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 designed for beginning students who want to become familiar with the basic concepts and applications of genomics. The course covers a wide range of topics including how genomes are mapped and sequenced as well as the latest computational and experimental techniques for calling genomic variants, epigenetic changes like DNA methylation and accessible chromatin, and inferring transcription factor binding sites and motifs. High throughput techniques for ascribing function to DNA, RNA, and protein sequences including single-cell RNA-seq, whole genome sequencing, massively parallel reporter assays, chromosome conformation capture (Hi-C) analysis, and metagenomics will also be discussed. Finally, the use of genomic techniques and resources for studies of human disease will be discussed.