Spring 2026 DBB Topics Course Offerings

PUBH 1099.10 Variable Topics – International Union for Conservation of Nature’s Red List. 1 credit

The International Union for Conservation of Nature (IUCN) Red List of Threatened Species is a globally recognized standard for categorizing species imperilment and an important resource for conservation management. A hands-on introduction to the IUCN Red List Assessment process, including understanding levels of species imperilment and IUCN Red List categories and criteria, compiling, and vetting relevant raw data and mapping species distributional ranges. Participants will conduct IUCN assessments for Thecostracan barnacles, a previously unassessed group. The course assumes a basic knowledge of biology and human impacts (equivalent to BISC 1006; BISC 1008) and conservation and ecology theory (equivalent to BISC 2010; BISC 2454; BISC 3460). Knowledge of marine systems would be beneficial (Equivalent of BISC 2194; BISC 3454)

PUBH 6899.10 Single Cell and Spatial Genomics Analysis. 3 credits 

This course covers the principles of single cell isolation methods and sequencing strategies and state-of-the-art statistical approaches for single cell and spatial data. Students will learn how to process raw sequencing data to generate primary data and perform downstream analysis and visualization. Various data modalities will be covered, with an emphasis on gene expression datasets. This course assumes a basic knowledge of biology (equivalent to BISC – 1116 & 1126), introductory courses in statistics or biostatistics (equivalent to PUBH 6868 or STAT 1127) and R (equivalent to PUBH 6851), and graduate standing. 

PUBH 8899.11 Advanced Omics Data Science: Longitudinal Analysis. 1 credit

Advanced statistical methods for analyzing longitudinal omics data using mixed effects, CPLM, GLMM-Lasso, and nonparametric models to uncover temporal biological variation. This advanced short course introduces statistical frameworks for modeling longitudinal omics data collected from temporal repeated measurements across subjects. Students will learn methods to account for subject-specific and nested effects, manage the preciseness comes from repeated measurement, within-subject correlation structure, and apply penalized regression approaches such LASSO for feature selection, as well as exploring the nonparametric models for flexible trend estimation. Emphasis is placed on practical
implementation, interpretation, and reproducible analysis of biological data using real-world datasets. Through lectures and hands-on exercises, participants will gain the skills to analyze temporal omics data and derive meaningful biological insights.

PUBH 6899.12 Microbiome Data Analysis in R. 2 credits 

This course is designed for graduate students interested in developing foundational knowledge, analytical skills and experience in microbiome
research, including genomics, bioinformatics, programming and biostatistics. Students will learn different R packages to analyze microbiome data and address key questions in microbiome research. This course assumes a basic knowledge of biology (equivalent to BISC - 1116 & 1126), introductory courses in statistics or biostatistics (equivalent to PUBH 6868 or STAT 1127) and R (equivalent to PUBH 6851), and graduate standing.