BIOMI 6300 - Computational Approaches for Microbial Systems

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

Prerequisite: BIOMI 2900/BIOMI 2911, BIOMG 2800. Enrollment preference given to: graduate students. 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. Co-meets with: BIOMI 4300.

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, genomic, and shot-gun metagenomic 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.)

Outcome 1: Develop proficiency in command line tools and cloud computing within the shell.

Outcome 2: Analyze the quality of sequencing data.

Outcome 3: Explain and compare the different sequencing technologies and their applications to microbial gene and genome analysis.

Outcome 4: Evaluate various meanings of diversity and interpret compositional changes in microbial communities through statistical approaches and analysis of amplicon sequencing.

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

Outcome 6: Develop, visualize, and statistically test biological hypotheses in R.