TY - JOUR
T1 - Exploiting interdata relationships in next-generation proteomics analysis
AU - Vitrinel, Burcu
AU - Koh, Hiromi W.L.
AU - Kar, Funda Mujgan
AU - Maity, Shuvadeep
AU - Rendleman, Justin
AU - Choi, Hyungwon
AU - Vogel, Christine
N1 - Funding Information:
From the ‡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 Author’s Choice—Final version open access under the terms of the Creative Commons CC-BY license. Received November 28, 2018, and in revised form, May 1, 2019 Published, MCP Papers in Press, May 24, 2019, DOI 10.1074/mcp.MR118.001246
Funding Information:
* The work was supported by the NIH/NIGMS grant 1R35GM127089-01 (to C.V.) and Singapore Ministry of Education grant MOE2016-T2-1-001 (to H.C.). B.V. acknowledges funding by American Heart Association grant 18PRE33990254. ** These authors contributed equally to this work. ‖ To whom correspondence should be addressed. E-mail: cvogel@ nyu.edu.
Publisher Copyright:
© 2019 Vitrinel et al.
PY - 2019
Y1 - 2019
N2 - 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.
AB - 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.
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U2 - 10.1074/mcp.MR118.001246
DO - 10.1074/mcp.MR118.001246
M3 - Article
C2 - 31126983
AN - SCOPUS:85071348650
VL - 18
SP - S5-S14
JO - Molecular and Cellular Proteomics
JF - Molecular and Cellular Proteomics
SN - 1535-9476
IS - 8
ER -