Xia, J., and Wishart, D. S. (2010a). MetPA: a web-based metabolomics tool for pathway analysis and visualization. Bioinformatics 26, 2342-2344. doi: 10.1093/ bioinformatics/btq418
Xia,)., and Wishart, D. S. (201 Ob). MSEA: a web-based tool to identify biologically meaningfnl pattems in quantitative metabolomic data. Nudeic Acids Res. 38, W71-W77. doi: 10.1093/nar/gkq329
Xiao, C., Hao, F., Qin, X., Wang, Y., and Tang, H. (2009). An optimized buffer sys-tcm tor NMR-based urinary metabolomics with effective pH control, Chemical shift consistencyand dilution minimization. Analyst 134,916-925. doi: 10.1039/ b818802e
Xie, Y., Pan, W.> and Khodursky, A. B. (2005). A notę on using permutation-based false discovery ratę estimates to compare different analysis methods for microarray data. Bioinformatics 21,4280-4288. doi.10.1093/bioinformatics/ bti685
Yang, C., He, Z., and Yu, W. (2009). Comparison of public peak detection algo-rithms for MALDI mass spectrometry data analysis. BMC Bioinformatics 10:4. doi: 10.1186/1471-2105-10-4
Yin, P., Peter, A., Franken, H., Zhao, X., Neukamm, S. S., Rosenbaum, L., etal. (2013). Preanalytical aspects and sample quality assessment in metabolomics studies of human blood. Clitu Chem. 59, 833-845. doi:10.1373/clinchem.2012. 199257
Zhang, A., Sun, H., Wang, P., Han, Y., and Wang, X. (2012). Modern analyti-cal techniques in metabolomics analysis. Analyst 137, 293-300. doi: 10.1039/ clanl5605e
Zhang, G., He, P., Tan, H., Budhu, A., Gaedcke, I., Ghadimi, B. M., etal. (2013). Integration of metabolomics and transcriptomics revealed a fatty acid network exerting growth inhibitory effects in human pancreatic cancer. Clin. Cancer Res. 19,4983-4993. doi: 10.1158/1078-0432.CCR-13-0209
Zhang, Z.-M., Chen, S., and Liang, Y.-Z. (2010). Baseline correction using adap-tive iteratively reweighted penalized least squares. Analyst 135, 1138-1146. doi: 10.1039/b922045c
Zheng, C., Zhang, S.. Ragg, S.. Raftery, D., and Vitek, O. (2011). Identification and quantification of metabolites in 1H NMR spectra by Bayesian model selection. Bioinformatics 27,1637-1644. doi:10.I093/bioinformatics/btrl 18 Zhou, B.,Xiao, I. F.,Tuli, L.,and Ressom, H. W. (2012). LC-MS-based metabolomics.
Mol Biosyst. 8,470-4S1. doi: 10.1039/c 1 mb05350g Zhu, W., and Zhang, H. (2009). Rejoinder: why do we test multiple traits in genetic association studies? J. Korean Stat. Soc. 38,25-27. doi:l0.1016/j.jkss.2008.10.007
Conflict of lnterest Statement: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interesL
Received: 15 December 2014; accepted: 18 February 2015; pubłished onlitte: 05 March 2015.
Citation: Alonso A, Marsal S and Jtdid A (2015) AnalyticaJ methods in untar-geted metabolomics: State of the art in 2015. Front. Bioeng. Biotechnol. 3:23. doi: 10.3389/(bioe.2015.00023
This article was submitted to Bioinformatics and Computational Biology, a section of the Journal Frontiers in Bioengineering and Biotechnolog)'.
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