Online Tools for Teaching Cancer Bioinformatics.

Area of Science:

• Bioinformatics
• Computational Biology
• Genomics and Proteomics

Background:

• Deep molecular characterization using omics data is now standard in biological sciences.
• Large, publicly available biological datasets offer educational opportunities but face access and integration barriers in undergraduate settings.
• There is a growing need for enhanced bioinformatics curricula to address these challenges.

Purpose of the Study:

• To present a cancer bioinformatics module designed to overcome logistical barriers in undergraduate education.
• To demonstrate the application of modern omics data in precision oncology through authentic computational exercises.
• To provide educators with ready-to-implement resources for teaching bioinformatics.

Main Methods:

• Developed a module with six hands-on exercises focused on cancer bioinformatics.
• Integrated real-world omics data (genomics and proteomics) for authentic learning experiences.
• Included explanatory text, code demonstrations, and practice problems using Python programming.

Main Results:

• Upper-division undergraduate students gained advanced Python programming and data analysis skills.
• Students worked with integrated proteomics and genomics data relevant to precision oncology.
• The module effectively demonstrates the use of omics data in a cancer research context.

Conclusions:

• The cancer bioinformatics module successfully addresses barriers to using large omics datasets in undergraduate education.
• The module equips students with practical computational and data analysis skills for precision oncology.
• The open-source module is readily available for implementation in bioinformatics courses worldwide.