Computational Approaches to Genome and Transcriptome Analysis (CAGTA)

Catalog description

This course provides a practical introduction to computational methods for analyzing modern genomic datasets, with emphasis on hands on programming skills and the biological interpretation of results. Students will learn core analytical workflows for RNA sequencing (RNA seq), whole exome sequencing (DNA seq), and single cell RNA seq, including data preprocessing, quality control, normalization, differential expression, variant analysis, and cellular clustering and annotation. Instruction will combine interactive lectures, guided coding sessions, and problem based exercises designed to build proficiency in command line tools and reproducible bioinformatics pipelines. A major component of the course is a team based research project in which students identify a publicly available dataset, develop a testable hypothesis, design and execute a computational workflow, and present their findings in a seminar style format. Evaluation will be based on class participation, and the final project presentation.