Translational Bioinformatics: Past, Present, and Future

Abstract

Though a relatively young discipline, translational bioinformatics (TBI) has become a key component of biomedical research in the era of precision medicine. Development of high-throughput technologies and electronic health records has caused a paradigm shift in both healthcare and biomedical research. Novel tools and methods are required to convert increasingly voluminous datasets into information and actionable knowledge. This review provides a definition and contextualization of the term TBI, describes the discipline's brief history and past accomplishments, as well as current foci, and concludes with predictions of future directions in the field.

Keywords: Biomarkers; Genomics; Personalized medicine; Precision medicine; Translational bioinformatics.

Figure 1

Translational Bioinformatics in context The Y axis depicts the “central dogma” of informatics, converting data to information and information to knowledge. Along the X axis is the translational spectrum from bench to bedside. Translational bioinformatics spans the data to knowledge spectrum, and bridges the gap between bench research and application to human health. The figure was reproduced from with permission from Springer.

Figure 2

A PheWAS Manhattan plot for a given SNP This plot shows the significance of association between SNP rs965513 and 866 different phenotypes. Along the X axis different disease groups are shown in different colors. This is in contrast to an analogous plot for GWAS in which the X axis would represent the different chromosomes. The Y axis reflects the P value for each phenotype. Blue and red horizontal lines represent P value of 0.05 and Bonferroni corrected P value of 5.8 × 10⁻⁵, respectively. PheWAS, phenome-wide association studies; GWAS, genome-wide association studies. The figure was reproduced from with permission from Elsevier.

Figure 3

Heterogeneous and non-traditional sources of big data Technological advances have enabled the collection and storage of big data beyond biomedicine, including everything from credit card transactions to security cameras to weather. Notably absent from this 2012 figure are smart watches and fitness tracking devices, which became pervasive in the years that followed. The figure was reproduced from under Creative Commons Attribution license.