TABLE 1.

Six lessons and associated learning outcomes: at the end of each lesson, students will be able to perform the following learning outcomes

Lesson topic	Biological learning outcomes	Computational learning outcomes
1. Introduction to Cancer Datasets	• Locate proteogenomic cancer datasets from real tumor samples	• Manipulate Pandas DataFrame
		• Select relevant clinical cancer data
2. Missense Mutation	• Identify frequent mutations in cancer cohorts	• Access UniProt knowledgebase using API
	• Compare and contrast different cancer types	• Parse JSON and integrate cancer data with UniProt
	• Assess functional impact of DNA mutation
3. Truncation Mutation	• Classify mutation impact on primary sequence, domain structure, and protein function	• Create new DataFrame elements with .apply() function
		• Access UniProt knowledgebase using API
		• Import/export data from Colabs
4. Copy Number Variation	• Identify copy number variation (CNV) changes in genes	• Plot CNV events
	• Distinguish between focal and arm level CNV events	• Integrate gene location and CNV data
	• Compare frequent CNVs in multiple cancer types	• Create new DataFrame elements with .apply() function
5. Transcriptomics	• Identify differential expression of transcripts between tumor and normal samples	• Perform pathway enrichment analysis
	• Interpret a gene list as a functional set of pathways	• Create boxplot visualization
	• Justify choice of enrichment methods based on gene list characteristics	• Perform unpaired t test with multiple hypothesis correction
6. Proteomics	• Evaluate utility of protein coexpression networks based on agreement with known protein interaction networks	• Perform correlation analysis
		• Create network visualization
		• Access UniProt knowledgebase using API