Exploiting Interdata Relationships in Next-generation Proteomics Analysis

Abstract

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.

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

Graphical abstract

Fig. 1.

Moving from multiomics studies that acquire and analyze data sets in parallel to modeling and exploiting the interactions between data.

Fig. 2.

Steps toward productive integromics.