Improvement of the quantitative differential metabolome pipeline for gas chromatography-mass spectrometry data by automated reliable peak selection

Abstract

Recent advances in metabolomics technology have enabled large-scale comprehensive analyses of metabolites, but the throughput of data processing of non-targeted, quantitative differential analyses is very low. It is crucial to solve this problem to generate biological hypotheses from a large-scale dataset. To improve the analysis of metabolite data, we focused on the processing of quantitative differential analysis after multiple peak alignment. We have developed a program named FAQuant that automatically selects reliable peaks from each chromatogram, quantifies the mean of peak intensity to compare between sample groups, and selects the peaks with differences in accumulation of metabolites. This program was incorporated into a quantitative differential metabolome pipeline as a module to improve the throughput of gas chromatography-mass spectrometry dataset analysis. As a result, the module incorporation largely reduced the total processing time. Furthermore, differential analysis of metabolites in soybean (Glycine max) cultivars was demonstrated by use of the system. This system might facilitate biological hypothesis generation from large-scale comparative metabolome analysis.Key words: Differential analysis, GC-TOF-MS, mass spectrometry, metabolomics, quantification.Plant Biotechnology 26, 445-449 (2009) Bioinformatics NoteAbbreviations: GC-TOF-MS, gas chromatography-time-of-flight-mass spectrometry; MST, mass spectral tag. This article can be found at

Data Mining Methods for Omics and Knowledge of Crude Medicinal Plants Toward Big Data Biology

“…In 2009, metabolome studies were extended to diverged species such as crops and medicinal plants [53, 60, 61, 67, 68, 73, 74, 78] and to engineering studies such as quality assessment based on metabolomics [73, 74]. Thus metabolomics was applied from model species to crops and medicinal herbs.…”

“…[ 123 ] , Citrus sinensis [ 131 ], Curcuma longa [ 77 ], Ephedra sp. [ 67 ], Escherichia coli [ 51 ], Fragaria x ananassa [ 40 , 43 , 44 ], Fragaria vesca [ 105 ], Glycine max [ 53 ], Glycyrrhiza uralensis [ 94 ] , Hordeum vulgare [ 80 , 102 ], Homo sapiens [ 63 , 101 ], Jatropha curcas [ 124 , 125 ], Malx x domestica [ 126 ], Ophiorrhiza pumila [ 117 ], Oryza sativa [ 49 , 61 ], Papaver somniferum [ 42 ], Rattus norvegicus [ 39 , 97 ], Rizotania solani [ 79 ] , Solanum lycopersicum [ 45 , 48 ], Solanum tuberosum [ 98 ] and Zea mays [ 120 ].…”

“…In the period of 2006-2008, many review papers [‘Review’ in Table 1 ] focused on metabolomics platforms integrated by mass-spectrometry and metabolite databases including KNApSAcK Core [ 29 , 31 , 34 , 38 , 42 , 49 , 52 ] and on linking chemistry with biology [ 24 ], and on metabolome researches targeting the model plant Arabidopsis thaliana [ 30 , 33 , 35 , 37 ]. In 2009, metabolome studies were extended to diverged species such as crops and medicinal plants [ 53 , 60 , 61 , 67 , 68 , 73 , 74 , 78 ] and to engineering studies such as quality assessment based on metabolomics [ 73 , 74 ]. Thus metabolomics was applied from model species to crops and medicinal herbs.…”