The University of Tennessee Herbert College of Agriculture has launched a new Bachelor of Science in Bioinformatics, creating what the university describes as the first undergraduate degree of its kind in Tennessee and the only bioinformatics bachelor’s program currently offered within the Southeastern Conference. The program is designed to train students to analyze biological information with the computational tools increasingly used to address challenges in agriculture, medicine, environmental protection and biotechnology. Its arrival reflects a broader transformation in the life sciences, where advances in DNA sequencing, artificial intelligence and high-performance computing are generating enormous datasets that cannot be interpreted effectively without specialized analytical methods.
Bioinformatics is an interdisciplinary field that connects biology with computer science, statistics, mathematics and data engineering. Modern researchers use it to compare genome sequences, identify mutations, predict the functions of genes and examine how organisms respond to disease or environmental stress. A single sequencing project can produce millions or billions of fragments of genetic information, requiring algorithms to assemble, organize and interpret the data. Students in Tennessee’s new program will study how biological questions can be translated into computational models, then use software, statistical approaches and machine-learning systems to extract meaningful patterns from complex datasets.
The degree combines biological sciences, data analytics, agriculture and environmental science, giving students a curriculum aimed at real-world problems rather than laboratory theory alone. Bioinformatics graduates may help identify genes associated with crop yield, drought tolerance or resistance to pests. They may analyze the genetic diversity of livestock, track pathogens or support breeding programs that use genomic information to select desirable traits. In environmental research, similar techniques can be applied to monitor biodiversity, study microbial communities, detect ecological change and assess the effects of pollution or climate-related stress on plants and animals.
The program’s emphasis on agriculture is particularly significant as food systems face simultaneous pressure from population growth, changing weather patterns, emerging diseases and limited natural resources. Precision agriculture increasingly depends on the integration of genetic, geographic and environmental data. By combining genomic information with measurements such as soil composition, moisture levels, temperature and crop performance, scientists can develop models that help determine which plants are likely to thrive in specific conditions. These systems can also support more targeted use of water, fertilizer and pesticides, reducing waste while improving productivity. The new degree is intended to prepare students to participate in this data-driven transition.
Artificial intelligence and machine learning will form part of the technical foundation of the program. Machine-learning algorithms are designed to detect relationships within large datasets and can be trained to classify biological images, predict disease risk or estimate how genetic changes may affect an organism. In agriculture, predictive models could help forecast pest outbreaks, identify early signs of plant disease or recommend management strategies based on local conditions. However, these tools depend on reliable data, careful statistical validation and biological interpretation. Students will therefore need to understand not only how to build computational models, but also how to evaluate their accuracy, recognize bias and determine whether a result is biologically meaningful.
Genomics will provide another central area of study. The falling cost of DNA sequencing has made it possible to examine the genetic material of crops, livestock, insects, microbes and wild species at a scale that was previously unavailable to many research groups. Yet sequencing data are not automatically useful. Researchers must remove errors, align sequences, compare genomes and apply statistical tests before drawing conclusions. They may also need to work with transcriptomic data, which reveal which genes are active under particular conditions, or with metagenomic data, which capture genetic material from entire microbial communities. Training in these methods could allow graduates to contribute to research ranging from crop improvement and animal health to ecosystem conservation.
David White, dean of the Herbert College of Agriculture, said the program is intended to prepare students for a future in which biological knowledge and technological expertise will be inseparable. Agriculture and environmental stewardship increasingly require researchers who can move between field observations, laboratory measurements and computational analysis. A student might collect plant samples, sequence their DNA, compare the results with a reference genome and then use a predictive model to estimate how a trait could influence crop performance. This combination of practical science and quantitative reasoning is also relevant to public health, where genomic surveillance can help track infectious diseases and identify changes in pathogen populations.
DeWayne Shoemaker, head of the Department of Entomology and Plant Pathology, where the major will be housed, described the degree as an investment in students who will use science and technology to address rapidly changing societal needs. The department’s connection to insects, plant diseases and agricultural production may provide a natural setting for applying computational biology to problems with immediate consequences. Digital analysis can help researchers distinguish closely related pathogen strains, identify disease-resistance genes or model how insect populations spread across landscapes. Such work is increasingly important as global trade, environmental change and evolving pests place new demands on farmers and scientists.
Graduates of the program are expected to find opportunities across agriculture, biotechnology, healthcare, environmental consulting, government and research. Their possible roles could include genomic data analyst, computational biologist, bioinformatics technician, agricultural data scientist or research associate. The skills developed through the degree are transferable because many industries now rely on large biological datasets and automated analysis. Employers, however, need professionals who can communicate across disciplines, understand experimental design and explain technical findings to people without specialized computational training. By combining scientific knowledge with programming, analytics and applied problem-solving, the University of Tennessee’s program aims to meet that demand while advancing the Herbert College of Agriculture’s focus on food security, environmental sustainability and the emerging bioeconomy.
The University of Tennessee Institute of Agriculture includes the Herbert College of Agriculture, the UT College of Veterinary Medicine, UT AgResearch and UT Extension. Through teaching, research and outreach, the institute supports agricultural and environmental work throughout Tennessee and beyond. The new bioinformatics degree expands that mission into one of the fastest-growing areas of modern science, where biological discovery increasingly depends on the ability to manage and interpret data. As students begin applying computational methods to crops, animals, ecosystems and disease, the program could help establish Tennessee as a regional center for training scientists capable of linking biological insight with artificial intelligence, genomics and advanced analytics.
Subject of Research: Bioinformatics education, agricultural genomics, computational biology and data-driven environmental science
Article Title: University of Tennessee Launches Tennessee’s First Undergraduate Bioinformatics Degree
Web References: https://utia.tennessee.edu/
Image Credits: Photo by C. Menard, courtesy UTIA.
Keywords: Bioinformatics, computational biology, agriculture, genomics, artificial intelligence, machine learning, precision agriculture, environmental science, biotechnology, University of Tennessee
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