Pursue complex, biological challenges in our online M.S. in Bioinformatics program. You will be prepared to apply artificial intelligence and machine learning methods to computational genomics and drug discovery. Your experience can lead to a rewarding career discovering novel genetic research findings and advancing precision medicine.
Students are eligible for scholarships.
Core Competencies in Our Online Bioinformatics Master’s
Bioinformatics integrates biology, computer science and data analysis. Our courses cover:
Degree Requirements14 courses, 42 graduate credits
Students will gain a common background in bioinformatics through thirteen core courses. The degree culminates with a comprehensive real-life, industry-type capstone, oriented toward the student’s domain of interest.
MSDS 510 Computer Programming Foundations for Data Science
3 credit hours.
Introduction to computer programming for data science using Python, R, and SAS.
There are no pre-requisites for this course. Students are expected to have a working familiarity with the discipline of data science and analytics and general knowledge about the impacts of Big Data in businesses and corporations. All students should have a working knowledge of all aspects of Microsoft Office; and it goes without saying that they should be familiar with Internet access and usage.
MSBD 520 Introduction to Biostatistics
3 credit hours.
Principles of biostatistics and the analysis of clinical and epidemiological data. Descriptions and derivations of statistical methods as well as demonstrations of these methods using Python/R. Topics include basic analysis methods, elementary concepts, statistical models and applications of probability, commonly used sampling distributions, parametric and nonparametric one and two sample tests, confidence intervals, applications of analysis of two-way contingency table data, simple linear regression, and simple analysis of variance.
MSDS 525 Data Management Foundations for Data Science
3 credit hours.
The concepts and structures used to store, analyze, manage, and present (visualize) information and navigation using Python, SQL, SAS, and QGIS. Topics will include information analysis and organizational methods, and metadata concepts and applications. Students will be assisted to identify disparate data sources needed to perform analysis for a given real-world problem. Typically, data from a single source will not be adequate to perform the required analysis. Students will pull data from the disparate data sources and import it into SAS and use several SAS procedures to detect invalid data; format, validate, clean the data; and impute the data if it is missing. This will prepare the data for statistical analysis and decision modeling in SAS.
MSBD 501 Introduction to Bioinformatics
3 credit hours.
Fundamental concepts and methods in bioinformatics; a wide range of topics including Unix/Linux Programming, sequence homology searching and motif finding, gene finding and genome annotation, protein structure analysis and modeling, genomics and SNP analysis, DNA, RNA, and protein databases, etc. (This is also a leveling course for students without background in biology.)
MSBD 502 Computational Structural Biology
3 credit hours.
This course introduces important topics in computational structural biology: fold recognition, protein structure classification, homology modeling, protein-protein docking, hierarchical docking, assembly modeling using experimental data from multiple sources, prediction of protein-protein networks, genome structures, and others. (This is also a leveling course for students without background in biology.)
MSBD 580 Research Methods
3 credit hours.
The research process investigating information needs, creation, organization, flow, retrieval, and use. Stages include research definition, question, objectives, data collection and management, data analysis and data interpretation. Techniques include observation, interviews, questionnaires, and transaction-log analysis.
MSBI 725 Mathematical Modeling for Bioinformatics
3 credit hours.
This course provides an in-depth exploration of the mathematical models and methods used to analyze and interpret biological data in bioinformatics. Students will learn to apply mathematical concepts such as probability theory, statistical inference, and dynamic systems to model complex biological processes, including gene regulation, metabolic networks, and population dynamics. The course emphasizes the development of quantitative skills necessary for building and analyzing models that can predict and elucidate biological behavior. Through a combination of theoretical instruction and practical exercises, students will gain the ability to construct and validate models that address key questions in genomics, proteomics, and systems biology. This course is essential for those seeking to understand the mathematical underpinnings of bioinformatics and to contribute to the development of innovative computational tools and approaches.
MSBD 570 Visualization and Unstructured Data Analysis
3 credit hours.
Data visualization tools and technologies essential to analyze massive disparate amounts of information and make data driven decisions. Information and geographic visualization of health data. Hands-on experience in planning, creating, and using compelling multimedia visualizations such as online maps, responsive graphs, interactive animations. Use of different visualizations to support various research activities including hypothesis formulation, data synthesis, analysis and exploration as well as communicate and share health information. Application of usability and user experience (UX) principles to evaluate the extents to which various visualizations meet expectations.
MSBD 550 Machine Learning for Biomedical Data Science
3 credit hours.
This course offers a comprehensive exploration of machine learning techniques and their transformative applications in the biomedical field. Students will gain hands-on experience in leveraging machine learning algorithms to analyze and interpret complex biomedical data, with applications ranging from disease diagnosis and prognosis to personalized treatment strategies. Emphasis is placed on understanding the entire machine learning pipeline, from data preprocessing and model selection to evaluation and deployment in real-world healthcare settings. Ethical considerations and challenges, such as bias and interpretability in AI-driven decisions, are also critically examined.
