Bioinformatics is a multi-disciplinary field combining biology, computer science, and quantitative analysis. As it has matured, its role in the biological and medical world has expanded. It is now commonplace across consumer, personal care, agriculture, tech, wellness, and, of course, biotech and medical sectors to implement bioinformatic projects and tools. Today, all scientists and scientific-adjacent roles can benefit from understanding the role of bioinformatics in R&D.
This course is designed to introduce key concepts and applications of modern bioinformatics to non-computational scientists, with a focus on their practical use in the professional sciences. This course does not assume prior coding experience (although advantageous) and will include a brief introduction to working within the command-line (e.g., UNIX/LINUX). We will primarily study and utilize web-based databases and tools to work with biological datasets.
The objective of the course is to understand how to frame biological problems as bioinformatic ones, identify the right tools to implement a solution, and be able to interpret for a variety of audiences. This course alone would not be sufficient to function as a bioinformatician but serves as an introduction to modern bioinformatic frameworks used in industry and will enable a non-bioinformatic scientist or manager to design, collaborate, or evaluate bioinformatic projects. The tools and projects in this course have been selected to closely follow common bioinformatic tasks across the professional sciences.
This course will draw on current, real-world projects and scenarios and emphasize relevant “soft skills” such as scientific communication, project management, and interdisciplinary collaboration in the professional sciences.
1. Introduce students to the current bioinformatics concepts and their applications.
2. Introduce students to the basics of working in command-line for common bioinformatic tasks including data processing and automation.
3. Teach students to cast biological and biotechnological problems as bioinformatic problems, provide them with the skills necessary to independently select relevant tools, optimize their settings, and design pipelines to solve problems.
4. Teach students a sufficient bioinformatics skill set, including an informed vocabulary and knowledge of basic script development, for productive collaboration within a multi-disciplined research team.
5. Prepare students for more advanced bioinformatics courses.
Technical Skills
Informed vocabulary and knowledge of basic script development for productive collaboration within a multi-disciplined research team.
Transferable Skills
Skills necessary to independently select relevant tools, optimize their settings, and design pipelines to solve problems.