A Review of Recent Advances in Translational Bioinformatics: Bridges from Biology to Medicine

Abstract

SummaryObjectives: To highlight and provide insights into key developments in translational bioinformatics between 2014 and 2016. Methods: This review describes some of the most influential bioinformatics papers and resources that have been published between 2014 and 2016 as well as the national genome sequencing initiatives that utilize these resources to routinely embed genomic medicine into healthcare. Also discussed are some applications of the secondary use of patient data followed by a comprehensive view of the open challenges and emergent technologies. Results: Although data generation can be performed routinely, analyses and data integration methods still require active research and standardization to improve streamlining of clinical interpretation. The secondary use of patient data has resulted in the development of novel algorithms and has enabled a refined understanding of cellular and phenotypic mechanisms. New data storage and data sharing approaches are required to enable diverse biomedical communities to contribute to genomic discovery. Conclusion: The translation of genomics data into actionable knowledge for use in healthcare is transforming the clinical landscape in an unprecedented way. Exciting and innovative models that bridge the gap between clinical and academic research are set to open up the field of translational bioinformatics for rapid growth in a digital era.

GM604 regulates developmental neurogenesis pathways and the expression of genes associated with amyotrophic lateral sclerosis

“…At present, however, exact mechanisms of action (MOA) for GM6 have not been determined. To develop hypotheses regarding the MOA of investigational drugs such as GM6, transcriptome profiling combined with bioinformatic analysis offers an increasingly powerful approach that can provide a global and objective view of a drug’s cellular effects [ 21 – 23 ]. This approach is especially well-suited to multi-target drugs not developed to specifically interact with one receptor, which may instead interact with multiple receptors with involvement of multiple signaling pathways [ 20 ].…”

“…This approach is especially well-suited to multi-target drugs not developed to specifically interact with one receptor, which may instead interact with multiple receptors with involvement of multiple signaling pathways [ 20 ]. To understand the MOA for such drug products, transcriptome profiling provides a valuable tool that can then be used to guide hypothesis-driven studies into one or more drug mechanisms [ 21 – 23 ].…”

In Silico Genetics Revealing 5 Mutations in CEBPA Gene Associated With Acute Myeloid Leukemia

“…For these reasons, bioinformatics analysis is an appropriate, rapid, low-cost, and dependable approach to enhance our understanding of how mutations could disturb the protein structure and function. 53,54 Disease-causing SNPs are commonly found to arise at evolutionarily conserved regions. Those have a key role at structural and functional levels of the protein 36,38 ; therefore, our focus was dedicated to the coding region, which unmasked 5 mutations in CEBPA gene using different sequence and structure-based algorithms (Figure 2).…”

PlatformTM, a standards-based data custodianship platform for translational medicine research

“…Various stages of the data life cycle in TM research, such as discovering, reusing, sharing, and analysing data are entirely dependent on the use of metadata and data standards. Several recent reviews suggest that a major challenge in translational bioinformatics is the lack of adoption of such standards that is often due to barriers in understanding, navigating and using these standards 11–13 . This paper begins by describing a data lifecycle management approach to develop a standard-compliant metadata management framework for translational medicine research.…”