Bioinformatics is a discipline that requires a combination of skills from the biological, computational, and statistical sciences. Many programs prioritize the technical aspects of bioinformatics over the biological concepts and logic of analyses, thus limiting the emphasis on critical thinking, problem solving, and in-depth inquiry. However, real-world problems found in research and industry require a flexible approach to bioinformatics that is equipped to analyze new types of data and address novel experimental questions.

To address the gap in bioinformatics education and train students to approach complex biomedical problems, we present a new model for curriculum development that combines our unique online learning environment with traditional pedagogical approaches delivered through academic partnerships. The T-BioInfo platform (https://t-bio.info) allows users to combine computational analysis modules into pipelines to develop solutions for ‘omics data and machine learning problems. State-of-the-art tools for analysis, integration, and visualization of data are offered through a user-friendly interface. In parallel, online educational modules provide a theoretical framework for the analysis methods and experimental techniques. This model for bioinformatics training was implemented at a liberal arts institution (Loyola University New Orleans) for the first time in January 2018. Twelve undergraduate students and five faculty members participated in a new one-semester bioinformatics course. Online content was managed through Blackboard and in-person discussion sessions were held at regular intervals. Participants gained experience investigating genetic variants in next-generation sequencing data, generating tables of differentially expressed genes from RNA-seq data, and analyzing transcriptomics data using principal component analysis and machine learning techniques. After completing a core set of online modules and pipelines, students conducted team research projects on topics such as patient derived xenograft (PDX) models, immune responses in cancer, and precision medicine. Results were presented in a poster session at the end of the semester. Gains in critical thinking and problem solving skills were observed and participants were enthusiastic about engaging in bioinformatics research.

Starting an independent bioinformatics department in many medium sized universities requires a lot of resources and long-term commitment that is often cost prohibitive. Especially, this is true in regions outside of the existing hubs where industry-academia collaborations are well established. As a resource that aligns curriculum between a number of partners and opens up the practical analysis capabilities to a broader community, T-BioInfo is a valuable alternative to such expensive endeavors.

In conclusion, our collaborative model for bioinformatics education combines best-practices in online and in-class learning with a powerful computational platform. This model could be implemented in undergraduate and graduate curricula to enhance research, build partnerships with industry, and strengthen the scientific workforce.
Read more and view the presentation here: https://edu.t-bio.info/collaborative-model-bioinformatics-education-combining-biologically-inspired-bioinformatics-project-based-learning/