Innovation: Metabolomics: the apogee of the omics trilogy

By G. Patti, Ó. Yanes, Gary Siuzdak. Nature reviews. Molecular cell biology, 2012

2,277 citations recorded by the source corpus.

Subfields: Molecular Biology, Spectroscopy

View Paper: https://www.semanticscholar.org/paper/24d5a07e52b1ef5766869b7ab53dc56f63d02df7

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Citations in the corpus

Searching molecular structure databases with tandem mass spectra using CSI:FingerID (2015, background): “Untargeted metabolomic studies may use NMR or MS technologies, but liquid chromatography followed by MS (LC/MS) can detect the highest number of metabolites from minimal amounts of sample (1, 2).”
Metabolomics and Metabolic Diseases: Where do we stand? (2016, background): “Today, the most advanced systems deployed in a nontargeted mode (sometimes referred to as ‘‘shotgun’’ metabolomics) are able to detect up to 10,000 independent spectral features in a single biological specimen (Zamboni et al., 2015; Patti et al., 2012; Jin et al., 2016).”
The ConsensusPathDB interaction database: 2013 update (2012, background): “With the possibility to measure the cellular concentrations of a panel of metabolites provided by state-of-the-art technologies, metabolite signatures for more and more phenotypes are being generated (31).”
Systems genetics approaches to understand complex traits (2013, methodology): “The large-scale analysis of metabolites (that is, metabolomics) using techniques such as mass spectrometry or NMR is a developing field that has already provided important insights into diseases and metabolic processes 44 .”
Computational mass spectrometry for small molecules (2013, background): “For a general introduction to metabolomics and metabolomic profiling see [2,3,38]; for recent work in the field see [39].”
MetaboAnalyst 4.0: towards more transparent and integrative metabolomics analysis (2018, background): “This can significantly improve the precision in identifying true patterns within the data (36–38).”
K-Profiles: A Nonlinear Clustering Method for Pattern Detection in High Dimensional Data (2015, background): “In recent years, large amounts of high dimensional data have been generated from high-throughput expression techniques, such as gene expression data using microarray or deep sequencing [1], and metabolomics and proteomics data using liquid chromatography-mass spectrometry (LC-MS) [2].”
Consensus guidelines for the use and interpretation of angiogenesis assays (2018, background): “1 3 products and is currently enjoying a revival (“Metabolomics, the apogee of the omics trilogy” [167]).”
Integrative Analysis of Multi-omics Data for Discovery and Functional Studies of Complex Human Diseases (2016, methodology): “Recent technology of MS profiling involves the use of liquid chromatography (LC) to separate analytes and high-resolution MS to accurately measure mass and abundance (Jones et al., 2012; Patti et al., 2012).”