This repository contains curricular material, lecture notes, labs, assignments, exams, code, solutions from the Spring 2022 version of BIOMI 609 Computational Genomics and Bioinformatics offered at San Diego State University. All lectures can be accessed at this YouTube Playlist: https://youtube.com/playlist?list=PL1e4GDlV5mnTQPoB7HR8UxQpq5stdMLiO
Course Description: BIOMI/CS 609 Computational Genomics and Bioinformatics
Genomics is shaping everything we do and everything we think about. From the role of genomics in medicine to the ready availability of personal genomics to the consumer, understanding the bioinformatics behind DNA sequence analysis is critical. This course covers computational algorithms in genomics, and their applications to biological questions. Topics covered will include genome assembly, annotation, variant calling, phylo- and population genomics, and genome-wide association studies.
Three hours of lecture+lab. Prerequisite: CS 503 or 514, or enrollment in Master of Science in Biological and Medical Informatics program, or PhD programs in Evolutionary Biology, Computer Science.
Course Learning Outcomes: By the end of this class, every student should be able to:
- Describe different types of biological data (genetic, genomic, proteomic) and how to analyze them.
- Understand the fundamental algorithms/statistics behind genomic data analyses.
- Use corresponding computational tools (online and offline) to analyze, interpret, and visualize genomic data.
- Write programs/tools for genomics in Python, Unix, and R.
- Inquiry-based learning all along in the labs – you will be provided with some background information, but the onus is on you to build a hypothesis, test it during lab, and summarize your results.
Assessments: Outcomes 1, 3: Bi/triweekly assignments and lab reports. Outcome 2: In-class problems, take-home assignments, midterm exams, final exam Outcomes 4, 5: Lab exercises in programming, take-home assignments
Activities: Outcome 1: Students are expected to practice analyses discussed in class on their own, during lab, and after class on a regular basis. The only way to learn genomics is by doing. While the background reading provides you some basic information, you will not learn anything if you don’t do it yourself. Outcome 2: Practice problems, work out assignments, work in groups, if need be. Outcome 3: Come to class, work on learning methods during lab, work separately on data analyses. Outcomes 4, 5: Practice, practice, practice! I can’t stress this enough. You are signed up for a class in genomics – you will learn to code. Period. If you don’t want to learn coding, or don’t care about it, please drop the class. This is not a class on just applying existing tools, you will write some tools yourself.
_The syllabus is tentative and is subject to change depending on how far we get each day. The problems will be due after we have discussed that topic. I will give you updates each week as to what problems will be due. _