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Mol Cell Proteomics. 2019 Aug 9;18(8 suppl 1):S5-S14. doi: 10.1074/mcp.MR118.001246. Epub 2019 May 24.

Exploiting Interdata Relationships in Next-generation Proteomics Analysis.

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‡Center for Genomics and Systems Biology, Department of Biology, New York University, New York, NY.
§Department of Medicine, Yong Loo Lin School of Medicine, National University Singapore, Singapore.
¶Institute of Molecular and Cell Biology, Agency for Science, Technology, and Research, Singapore.
‡Center for Genomics and Systems Biology, Department of Biology, New York University, New York, NY


Mass spectrometry based proteomics and other technologies have matured to enable routine quantitative, system-wide analysis of concentrations, modifications, and interactions of proteins, mRNAs, and other molecules. These studies have allowed us to move toward a new field concerned with mining information from the combination of these orthogonal data sets, perhaps called "integromics." We highlight examples of recent studies and tools that aim at relating proteomic information to mRNAs, genetic associations, and changes in small molecules and lipids. We argue that productive data integration differs from parallel acquisition and interpretation and should move toward quantitative modeling of the relationships between the data. These relationships might be expressed by temporal information retrieved from time series experiments, rate equations to model synthesis and degradation, or networks of causal, evolutionary, physical, and other interactions. We outline steps and considerations toward such integromic studies to exploit the synergy between data sets.


Bioinformatics; Computational Biology; Degradomics*; Metabolomics; Modeling; Post-translational modifications*; RNA SEQ; Systems biology*; Transcription*; Translation*; integration; multiomics; systems biology

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