BIOMI 4300 - Computational Approaches for Microbial Systems

Spring. 3 credits. Student option grading (no audit).

Prerequisite: Background in microbiology (e.g. BIOMI 2900 or similar) AND a background in genetics and genomics (e.g. BIOMG 2800 or similar course). No experience with computing or programming is required as these materials will be covered in the first unit of the course. Co-meets with: BIOMI 6300.

M. Schmidt.

High-throughput sequencing has revolutionized and become common practice across the field of microbiology. This course will prepare students for analyzing large sequencing datasets through a meaningful biological lens. Via a combination of lectures, discussions of primary literature, and hands-on, data-driven computational labs, we will learn how to organize computational projects, work in the command line, perform cloud computing, and gather, interpret, and analyze amplicon and (meta)genomic data to advance our understanding of microbial systems. We will evaluate the distribution of microbial biodiversity and gene abundances and compare the taxonomic and genomic composition of microbial communities. This course is geared towards graduate students and upper-level undergraduate students across biology. We will focus on how to use software for biological analyses while touching on broader concepts of statistical algorithms. (Note: the specifics of statistical models will not be the focus.) No prior knowledge of coding is required as an introduction to coding and data science will be covered in the first unit of the course.

Outcome 1: Develop proficiency in command line programming in unix and R.

Outcome 2: Organize, build, and practice a bioinformatics computing workflow under version control with git and GitHub.

Outcome 3: Perform an amplicon sequencing analysis of microbial community data.

Outcome 4: Evaluate various meanings of biodiversity and interpret compositional changes in microbial communities through statistical approaches.

Outcome 5: Build and describe the steps to generating (meta)genomes from microbial sequencing data that can be used for downstream genomic analyses.