MS in Bioinformatics

The MS in Bioinformatics is designed for students with undergraduate experience in molecular biology and genetics but lacking prior coding experience. Graduates will have gained foundational knowledge, applied skills, and experiential learning opportunities to prepare them for bioinformatics careers in biotechnology, healthcare, environmental science, and scientific research.

Unique aspects of the MS in Bioinformatics are:

It is biology-first. Students with strong backgrounds in molecular biology and genetics will receive core computational training to enter bioinformatics. No prior coding experience is required.
It is experiential. Through their required capstone projects, students will develop and demonstrate skills in data analysis, statistics, programming, and computational approaches to biological research. Other experiential learning opportunities are also available.
It is customizable. Electives in biomedical applications, computational methods and AI, and ecological informatics are available across various departments and schools at Tufts.
It prioritizes career development. Students will receive instruction, mentorship, and “career readiness” training from faculty and staff with experience in industry, academia, non-profits, and other sectors.

The MS in Bioinformatics curriculum

Our MS in Bioinformatics is a 30-credit, course-based, non-thesis program with an emphasis on experiential learning. It has three main elements:

1. Core curriculum: Our four-course core provides students with a formal theoretical and hands-on foundation in bioinformatics. Full-time students are expected to complete all four core of these classes in their first year:
   • Intro to Bioinformatics (fall, 3 credits): An introduction to computational approaches for analyzing DNA, RNA, protein, and multi-omics data, from sequencing to biological interpretation. Gain hands-on experience using the bioinformatics toolkit through real-world biological case studies.
   • Programming for Bioinformatics (fall, 3 credits): Introduction to programming for biologists in Python, applied to reading, transforming, and analyzing biological data. Covers core programming skills, working with standard biological data formats and public databases, and version control and reproducibility with Git.
   • Biostatistics for Bioinformatics (spring, 3 credits): Statistical methods (in R) for designing, analyzing, and interpreting bioinformatics experiments and observations. Accelerated coverage of core concepts (probability, parameter estimation, inference, correlation, regression, analysis of variance, nonparametric methods, etc.) followed by in-depth coverage of methods specific to large bioinformatics data sets.
   • Scalable and Reproducible Computing for Bioinformatics (spring, 3 credits): Principles and practices for scalable and reproducible computing in bioinformatics, including the use of shared, high-performance, and cloud computing environments, version control, workflow automation, and containers, applied to building and running large-scale bioinformatics workflows.
2. Bioinformatics capstone: This required three-credit course (offered both semesters) provides students with a mentored experience integrating statistical, computational, and biological knowledge to solve a real-world bioinformatics problem. Most full-time students will take the capstone course in the fall of their second year. During the course, students will meet regularly with the faculty course director and other students to discuss their progress, challenges, and opportunities.
   Students can identify capstone mentors from across Tufts, including the Department of Biology, the School of Engineering, and the Graduate School of Biomedical Sciences (at the Tufts medical campus). Examples of potential mentors include:
   • Lenore Cowen – Predicting protein function from structural and biological network information
   • Ethan Deyle – Ecological systems and dynamics in underwater populations
   • Fatima Hussain – Phage-bacteria and bacteria-bacteria interactions in the vaginal microbiome
   • Mitch McVey – Molecular mechanisms of DNA repair, recombination, and damage tolerance
   • Randi Rotjan – Marine ecology and global change
   • Paola Sebastiani – Genetics, biology, and epidemiology of aging and human longevity
   • Donna Slonim – Genes, pathways, and networks in human development and disease; characterizing individual samples in precision medicine; pharmacogenomics and drug discovery
   • Khoa Tran – Epigenetic regulation during cellular senescence and aging
3. Electives: Starting in their first semester, students can take electives across Tufts related to their interests and career aspirations, selected in consultation with their faculty advisor. The list of approved electives evolves based on curricular offerings, and students may petition the Program Director to receive credit toward the MS in Bioinformatics for courses not on the list that align with their academic and professional goals. Most students will take approximately five electives during the program.
   
   Most approved bioinformatics electives will fall into one of three areas. Students may focus mainly in a particular area or spread their exposure broadly across topics:
   • Biomedical applications: Epigenetics, microbiological genomics, drug development, human population genomics, etc.
   • Computational methods and AI: Computational biology, biological systems analysis, algorithms, etc.
   • Ecological informatics: Population and community ecology, evolutionary biology, geographic information systems, etc.

For more information about the curriculum and other aspects of the MS in Bioinformatics program, consult the MS in Bioinformatics Student Handbook.

Experiential learning for MS in Bioinformatics students

In addition to the Bioinformatics Capstone, qualified Bioinformatics MS students may apply to pursue additional experiential learning opportunities to broaden and deepen their hands-on experience, such as Research for Credit (BIO 193) or Independent Study (BIO 195). Note that except for the capstone, research lab positions are not guaranteed, and there is no thesis component to the MS in Bioinformatics program.

In addition, students in the Bioinformatics MS program are eligible to apply for the Biology Graduate Co-Op program, in which accepted students can participate in paid, full-time work experiences in non-academic settings.

Career development for MS in Bioinformatics students

We take a multi-pronged approach to helping bioinformatics students achieve their post-graduation career goals. Our program director, a seasoned professional with over 15 years of bioinformatics experience in academia and biotech, provides 1:1 advice to all Bioinformatics MS students in his role as faculty advisor. We also connect Bioinformatics MS students with current PhD students, experienced staff members in Tufts Career Services, external career advisors, and bioinformatics professionals in biopharma, healthcare, government, and non-governmental organizations for “informational interviews” and personalized guidance. We also offer a “Career Skills for Biologists” seminar each semester, which helps students explore diverse biology-related careers, prepare for job applications and interviews, and succeed in the workplace. MS in Bioinformatics students interested in the biopharma industry can also take advantage of additional resources and opportunities available to Master’s students in our department, described on the MS in Biology web page.

Sample Schedule

Please get in touch with the department directly at biology@tufts.edu for any questions about the MS in Bioinformatics program.