Pixel International Conferences

The rapid adoption of high-throughput technologies in biology such as next-generation sequencing (NGS) and proteomics has required cross-disciplinary knowledge and experience in statistics, algorithmics and programming among life scientists. As well, efforts to find solutions to current problems in computational biology would benefit from the expertise of computational scientists. However, most undergraduate and graduate biology programs have antiquated curricula that do not adequately prepare their graduates in facing an increasingly data science-driven field. Meanwhile, computational scientists lack the necessary life science background to give context to problems in biology. To address the need for bioinformatics training, we developed a 6-week internship program called IMBUE to introduce the use and the development of computational approaches to biological problems to current and future life science and computation researchers. We implemented and assessed the first two of four planned iterations of this internship within a 6-week duration between 17 June to 26 July 2019 and 02 September to 11 October 2019 at the Philippine Genome Center (PGC). Each cohort consisted of 25 individuals with varying life science or computation backgrounds. For the first iteration, six lecture modules were designed to introduce basic concepts in biology and computation which were implemented in the first two weeks of the internship. The biology topics included molecular biology and biochemistry, genetics, and next-generation sequencing technologies. The computational topics included statistics and probability theory, programming, and bioinformatics pipelines. The remainder of the internship consisted of project-based learning through a hands-on bioinformatics research project in a topic of the interns’ choice. Administration of the lecture modules resulted in increased knowledge gain in the post-module assessments and knowledge retention towards the end of the internship among both biology and computation cohorts. However, biologists reported no increase in programming confidence towards the end of the internship, as opposed to the increased confidence of computational scientists in biological knowledge. This highlights the need for more rigorous interventions to increase programming competency among biologists, which we are currently implementing on the on-going second iteration of the internship. Meanwhile, the use of project-based learning greatly improved the interns’ interest in bioinformatics. The mix of computational and life scientists in the training pool enabled cross-disciplinary collaboration towards tackling their respective research projects.

Keywords: bioinformatics, genomics, computational biology, project-based learning.