Translational Bioinformatics to Accelerate the Life Sciences Research

Bioinformatics is an interdisciplinary science which develops algorithms and software tools for analyzing and understanding large amounts of biological information. It is a highly interdisciplinary field combining diverse types of specialists, including biologist, molecular life scientists, computer scientists, statisticians, and mathematicians.

 

Figure source: https://www.applicationnexus.com/bioinformatics/

The advancement of high-throughput technologies such as DNA and RNA sequencing, DNA microarrays, and high-throughput proteomics and metabolomics, raises the need for novel methods to converse these huge data into new information and knowledge. According to those issues, translational bioinformatics (TBI) emerges with aims to develop storage, analytic and interpretive methods for optimizing the transformation of increasingly voluminous biomedical and -omics data, into proactive, preventive, and participatory health.

The American Medical Informatics Association (AMIA) recently added translational bioinformatics as one of its three major domains of informatics. The AMIA has defined translational bioinformatics as:

“… the development of storage, analytic, and interpretive methods to optimize the transformation of increasingly voluminous biomedical data into proactive, predictive, preventative, and participatory health. Translational bioinformatics includes research on the development of novel techniques for the integration of biological and clinical data and the evolution of clinical informatics methodology to encompass biological observations. The end product of translational bioinformatics is newly found knowledge from these integrative efforts that can be disseminated to a variety of stakeholders, including biomedical scientists, clinicians, and patients.”

Deny (2014) categorized recent themes in the field of TBI into four major categorizations:

1. Clinical “big data”, or the use of electronic health record (EHR) data for discovery (genomic and otherwise)
2. Genomics and pharmacogenomics in routine clinical care
3. Omics for drug discovery and repurposing
4. Personal genomic testing, including a number of ethical, legal, and social issues that arise from such services.

Big Data and Biomedicine

As technology enables us to take an increasingly comprehensive look across the genome, transcriptome, proteome, metabolome, etc., the resulting datasets are increasingly high-dimensional. This in turn requires a larger number of samples in order to achieve the statistical power needed to detect the true signal.

The past decade or so has seen an increasing number of large-scale bio-repositories intended for clinical and translational research all over the world. These projects comprise both information and biospecimens from individual patients, enabling researchers to reclassify diseases based on underlying molecular pathways, instead of the macroscopic symptoms that have been relied on for centuries in defining disease. 

In order to accrue the numbers of samples required for the “big data” discipline that biomedical research is becoming, the ability to use patient data and samples in research would be of significant benefit. One major point addressed in the NPRM (http://www.hhs.gov/ohrp/humansubjects/regulations/nprmhome.html) is the ability for patients to give broad consent for future use of data and samples, without knowing the specifics of research studies ahead of time.

As we move toward the learning healthcare system (LHS) model in which every encounter is an additional data point, explicit research registries will become less relevant. They will be too expensive to maintain, and larger numbers of patients/participants will be available through federated initiatives that allow a researcher to query across institutions regionally, nationally, and even internationally.

The National Patient-Centered Clinical Research Network (PCORnet) takes this approach, enabling clinical outcome research through federated pragmatic clinical trials. Importantly, this initiative emphasizes partnership with patients and their advocates, so that they are empowered as collaborators, with a say in what research questions matter most.

Opinion and comments:

In my opinion, bioinformatic is an interesting science that is greatly useful especially for biologists and medical scientists. The rapid development of bioinformatic algorithms is extremely needed in the era of high-throughput technologies. However, the validity and reproducibility of numerous bio-software available in the market is still daunting tasks for bioinformaticians. For instance, based on my experience in metagenomics field, when we use the same input data (sequencing reads), processed using different tools with the same hypotheses and purposes, it is possibly to provide completely different outputs, resulting in different interpretation. This kind of issues need to be addressed in the future for reducing the ambiguity in analyzing data and making the outputs more accurate. In addition, wet lab tests are always necessary for validating any bioinformatic analysis.

References:

1. Denny, J. C. (2014). Surveying recent themes in translational bioinformatics: big Data in EHRs, omics for drugs, and Personal Genomics. Yearbook of medical informatics, 23(01): 199-205.
2.  Butte, Atul J. (2008). Translational bioinformatics: coming of age. Journal of the American Medical Informatics Association: JAMIA.15(6): 709-14.
3. Tenenbaum, J. D. (2016). Translational bioinformatics: past, present, and future. Genomics, proteomics & bioinformatics, 14(1): 31-41.