National Institutes of Health via grants NIH/NIGMS R01 GM058008-14 and NIH/NIGMS P41 GM103391-03. | - |
dc.language.iso | en | - |
dc.rights | © 2014 The Authors. Published by Elsevier B.V. on behalf of European Proteomics Association (EuPA). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/3.0/). | - |
dc.subject.other | lipidomics; peptidomics; bioinformatics; machine learning; lipid/peptide classification; lipid centrifuge | - |
dc.title | Towards automated discrimination of lipids versus peptides from full scan mass spectra | - |
dc.type | Journal Contribution | - |
dc.identifier.epage | 100 | - |
dc.identifier.spage | 87 | - |
dc.identifier.volume | 4 | - |
local.bibliographicCitation.jcat | A1 | - |
local.type.refereed | Refereed | - |
local.type.specified | Article | - |
dc.identifier.doi | 10.1016/j.euprot.2014.05.002 | - |
item.fulltext | With Fulltext | - |
item.fullcitation | Dittwald, Piotr; Nghia, Vu Trung; Harris, Glenn A.; Caprioli, Richard .M.; Van de Plas, Raf; Laukens, Kris; Gambin, Anna & VALKENBORG, Dirk (2014) Towards automated discrimination of lipids versus peptides from full scan mass spectra. In: EuPA open proteomics, 4, p. 87-100. | - |
item.accessRights | Open Access | - |
item.contributor | Dittwald, Piotr | - |
item.contributor | Nghia, Vu Trung | - |
item.contributor | Harris, Glenn A. | - |
item.contributor | Caprioli, Richard .M. | - |
item.contributor | Van de Plas, Raf | - |
item.contributor | Laukens, Kris | - |
item.contributor | Gambin, Anna | - |
item.contributor | VALKENBORG, Dirk | - |
crisitem.journal.issn | 2212-9685 | - |
Appears in Collections: | Research publications |
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File | Description | Size | Format | |
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1-s2.0-S221296851400035X-main.pdf | Published version | 2.32 MB | Adobe PDF | View/Open |
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