Genetics Courses

Work in the life sciences increasingly relies on large scale, quantitative data that requires basic computational and statistics skills. This course is an introduction to basic Python and statistical concepts used in molecular genetics and genomics, aimed at first-year DBBS students. The format emphasizes practical problem-solving skills by teaching both core statistical concepts, such as hypothesis testing, confidence intervals, bootstrap simulation, and power analysis, as well as computational methods to implement them. The goal of the course is to prepare students for more advanced coursework, as well as self-teaching during their research careers.

This course is tailored for graduate students with a basic understanding of genomics who aim to deepen their expertise in advanced concepts and applications in the field. The curriculum covers a wide range of topics including the mapping and sequencing of genomes, the latest computational and experimental techniques for identifying genomic variants, and the study of epigenetic modifications such as DNA methylation and chromatin accessibility. Students will also delve into methods for inferring transcription factor binding sites and motifs. High-throughput techniques for ascribing function to DNA, RNA, and protein sequences, including single-cell RNA sequencing, whole-genome sequencing, massively parallel reporter assays, chromosome conformation capture (Hi-C) analysis, metagenomics, and proteogenomic, will also be discussed. Finally, the use of genomic techniques and resources for studies of human disease will be addressed.