High-Throughput Profiling of Metabolic Phenotypes Using High-Resolution GC-MS
Methods in Molecular Biology, ISSN: 1940-6029, Vol: 2539, Page: 235-260
2022
- 7Citations
- 9Captures
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
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Metrics Details
- Citations7
- Citation Indexes7
- Captures9
- Readers9
Book Chapter Description
Metabolite profiling provides insights into the metabolic signatures, which themselves are considered as phonotypes closely related to the agronomic and phenotypic traits such as yield, nutritional values, stress resistance, and nutrient use efficiency. GC-MS is a sensitive and high-throughput analytical platform and has been proved to be a vital tool for the analysis of primary metabolism to provide an overview of cellular and organismal metabolic status. The potential of GC-MS metabolite profiling as a tool for detecting metabolic changes in plants grown in a high-throughput plant phenotyping platform was explored. In this chapter, we describe an integrated workflow of semi-targeted GC-high-resolution (HR)-time-of-flight (TOF)-MS metabolomics with both the analytical and computational steps, focusing mainly on the sample preparation, GC-HR-TOF-MS analysis part, and data analysis for plant phenotyping efforts.
Bibliographic Details
http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85135377663&origin=inward; http://dx.doi.org/10.1007/978-1-0716-2537-8_19; http://www.ncbi.nlm.nih.gov/pubmed/35895208; https://link.springer.com/10.1007/978-1-0716-2537-8_19; https://dx.doi.org/10.1007/978-1-0716-2537-8_19; https://link.springer.com/protocol/10.1007/978-1-0716-2537-8_19
Springer Science and Business Media LLC
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