Instructor: Ryan Blaustein, Ph.D. (rblauste@umd.edu)
Location: Woods Hall, Room 1127
Time: Tuesdays and Thursdays, 9:30-10:45am
Office Hours: Contact instructor for appointment
Course Objective: Genomics research is increasingly important in agriculture and biotechnology. New advances in understanding genome function and, at the foundation of food systems and nutrition, interactions in complex microbiomes (microbial communities and their genes) have come from the generation and processing of ‘big data.’ Essential to applied genomics research is knowledge of programming language for statistical analysis and interpretation of large datasets. NFSC-415/615 will train students with skills for programming in R, the primary open-source language used in the agricultural and life sciences, and how to analyze genomics and microbiome data.
Course Description: This course will provide a comprehensive introduction to R, along with statistical concepts and algorithms used in whole genome sequencing (WGS) and microbiome analysis. Most of the course will deal with R scripts and packages, though additional software tools may be incorporated. This course will consist of three modules:
1. Basics of the R language
2. Genomic applications in food science and nutrition
3. Introduction to omics data analysis
Prerequisites: Students are expected to have taken at least one semester of biostatistics, such as BIOM-301 or BIOM-601, and to have background understanding of principles in molecular biology, or permission granted from the instructor.
Student Learning Outcomes: After completion of this course, students will be able to: 1. Write R scripts and use coding language
2. Navigate open-source software applications and genomics repositories, such as in the National Center for Biotechnology Information (NCBI) archives
3. Perform basic genome and microbiome analysis
Grading: Students will complete weekly exercises and module projects to demonstrate knowledge and application of concepts discussed in class.
Course Assignment Final Grade Scale*
Weekly exercise, 10 points, x10 = 100 points A+ (97-100%); A (93-96%); A- (90-92%); Module 1 Project, 100 points B+ (87-89%); B (83-86%); B- (80-82%); Module 2 Project, 100 points C+ (77-79%); C (73-76%); C- (70-72%); Module 3 Project, 100 points D+ (67-69%); D (63-66%); D- (60-62%); Term paper, 100 points (NFSC-615 only) F (<60%)
*The final grade reflects the student’s understanding of the subject material. At the end of the semester, the final grade scale may be adjusted.
Communication: All lecture materials, including R-scripts, papers for discussion, and assignments will be posted to CANVAS on a weekly basis.
Resources and Software: Students need to install the free software R (https://www.r project.org/) and R-studio (https://posit.co/download/rstudio-desktop/) on their personal computer and bring the computer to each lecture.
Assignments:
Weekly Exercises – Given the fast-paced nature of this course, weekly exercises will be assigned to support continuous learning and skill development. These ‘take-home quizzes’ will be designed to build proficiency in R scripting and reinforce key course concepts. Exercises will be due on the Tuesday following the week in which they are assigned (e.g., a Week 1 assignment is due Tuesday of Week 2).
Module Projects – At the conclusion of each module, students will complete a comprehensive project to demonstrate their ability to apply R programming and integrate course concepts. Projects will include annotated R scripts and accompanying summaries that reflect core coding skills, e.g., use logical and mathematical operators, sub-setting data, working with apply family functions, writing loops and original functions, navigating conditional statements, generating data visualizations, and/or applying statistics to answer relevant analytical questions. Example datasets (e.g., microbiome and RNA-seq) will be provided for these projects.
Term Paper (NFSC-615) – Graduate students will develop a research proposal focused on genomics applications covered in the course. Proposals may address either applied or fundamental research questions and must incorporate at least two of the following approaches in the detailed experimental design and plans for the computational analysis: • Whole genome sequencing
• Amplicon sequencing (e.g., 16S rRNA gene)
• Shotgun metagenomic sequencing
• Transcriptomic sequencing
• Other area approved by the instructor
The paper is limited to 10 pages (excluding references) and is due Friday, December 4, 2026.
Late assignments will receive a 20% reduction in the assignment grade for each week that it is late. Assignments that are not submitted after 2 weeks will receive a 0.
Policy on Artificial Intelligence (AI) tools: Understanding how and when to use generative AI tools (such as ChatGPT and Gemini) is quickly emerging as an important skill for future professionals. While students are permitted to use AI for general brainstorming, every element of the above class assignments must be prepared by the student. The use of generative AI tools to replace independent research or in an unreferenced way will be treated as plagiarism.
Honor Code: It is expected that all students adhere to the Honor Code administered by the UMD Student Honor Council. Any student involved in academic dishonesty will be reported and will receive a course grade consistent with university policies. For more information see: http://www.shc.umd.edu/code.html.
Accommodations: If you wish to discuss academic accommodations, please provide documentation from the Accessibility and Disability Support Service (301-314-7682; adsfrontdesk@umd.edu). If you are encountering personal difficulties during the course, please let the instructor know as soon as possible. The UMD Counseling Center (301-314-7651) is available for assistance as well.
Schedule of Classes:
Week | Date | Topic | Description |
MODULE 1: Basics of the R language |
1 | 09/01/26 | Introduction to R | R language, R studio, Bioconductor, software installation |
09/03/26 | language elements, i.e., vectors, matrices, lists, data frames, factors |
2 | 09/08/26 | Labor Day - no class |
09/10/26 | Basic operations | read and write, import, export, assign values to variables, browse data |
3 | 09/15/26 | Coding functions I | summary stats (mean, stdev, min/max), compare elements |
09/17/26 | string manipulation, subset, ordering |
4 | 09/22/26 | Coding functions II | using apply family functions, loops |
09/24/26 | writing custom functions |
5 | 09/29/26 | Graphing features | ggplot2, graphical parameters |
10/01/26 | preparing high-quality figures |
MODULE 2: Genomic applications in food science and nutrition |
6 | 10/06/26 | Next-generation sequencing | overview of sequencing technologies |
10/08/26 | generating data, accessing repositories (NCBI), large-scale project examples |
7 | 10/13/26 | Fall break - no class |
10/15/26 | Applications for genomics in food science and nutrition | Overview of whole genome sequencing, microbiome, metagenomics |
8 | 10/20/26 | Experimental design for omics projects | sampling applications and limitations |
10/22/26 | hypothesis testing, data distributions |
9 | 10/26/26 | Univariate and multivariate statistics | t-test, ANOVA, Wilcoxon, Kruskal Wallis, linear models, correlation |
10/29/26 | bootstrap, permutational ANOVA, PCA, PCoA, NMDS |
10 | 11/03/26 | Machine learning applications | overview of models |
11/05/26 | caret package, random forest example |
MODULE 3: Introduction to omics data analysis |
11 | 11/10/26 | Working with WGS data | quality control, genome assembly, annotation |
11/12/26 | exploring features with BLAST; e.g., antimicrobial resistance, virulence |
12 | 11/17/26 | Microbiome analysis | R and command line tools for amplicon datasets |
11/19/26 | characterizing microbial diversity, data visualization |
13 | 11/24/26 | shotgun metagenomics applications for functional profiling |
11/26/26 | Thanksgiving break - no class |
14 | 12/01/26 | Functional genomics | overview of gene expression analysis such as with Blast2GO |
12/03/26 | pathway assignment, e.g., KEGG, COG |
15 | 12/08/26 | Multi-omic applications | new directions and advancements in workflows |
12/10/26 |
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