Ten Simple Rules for Developing a Short Bioinformatics Training Course

The foundation of a successful course is a well-defined and relevant need. The topic should address an immediate, widespread challenge faced by your target audience of biomedical researchers. To identify these needs, look for prevalent issues discussed in literature, such as those concerning reproducibility in research or common statistical misconceptions. Aligning the course with both the strategic goals of a research organization and the expressed interests of self-organized groups ensures that the content is not only relevant but also likely to attract a dedicated audience ¹ . Centering your planning on the learner, rather than a desire to teach a favorite topic, is key to creating a course that truly fills a gap.

Resist the urge to build a curriculum entirely from scratch. The wealth of high-quality, open educational resources available today makes this unnecessary and inefficient. A good starting point is to seek out existing materials that feature an applied, hands-on approach. Excellent examples include free online books like R for Data Science and The Python Data Science Handbook, or curated materials from projects like Bioconductor ¹ . Your role as a developer is to act as a curator, integrating the best materials from various sources—such as software user guides, online tutorials, and open-access texts—to create a coherent learning path. This approach significantly reduces development time and allows for more rapid deployment.

Effective bioinformatics training requires a careful balance between theory and practice. While lectures are efficient for conveying concepts, practical skills like coding and data analysis are best learned through hands-on practice ¹ . A course that leans too heavily on theory will leave learners unable to apply their knowledge; one that is purely practical may produce researchers who can run a script but cannot interpret the results or troubleshoot when something goes wrong. Strive for a blend where new concepts are briefly introduced just before learners use them in a practical exercise. This "just-in-time" teaching helps ground abstract ideas in immediate application, leading to a deeper and more functional understanding.

Structure is crucial for learning. A proven model for a short course involves dividing content into discrete sessions, each built around a clear learning objective. A effective session structure includes ⁸ :

This block structure can be repeated throughout the course, creating a predictable and supportive rhythm for learners. A typical three-day course would aggregate several of these blocks per day, beginning with an orientation and ending with a comprehensive final wrap-up to solidify the learning ⁸ .

One of the biggest challenges in short-course training is an audience with varied backgrounds. To bring all learners to a similar starting point, it is essential to clearly state the prerequisite knowledge and skills. When compiling materials, note the assumptions they make about prior knowledge in areas like statistics or programming. You can then provide, or point learners to, foundational resources to address these gaps beforehand ¹ . Using a simple pre-course survey to gauge familiarity with key concepts can help you tailor your instruction and ensure no one is left behind from the very first session.

Learning is a social activity. For in-person courses, the physical space should allow participants to work in small groups, fostering an atmosphere where they can learn from each other ¹ . The beginning of the course is the perfect time to "break the ice," establish ground rules, and create a team-oriented environment where instructors and participants share a common mission ⁸ . This collaborative spirit builds a supportive network, encouraging participants to ask questions and work through challenges together, which is far more effective than isolated, individual learning.

Be realistic about what can be achieved in a short time and how long people can maintain focus. Short sessions scheduled at a regular time are known to maximize participation ¹ . Avoid the temptation to cram in too much content; it is better to cover less material thoroughly than to rush through a comprehensive list. Furthermore, long, multi-hour practical exercises that require work outside of class should be avoided. Instead, design exercises that can be completed within the class session, allowing for immediate feedback and support from instructors ¹ .

Build in opportunities for learners to check their understanding throughout the course. This can be achieved through low-stakes, formative assessments such as in-session quizzes, short practical reports, or group discussions ^{2 4} . The goal is not to grade, but to provide feedback to both the learner and the instructor. For the learner, it reinforces key concepts; for the instructor, it highlights which areas may need further explanation. This continuous feedback loop ensures the course remains responsive to the learners' needs.

No course is perfect on the first delivery. A robust feedback mechanism is essential for continuous improvement. This can include a end-of-course questionnaire to be filled out before the final wrap-up session, as well as verbal feedback collected during that final discussion ⁸ . This feedback is a valuable source of information, revealing what worked well, what didn't, and how the balance between theory and practice was perceived. Use these insights to refine and improve the course for its next iteration.

When reusing and adapting existing materials, always be mindful of licensing. Ideally, use content with open licenses, such as Creative Commons licenses that permit adaptation ¹ . If the license is not clear, seek permission from the author or publisher. The bioinformatics training community thrives on sharing and collaboration. If you develop new materials to meet a critical need for which no resources exist, consider sharing them with an open license to contribute to this ecosystem and support fellow educators.