Trans-Omics: How To Reconstruct Biochemical Networks Across Multiple 'Omic' Layers

Katsuyuki Yugi, Hiroyuki Kubota, Atsushi Hatano, Shinya Kuroda
2016 Trends in Biotechnology  
24 We propose "trans-omic" analysis for reconstructing global biochemical networks 25 across multiple omic layers by use of both multi-omic measurements and computational 26 data integration. We introduce technologies for connecting multi-omic data based on 27 prior knowledge of biochemical interactions and characterize a biochemical trans-omic 28 network by concepts of a static and dynamic nature. We introduce case studies of 29 © 2015. This manuscript version is made available under the
more » ... er user license http://www.elsevier.com/open-access/userlicense/1.0/ 2 metabolism-centric trans-omic studies to show how to reconstruct a biochemical 1 trans-omic network by connecting multi-omic data and how to analyze it in terms of the 2 static and dynamic nature. We propose a trans-ome-wide association study 3 (trans-OWAS) connecting phenotypes with trans-omic networks that reflect both genetic 4 and environmental factors, which can characterize several complex lifestyle diseases as 5 breakdowns in the trans-omic system. 6 7 Trans-omic network across multiple omic layers 8 Specific "omic" layers can be defined and categorized according to the different basic 9 building blocks of the cell, e.g. DNA, RNA, protein, or metabolite [1, 2] (Figure 1). 10 Many cellular functions are orchestrated by global networks that cut across multiple 11 omic layers, and we define the collection of these networks here as the "trans-omic" 12 network (Figure 1). Most biological studies have been conducted by focusing on a few 13 specific molecules, and the trans-omic network has been built by accumulating literature 14 based on such small-scale analyses. This is a powerful strategy, but the 15 comprehensiveness of each layer is limited. Comprehensive measurement technologies 16 for each omic layer are now becoming available, such as polynucleotide sequencing by 17 next-generation sequencers (genome sequencing [3], RNA sequencing [4, 5], chromatin 18 3 immunoprecipitation sequencing [ChIP-seq] [6-8], etc.), mass spectrometry-based 1 phosphoproteomics [9-16], expression proteomics [17, 18] and metabolomics (gas 2 chromatography-mass spectrometry [GC-MS] [19], liquid chromatography-mass 3 spectrometry [LC-MS] [20, 21], capillary electrophoresis-mass spectrometry [CE-MS] 4 [22-24], supercritical fluid chromatography-mass spectrometry [SFC-MS] [25], and 5 nuclear magnetic resonance [NMR] [26, 27]). However, a single omic layer analysis 6 alone does not directly elucidate interaction across multiple omic layers. To overcome 7 the lack of comprehensiveness and the information gap regarding interaction across 8 multiple omic layers, an approach for reconstructing molecular networks by connecting 9 multiple omic data has been proposed [28-42] (Figure 1). Here, we call such an 10 approach "trans-omics." Trans-omics connects multiple omic data. There are two major 11 approaches in reconstructing a trans-omic network: one using prior knowledge of a 12 molecular network and another based only on the data-driven approach without use of 13 prior knowledge [43-46]. The former approach is reconstruction of biochemical 14 networks by connecting multiple omic layers with the support of prior knowledge of 15 molecular networks such as publicly available databases. A reconstructed biochemical 16 trans-omic network inherently provides causality and an input-output relationship at a 17 molecular level, allowing interpretation of the biochemical networks. The biochemical 18
doi:10.1016/j.tibtech.2015.12.013 pmid:26806111 fatcat:4vfmjxezyzaoloh6n25cgkdchu