Abstract
Following the publication of the complete human genomic sequence, the post-genomic era is driven by the need to extract useful information from genomic data. Genomics, transcriptomics, proteomics, metabolomics, epidemiological data and microbial data provide different angles to our understanding of gene-environment interactions and the determinants of disease and health. Our goal and our challenge are to integrate these very different types of data and perspectives of disease into a global model suitable for dissecting the mechanisms of disease and for predicting novel therapeutic strategies. This review aims to highlight the need for and problems with complex data integration, and proposes a framework for data integration. While there are many obstacles to overcome, biological models based upon multiple datasets will probably become the basis that drives future biomedical research.
Highlights
In 1990, the human genome project was established to sequence the human genome [1], with the aim of applying the acquired genomic data to improve disease diagnosis and determine genetic susceptibility [2]
Following the publication of the complete human genomic sequence, the post-genomic era is driven by the need to extract useful information from genomic data
Over the last few years, biomolecular research has progressed from the completion of the human genome project to functional genomics and the application of this knowledge to advance our understanding of health and disease
Summary
Over the last few years, biomolecular research has progressed from the completion of the human genome project to functional genomics and the application of this knowledge to advance our understanding of health and disease. A new breed of scientist with a working knowledge of different post-genomic approaches, disease pathophysiology and mathematical modeling will be needed during the initial attempts at data integration. Experimental design and subject selections (such as appropriate controls) will need to be tailored to utilize the strengths of each profiling platform and optimize the final dataset for modeling. This needs to be followed by appropriate model interpretation that takes into account all the assumptions and limitations of the experimental and modeling processes. GWA, genome-wide association; HLA, human leukocyte antigen; QTL, quantitative trait loci; RCT, randomized controlled trial; SNP, single nucleotide polymorphism
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