Abstract
Liquid chromatography-tandem mass spectrometry (LC-MS/MS) is highly sensitive, selective, and enables extensive detection of metabolites within a sample. The result allows us to characterize comprehensive metabolite accumulation patterns without dependence on authentic standard compounds and isolation of the individual metabolites. A reference database search is essential for the structural assignment process of un-targeted MS and MS/MS data. Moreover, the characterization of unknown metabolites is challenging, since these cannot be assigned a candidate structure by using a reference database. In this case study, integrated LC-MS/MS based plant metabolomics allows us to detect several hundred metabolites in a sample; and integrated omics analyses, e.g., large-scale reverse genetics, linkage mapping, and association mapping, provides a powerful tool for candidate structure selection or rejection. We also examine emerging technology and applications for LC-MS/MS-based un-targeted plant metabolomics. These activities promote the characterization of massive extended detectable metabolites.
Highlights
Liquid chromatography-tandem mass spectrometry (LC- integrated with other omics approaches based on metabolic pathwaysMS/MS) based un-targeted metabolomics is a challenging activity in [21-42], but the connections between metabolites and gene functions the characterization of detectable metabolites
The description of have too large gaps for interpreting the un-targeted data [43]. This is metabolite accumulation patterns is desirable in many fields of because metabolite levels in an organism are dynamically changed by research; e.g. fuel, lead chemicals for pharmaceuticals, safety the effects of multiple levels
We focus on recent advances in LC-MS/MS
Summary
MS/MS) based un-targeted metabolomics is a challenging activity in [21-42], but the connections between metabolites and gene functions the characterization of detectable metabolites. Gene annotations in whole based un-targeted metabolomics by characterizing detectable genome sequence are assigned to representative biosynthesis enzyme metabolites and by multi-type MS integration for practical classes (hydroxylation, methylation, glycosylation, acetylation, etc.), quantification and qualification of metabolites in a few hundred and these modification reactions generate a huge number of bioresources. MS-based un-targeted detection of metabolites is an innovative methodology that does not depend on the following classical identification process: prepared biological samples, acquisition of extracted samples, data alignment, and generation of a data matrix (samples versus metabolites), multivariate analysis, and characterization of significantly changed metabolite structure.
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