Analytical methods in untargeted metabolomics: state of the art in 2015

被引:463
作者
Alonso, Arnald [1 ,2 ]
Marsal, Sara [1 ]
Julia, Antonio [1 ]
机构
[1] Vall Hebron Res Inst, Rheumatol Res Grp, Baldiri & Reixac 15-21, Barcelona 08028, Spain
[2] Univ Politecn Cataluna, Dept Automat Control ESAII, Barcelona, Spain
来源
FRONTIERS IN BIOENGINEERING AND BIOTECHNOLOGY | 2015年 / 3卷
关键词
metabolomics; nuclear magnetic resonance; mass spectrometry; untargeted; spectral processing; data analysis; pathway analysis; integration; GENOME-WIDE ASSOCIATION; CHROMATOGRAPHY-MASS SPECTROMETRY; FALSE DISCOVERY RATE; WEB-BASED TOOL; NMR-SPECTRA; PEAK ALIGNMENT; R PACKAGE; METABOLITE IDENTIFICATION; LIQUID-CHROMATOGRAPHY; DISCRIMINANT-ANALYSIS;
D O I
10.3389/fbioe.2015.00023
中图分类号
Q81 [生物工程学(生物技术)]; Q93 [微生物学];
学科分类号
071005 ; 0836 ; 090102 ; 100705 ;
摘要
Metabolomics comprises the methods and techniques that are used to measure the small molecule composition of biofluids and tissues, and is actually one of the most rapidly evolving research fields. The determination of the metabolomic profile - the metabolome has multiple applications in many biological sciences, including the developing of new diagnostic tools in medicine. Recent technological advances in nuclear magnetic resonance and mass spectrometry are significantly improving our capacity to obtain more data from each biological sample. Consequently, there is a need for fast and accurate statistical and bioinformatic tools that can deal with the complexity and volume of the data generated in metabolomic studies. In this review, we provide an update of the most commonly used analytical methods in metabolomics, starting from raw data processing and ending with pathway analysis and biomarker identification. Finally, the integration of metabolomic profiles with molecular data from other high-throughput biotechnologies is also reviewed.
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页数:20
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共 220 条
[1]   Primary differentiation in the human blastocyst: Comparative molecular portraits of inner cell mass and trophectoderm cells [J].
Adjaye, J ;
Huntriss, J ;
Herwig, R ;
BenKahla, A ;
Brink, TC ;
Wierling, C ;
Hultschig, C ;
Groth, D ;
Yaspo, ML ;
Picton, HM ;
Gosden, RG ;
Lehrach, H .
STEM CELLS, 2005, 23 (10) :1514-1525
[2]   Graph-based methods for analysing networks in cell biology [J].
Aittokallio, Tero ;
Schwikowski, Benno .
BRIEFINGS IN BIOINFORMATICS, 2006, 7 (03) :243-255
[3]  
Akiyama Kenji, 2008, In Silico Biology, V8, P339
[4]   Multiple phenotype modeling in gene-mapping studies of quantitative traits: Power advantages [J].
Allison, DB ;
Thiel, B ;
St Jean, P ;
Elston, RC ;
Infante, MC ;
Schork, NJ .
AMERICAN JOURNAL OF HUMAN GENETICS, 1998, 63 (04) :1190-1201
[5]   Focus: A Robust Workflow for One-Dimensional NMR Spectral Analysis [J].
Alonso, Arnald ;
Rodriguez, Miguel A. ;
Vinaixa, Maria ;
Tortosa, Rauel ;
Correig, Xavier ;
Julia, Antonio ;
Marsal, Sara .
ANALYTICAL CHEMISTRY, 2014, 86 (02) :1160-1169
[6]   AStream: an R package for annotating LC/MS metabolomic data [J].
Alonso, Arnald ;
Julia, Antonio ;
Beltran, Antoni ;
Vinaixa, Maria ;
Diaz, Marta ;
Ibanez, Lourdes ;
Correig, Xavier ;
Marsal, Sara .
BIOINFORMATICS, 2011, 27 (09) :1339-1340
[7]   Gaussian binning: a new kernel-based method for processing NMR spectroscopic data for metabolomics [J].
Anderson, Paul E. ;
Reo, Nicholas V. ;
DelRaso, Nicholas J. ;
Doom, Travis E. ;
Raymer, Michael L. .
METABOLOMICS, 2008, 4 (03) :261-272
[8]   Dynamic adaptive binning: an improved quantification technique for NMR spectroscopic data [J].
Anderson, Paul E. ;
Mahle, Deirdre A. ;
Doom, Travis E. ;
Reo, Nicholas V. ;
DelRaso, Nicholas J. ;
Raymer, Michael L. .
METABOLOMICS, 2011, 7 (02) :179-190
[9]   Metabolomics in cancer biomarker discovery: Current trends and future perspectives [J].
Armitage, Emily G. ;
Barbas, Coral .
JOURNAL OF PHARMACEUTICAL AND BIOMEDICAL ANALYSIS, 2014, 87 :1-11
[10]   A Bayesian Model of NMR Spectra for the Deconvolution and Quantification of Metabolites in Complex Biological Mixtures [J].
Astle, William ;
De Iorio, Maria ;
Richardson, Sylvia ;
Stephens, David ;
Ebbels, Timothy .
JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION, 2012, 107 (500) :1259-1271