DICP OpenIR
Retention Time Prediction Improves Identification in Nontargeted Lipidomics Approaches
Aicheler, Fabian1,2; Li, Jia3; Hoene, Miriam4; Lehmann, Rainer4,5,6; Xu, Guowang3; Kohlbacher, Oliver1,2,5,6
Source PublicationANALYTICAL CHEMISTRY
2015-08-04
DOI10.1021/acs.analchem.5b01139
Volume87Issue:15Pages:7698-7704
Indexed BySCI
SubtypeArticle
WOS HeadingsScience & Technology ; Physical Sciences
WOS SubjectChemistry, Analytical
WOS Research AreaChemistry
WOS KeywordSUPPORT VECTOR REGRESSION ; 2-DIMENSIONAL GAS-CHROMATOGRAPHY ; FLIGHT MASS-SPECTROMETRY ; BIOLOGICAL SAMPLES ; PLASMA LIPIDOMICS ; FATTY-ACIDS ; GC-MS ; DATABASE ; PROTEOMICS ; SYSTEM
AbstractIdentification of lipids in nontargeted lipidomics based on liquid-chromatography coupled to mass spectrometry (LC-MS) is still a major issue. While both accurate mass and fragment spectra contain valuable information, retention time (t(R)) information can be used to augment this data. We present a retention time model based on machine learning approaches which enables an improved assignment of lipid structures and automated annotation of lipidomics data. In contrast to common approaches we used a complex mixture of 201 lipids originating from fat tissue instead of a standard mixture to train a support vector regression (SVR) model including molecular structural features. The cross-validated model achieves a correlation coefficient between predicted and experimental test sample retention times of r = 0.989. Combining our retention time model with identification via accurate mass search (AMS) of lipids against the comprehensive LIPID MAPS database, retention time filtering can significantly reduce the rate of false positives in complex data sets like adipose tissue extracts. In our case, filtering with retention time information removed more than half of the potential identifications, while retaining 95% of the correct identifications. Combination of high-precision retention time prediction and accurate mass can thus significantly narrow down the number of hypotheses to be assessed for lipid identification in complex lipid pattern like tissue profiles.
Language英语
WOS IDWOS:000359277900031
Citation statistics
Document Type期刊论文
Identifierhttp://cas-ir.dicp.ac.cn/handle/321008/146465
Collection中国科学院大连化学物理研究所
Affiliation1.Univ Tubingen, Quantitat Biol Ctr, Appl Bioinformat Ctr Bioinformat, D-72076 Tubingen, Baden Wurttembe, Germany
2.Univ Tubingen, Dept Comp Sci, D-72076 Tubingen, Baden Wurttembe, Germany
3.Chinese Acad Sci, Dalian Inst Chem Phys, Key Lab Separat Sci Analyt Chem, Dalian 116023, Liaoning, Peoples R China
4.Univ Tubingen Hosp, Dept Internal Med 4, Div Clin Chem & Pathobiochem, D-72076 Tubingen, Baden Wurttembe, Germany
5.Univ Tubingen, Helmholtz Ctr Munich, Inst Diabet Res & Metab Dis, Dept Mol Diabetol, D-72076 Tubingen, Baden Wurttembe, Germany
6.German Ctr Diabet Res DZD, D-72076 Tubingen, Baden Wurttembe, Germany
Recommended Citation
GB/T 7714
Aicheler, Fabian,Li, Jia,Hoene, Miriam,et al. Retention Time Prediction Improves Identification in Nontargeted Lipidomics Approaches[J]. ANALYTICAL CHEMISTRY,2015,87(15):7698-7704.
APA Aicheler, Fabian,Li, Jia,Hoene, Miriam,Lehmann, Rainer,Xu, Guowang,&Kohlbacher, Oliver.(2015).Retention Time Prediction Improves Identification in Nontargeted Lipidomics Approaches.ANALYTICAL CHEMISTRY,87(15),7698-7704.
MLA Aicheler, Fabian,et al."Retention Time Prediction Improves Identification in Nontargeted Lipidomics Approaches".ANALYTICAL CHEMISTRY 87.15(2015):7698-7704.
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