The rise of deep molecular characterization with omics data as a standard in biological sciences has highlighted a need for expanded instruction in bioinformatics curricula. Many large biology data sets are publicly available and offer an incredible opportunity for educators to help students explore biological phenomena with computational tools, including data manipulation, visualization, and statistical assessment. However, logistical barriers to data access and integration often complicate their use in undergraduate education. Here, we present a cancer bioinformatics module that is designed to overcome these barriers through six exercises containing authentic, biologically motivated computational exercises that demonstrate how modern omics data are used in precision oncology. Upper-division undergraduate students develop advanced Python programming and data analysis skills with real-world oncology data which integrates proteomics and genomics. The module is publicly available and open source at https://paynelab.github.io/biograder/bio462. These hands-on activities include explanatory text, code demonstrations, and practice problems and are ready to implement in bioinformatics courses.
Keywords: Python; bioinformatics; cancer; classroom teaching; genomics; online modules; proteomics.
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