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The May CCTS Forum focused on bioinformatics: what is it, how it advances genomics and personalized medicine, how it supports translational science, and what bioinformatics resources the CCTS can offer. UAB Informatics Institute Associate Director Dr. Jake Y. Chen and CCTS Bioinformatics Codirector Dr. Elliot Lefkowitz presented.

Dr. Chen described bioinformatics as “still the wild west of science,” in which individual investigators are conducting research at the frontier of biomedical data science and building new tools that advance the field in real time. He noted there are now thousands of bioinformatics software tools available, with some of the most widely used built by the National Center for Biotechnology Information at the NIH.

He also described several open-source bioinformatics platforms such as Galaxy, GenePatterns, PAGER, and HAPPI. Dr. Chen explained that different types of biomedical research, such as omics, systems biology, bioengineering, and epidemiology, may require different bioinformatics analytic computing environments, and that the UAB Informatics Institute would be actively involved in helping catalogue these relationships. 

Dr. Lefkowitz presented examples of how bioinformatics is advancing genomics and personalized medicine, including decoding minor allele variants in viruses that impact phylogeny and transmission of the viral illness, and so have bearing on vaccine development. He described the bioinformatics resources available through the CCTS and encouraged attendees to contact the Research Commons (This email address is being protected from spambots. You need JavaScript enabled to view it.) to schedule a bioinformatics consultation.

When asked about his vision for the future of translational bioinformatics, Dr. Chen replied he imagined a world in which the genomics of patient samples could be collected and analyzed in real time, with results and recommendations pushed to clinicians. “We’re not there yet,” he said, “It will require informaticians to build models from existing genomic big data, interact with genomics-oriented clinicians who can then use the data to discuss what if scenarios and best treatment options with patients, and develop software that constantly learns from longitudinal data and can incorporate data from health sensors. That’s the goal we’re working toward.”