TL;DR: This review provides an overview of omics technologies and methods for their integration across multiple omics layers and offers the opportunity to understand the flow of information that underlies disease.
Abstract: High-throughput technologies have revolutionized medical research. The advent of genotyping arrays enabled large-scale genome-wide association studies and methods for examining global transcript levels, which gave rise to the field of "integrative genetics". Other omics technologies, such as proteomics and metabolomics, are now often incorporated into the everyday methodology of biological researchers. In this review, we provide an overview of such omics technologies and focus on methods for their integration across multiple omics layers. As compared to studies of a single omics type, multi-omics offers the opportunity to understand the flow of information that underlies disease.
TL;DR: This review cuts across the boundaries between genomics, transcriptomics and proteomics, summarizing how omics data are generated, analysed and shared and provides an overview of the current strengths and weaknesses of this global approach.
Abstract: Advances in the technologies and informatics used to generate and process large biological data sets (omics data) are promoting a critical shift in the study of biomedical sciences. While genomics, transcriptomics and proteinomics, coupled with bioinformatics and biostatistics, are gaining momentum, they are still, for the most part, assessed individually with distinct approaches generating monothematic rather than integrated knowledge. As other areas of biomedical sciences, including metabolomics, epigenomics and pharmacogenomics, are moving towards the omics scale, we are witnessing the rise of inter-disciplinary data integration strategies to support a better understanding of biological systems and eventually the development of successful precision medicine. This review cuts across the boundaries between genomics, transcriptomics and proteomics, summarizing how omics data are generated, analysed and shared, and provides an overview of the current strengths and weaknesses of this global approach. This work intends to target students and researchers seeking knowledge outside of their field of expertise and fosters a leap from the reductionist to the global-integrative analytical approach in research.
TL;DR: The use of metabolomics in integrated applications where metabolomics information has been combined with other "omic" data sets (proteomics, transcriptomics) to enable greater understanding of a biological system is reviewed.
Abstract: Metabolomics is the study of global metabolite profiles in a system (cell, tissue, or organism) under a given set of conditions. The analysis of the metabolome is particularly challenging due to the diverse chemical nature of metabolites. Metabolites are the result of the interaction of the system's genome with its environment and are not merely the end product of gene expression but also form part of the regulatory system in an integrated manner. Metabolomics has its roots in early metabolite profiling studies but is now a rapidly expanding area of scientific research in its own right. Metabolomics (or metabonomics) has been labeled one of the new "omics", joining genomics, transcriptomics, and proteomics as a science employed toward the understanding of global systems biology. Metabolomics is fast becoming one of the platform sciences of the "omics", with the majority of the papers in this field having been published only in the last two years. In this review metabolomic methodologies are discussed briefly followed by a more detailed review of the use of metabolomics in integrated applications where metabolomics information has been combined with other "omic" data sets (proteomics, transcriptomics) to enable greater understanding of a biological system. The potential of metabolomics for natural product drug discovery and functional food analysis, primarily as incorporated into broader "omic" data sets, is discussed.
TL;DR: This work reviews recent approaches to integrate multiple omics data in higher plants and especially focuses on metabolomics data management, normalization, meta-omics data analysis, and an integrative approach with other omic data.
TL;DR: One leading effort in the lipidomics evolution is described in this issue of PNAS, which aims to evolve an integrated omics picture of the genes, transcripts, proteins, and metabolites to fully describe cellular functioning.
Abstract: The end of the 20th century was marked by the genomics revolution, and one might say that the beginning of the 21st century is marked by efforts to bring our knowledge of a cell's proteins, known as proteomics, to a par with our growing knowledge of a cell's transcripts, known as transcriptomics. Many of us believe that the next evolution of the omics revolution will be to map all of the metabolites of a cell, known as metabolomics. Leading the way in these efforts is the work in many laboratories on one subset of the metabolome, the lipidome, aimed at mapping all of the lipids of a cell, known as lipidomics (1–4). The ultimate goal is to evolve an integrated omics picture (the “interactome”) of the genes, transcripts, proteins, and metabolites to fully describe cellular functioning. One leading effort in the lipidomics evolution is described in this issue of PNAS (5).