MSBI 740 Computational Biomedicine and Drug Discovery
3 credit hours.
This course focuses on the application of computational methods to the drug discovery process, integrating principles of computational biomedicine with the challenges of identifying and developing new therapeutic agents. Students will explore key topics such as target identification, virtual screening, molecular docking, structure-based drug design, and the use of machine learning in predicting drug efficacy and safety. The course also covers systems pharmacology and the modeling of drug interactions within biological networks. Emphasis is placed on the practical application of computational tools in the pharmaceutical industry, including the use of high-throughput screening data and the optimization of lead compounds. Through a combination of theoretical instruction, hands-on projects, and case studies, students will gain the skills necessary to contribute to the development of new drugs and to advance innovations in personalized medicine.
MSDS 575 Ethics in Data Science
3 credit hours.
Analysis of ethical issues, algorithmic challenges, and policy decisions (and social implications of these decisions) that arise when addressing real-world problems through the lens of bioinformatics, and the choices we make at the different stages of the data analysis pipeline, from data collection and storage to understand feedback loops in analysis.
MSBI 765 Computational Genomics
3 credit hours.
This course entails Fundamentals of Genomics, Sequencing Tools and Advanced technologies and developing advanced computational methods for the application in Comparative Genomics, Genome wide association study, population genomics, clinical genomics, functional genomics, application of genomics in public health and personalized medicine, privacy, and security in genomic data analysis.
MSBI 785 Special Topics in Bioinformatics and Computational Biology
3 credit hours.
This course delves into emerging and advanced topics in bioinformatics and computational biology, offering students the opportunity to explore cutting-edge research and technologies in the field. Each offering of the course focuses on different specialized areas, which may include topics such as metagenomics, structural bioinformatics, systems biology, single-cell genomics, or computational approaches to precision medicine. Through a combination of lectures, seminars, and project-based learning, students will engage with current literature, develop critical thinking skills, and apply advanced computational techniques to solve complex biological problems. This course is designed to adapt to the rapidly evolving landscape of bioinformatics, providing students with the tools and knowledge to stay at the forefront of the discipline.
MSBI 590 Capstone
3 credit hours.
Comprehensive real-life industry-type capstone, oriented toward the student’s domain of interest. Projects will include formulation of a question to be answered by the data; collection, cleaning, and processing of data; choosing and applying a suitable model and/or analytic method to the problem; and communicating the results to a non-technical audience.
You can apply your M.S. Bioinformatics classroom experience to research projects that will prepare you for your future career. Potential projects include:
Your research projects will be powered by a high-performance, supercomputer network and access to Meharry’s robust, diverse dataset.
Students at Meharry SACS may also benefit from the unique exposure to the Together for CHANGE (T4C) Initiative. The initiative, in partnership with leading pharmaceutical companies at the highest level of drug development—Regeneron, AstraZeneca, Novo Nordisk and Roche—is a comprehensive 10-year initiative that will lead to more equitable medical research and better treatments for Black populations worldwide by creating the first genomics database of people of African ancestry—the world’s largest—composed of genomic and phenotypic data from up to 500,000 volunteer participants.
Graduates could pursue impactful careers in the pharmaceutical, biotechnology, healthcare and similar industries.
Potential roles include:
If you meet one of the above requirements, you may begin the admissions process.
Applicants can apply to begin our programs in the Fall Semester, starting in August. While we accept students on a rolling basis, the following dates serve as application deadlines the semester. Please contact the Office of Enrollment Management about applying after these deadlines.
May 30, 2026: Priority deadline for application. All materials outlined in Step 2 of the applicant process are due by June 13, 2026.
Step 1
Complete an application.
That application will include the following:
Step 2
Step 3
On-campus (or virtual) interview with the School’s Admissions Committee.
International applicants must hold a degree comparable to a regionally accredited US baccalaureate or master’s degree. Applicants submitting transcripts from international colleges and universities are required to have them verified for US degree program equivalency before being considered for admission. Verification from the following organizations is acceptable:
The decision of the verifying organization must be transmitted directly to Meharry Medical College in electronic form.
Adequate proficiency of spoken and written English is essential to success in graduate study, and medical residency training at Meharry Medical College.
Please review Meharry’s F-1 English Language Policy and the College’s English proficiency requirements policy.
Are you interested in the M.S. Data Science or M.S. Biomedical Data Science program but need to improve your programming or statistics skillset? Contact us at sacsenrollment@mmc.edu or complete the request information form to learn about courses you can take to prepare you for either program.
Te’Onnika Hollowell
Student Recruitment and Admissions Specialist
teonnika.hollowell@mmc.edu
