iMet-Q: A User-Friendly Tool for Label-Free Metabolomics Quantitation Using Dynamic Peak-Width Determination

• Published in: PloS one Vol. 11; no. 1; p. e0146112
• Main Authors: Chang, Hui-Yin, Chen, Ching-Tai, Lih, T Mamie, Lynn, Ke-Shiuan, Juo, Chiun-Gung, Hsu, Wen-Lian, Sung, Ting-Yi
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 19.01.2016; Public Library of Science (PLoS)
• Subjects: Abundance; Algorithms; Analytical chemistry; Arabidopsis; Arabidopsis - metabolism; Bioinformatics; Chromatography; Comics; Computational biology; Data processing; Datasets; Downloading; Humans; Identification; Informatics; Information science; Interfaces; Isotope ratios; Mass spectrometry; Metabolites; Metabolome; Metabolomics; Metabolomics - methods; Performance evaluation; Programming languages; Proteomics; Quantitation; Retention; Science; Scientific imaging; Software; Statistical analysis; Taiwan; Cleveland Ohio
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0146112

Efficient and accurate quantitation of metabolites from LC-MS data has become an important topic. Here we present an automated tool, called iMet-Q (intelligent Metabolomic Quantitation), for label-free metabolomics quantitation from high-throughput MS1 data. By performing peak detection and peak alignment, iMet-Q provides a summary of quantitation results and reports ion abundance at both replicate level and sample level. Furthermore, it gives the charge states and isotope ratios of detected metabolite peaks to facilitate metabolite identification. An in-house standard mixture and a public Arabidopsis metabolome data set were analyzed by iMet-Q. Three public quantitation tools, including XCMS, MetAlign, and MZmine 2, were used for performance comparison. From the mixture data set, seven standard metabolites were detected by the four quantitation tools, for which iMet-Q had a smaller quantitation error of 12% in both profile and centroid data sets. Our tool also correctly determined the charge states of seven standard metabolites. By searching the mass values for those standard metabolites against Human Metabolome Database, we obtained a total of 183 metabolite candidates. With the isotope ratios calculated by iMet-Q, 49% (89 out of 183) metabolite candidates were filtered out. From the public Arabidopsis data set reported with two internal standards and 167 elucidated metabolites, iMet-Q detected all of the peaks corresponding to the internal standards and 167 metabolites. Meanwhile, our tool had small abundance variation (≤ 0.19) when quantifying the two internal standards and had higher abundance correlation (≥ 0.92) when quantifying the 167 metabolites. iMet-Q provides user-friendly interfaces and is publicly available for download at http://ms.iis.sinica.edu.tw/comics/Software_iMet-Q.html.