title: Bioinformatic primer for clinical and translational science.
abstract: The advent of high-throughput technologies has accelerated generation and expansion of genomic, transcriptomic, and proteomic data. Acquisition of high-dimensional datasets requires archival systems that permit efficiency of storage and retrieval, and so, multiple electronic repositories have been initiated and maintained to meet this demand. Bioinformatic science has evolved, from these intricate bodies of dynamically updated information and the tools to manage them, as a necessity to harness and decipher the inherent complexity of high-volume data. Large datasets are associated with a variable degree of stochastic noise that contributes to the balance of an ordered, multistable state with the capacity to evolve in response to stimulus, thus exhibiting a hallmark feature of biological criticality. In this context, the network theory has become an invaluable tool to map relationships that integrate discrete elements that collectively direct global function within a particular -omic category, and indeed, the prioritized focus on the functional whole of the genomic, transcriptomic, or proteomic strata over single molecules is a primary tenet of systems biology analyses. This new biology perspective allows inspection and prediction of disease conditions, not limited to a monogenic challenge, but as a combination of individualized molecular permutations acting in concert to effect a phenotypic outcome. Bioinformatic integration of multidimensional data within and between biological layers thus harbors the potential to identify unique biological signatures, providing an enabling platform for advances in clinical and translational science.
authors.affiliation: Department of Medicine, Mayo Clinic, Rochester, Minnesota, USA.
authors.forename: Randolph S
authors.initials: RS
authors.lastname: Faustino
authors.forename: Anca
authors.initials: A
authors.lastname: Chiriac
authors.forename: Andre
authors.initials: A
authors.lastname: Terzic
mesh_terms: D016000:Cluster Analysis; D019295:Computational Biology / Q000379:methods*; D003201:Computers; D030541:Databases, Genetic; D001330:Electronic Data Processing; D057166:High-Throughput Screening Assays; D010363:Pattern Recognition, Automated; D057170:Translational Research, Biomedical
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