Abstract
Since the dawn of the post-genomic era a myriad of novel high-throughput technologies have been developed that are capable of measuring thousands of biological molecules at once, giving rise to various “omics” platforms. These advances offer the unique opportunity to study how individual parts of a biological system work together to produce emerging phenotypes. Today, many research laboratories are moving toward applying multiple omics platforms to analyze the same biological samples. In addition, network information of interacting molecules is being incorporated more and more into the analysis and interpretation of these multiple omics datasets, which provides novel ways to integrate multiple layers of heterogeneous biological information into a single coherent picture. Here, we provide a perspective on how such recent “integrative omics” efforts are likely going to shift biological paradigms once again, and what challenges lie ahead.
Highlights
The first generation of whole-genome sequencing projects have inspired the development of technologies aimed at comprehensively characterizing various types of biological molecules, opening up entirely new fields such as genomics, transcriptomics, proteomics, metabolomics, and so forth
The systems-level information provided by each omics platform offers a unique insight into the complexity of a biological system and, as a consequence, scientific discoveries and their clinical applications have immensely benefited from omics data over the past decade (Van de Vijver et al, 2002; Van ’t Veer et al, 2002; Hanash et al, 2008; Stratton et al, 2009; 1000_Genomes_Project_Consortium, 2010; Hudson et al, 2010; Meyerson et al, 2010; Pang et al, 2010; Solit and Mellinghoff, 2010)
Microarrays were among the first omics platforms to be developed, and already since their first appearance it became clear that microarray data would have to be integrated with other levels of biological information in order to allow researchers to see the “big picture” (Kohane et al, 2002)
Summary
The first generation of whole-genome sequencing projects have inspired the development of technologies aimed at comprehensively characterizing various types of biological molecules, opening up entirely new fields such as genomics, transcriptomics, proteomics, metabolomics, and so forth. As experimental protocols evolve with declining costs, scientists are starting to apply multiple omics platforms to analyze the same biological samples (Ideker et al, 2001; Joyce and Palsson, 2006; Zhang et al, 2010) This type of studies will be critically useful for biologists since they can measure molecular changes at multiple levels simultaneously and get one step closer to understanding how biological systems work as a whole, which is one of the primary goals of “systems biology” (Kitano, 2002; Ge et al, 2003; Fukushima et al, 2009). The key to successful application will depend on properly designed experiments, statistically sound data analysis, and appropriate interpretation of the data In this Perspective, we review both challenges and opportunities encountered by systems biologists, bioinformaticians, and statisticians undertaking the exciting www.frontiersin.org
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