Information for Prospective Students
The Bioinformatics and Computational Biology graduate program is an all-University, interdisciplinary graduate program. The administrative home is at the University of Minnesota Rochester. Faculty come from the University of Minnesota Twin Cities, the University of Minnesota Rochester, the Hormel Institute, Mayo Clinic, IBM, Cray Inc., National Marrow Donor Program (NMDP) and the Brain Sciences Center. A Director of Graduate Studies (DGS) and an Associate Director of Graduate Studies (A-DGS) are the liaison with departments and partnering institutions.
The program aims to create a paradigm shift in the way interdisciplinary, multi-institutional higher education is delivered. With multiple institutional partners, we provide students the opportunity to work with faculty from academia, a clinical institution, and industry. The program is designed to overcome challenges of geographically dispersed partnering institutions by delivering courses via ITV, video-conferencing, and regular meetings of all faculty and students.
We expect students to gain competency in the areas of computer science, informatics, mathematics, statistics, and the biological and health sciences. While the students' research will focus on development and applications of computational methods, conducting the research in industry or laboratories will prepare students for an interdisciplinary and collaborative work environment and provide hands-on experience with experiments to gain a deeper understanding of the data types that are generated. We expect that most students who pursue a research thesis as part of the degree will become members of a research team to promote this interdisciplinary and collaborative mode of training through co-advising across institutions and disciplines.
The majority of our M.S. students work full time in health care industries or clinics. They pursue the degree part-time to advance their skills to meet the demands for a rapidly changing workplace where complex data have become the basis of clinical decision making. Full-time students are either enrolled in the M.S. or Ph.D. degree to prepare themselves for careers in the private sector or academia. A small number of part-time Ph.D. students work full time in a research environment where they can combine work with pursuing a Ph.D. degree at the same time.
The graduate program is committed to working with students on understanding the financial implications of enrolling into a graduate program. A limited number of one- or two-year UMR BICB fellowships are expected to be available for full-time Ph.D. graduate students upon entry. These are competitively awarded. (Ph.D. students who work full time and are thus part-time students are not eligible for the UMR BICB fellowship.) Students who are applying for the Ph.D. and for whom the program has identified potential adviser(s) are automatically considered for these fellowships. Admission to the Ph.D. program is contingent on identifying both guaranteed funding sources for the first two years and likely funding sources for the following years in addition to a thesis adviser. Funding sources may include the student’s own funding and/or company reimbursement policies.) A Ph.D. thesis adviser is expected to provide funding for full-time Ph.D. students after the fellowship funding ends, unless a different arrangement has been agreed on. Ph.D. students are encouraged to apply for their own funding, and the program offers help with proposal writing during the first-year course where every Ph.D. student is asked to write a proposal to initiate their research. The program does not provide funding for M.S. graduate students and does not ask M.S. advisers to provide funding. For more information regarding work-related policies, see Graduate Assistant Employee Services in the Office of Human Resources.
Graduate students are admitted to the University of Minnesota after review of applications by the faculty of the program for which the student applied. Students can pursue their degrees on either the Rochester campus or the Twin Cities campus. However, international students typically enroll on the Twin Cities campus while completing their course work due to visa regulations. The Bioinformatics and Computational Biology (BICB) graduate program is one of many graduate programs offered by the University of Minnesota. A list of all majors and degrees offered by the University of Minnesota, the faculty members, requirements, and courses can be found in the Graduate School Catalog.
Tuition and Fees are listed on the University web page Tuition fact sheet.
We expect incoming graduate students to have a strong background in the quantitative sciences and varied backgrounds in the life/health sciences. Specifically, we expect incoming students to have taken the following courses at the undergraduate level prior to entering the program:
- Calculus (1 year)
- Introductory computer science course and basic programming skills (1 semester)
- Chemistry (1 year)
- General biology course (1 semester)
In addition, we expect students to have background in either two of the areas 1-3 or one of the areas 1-3 and one of the areas 4 and 5:
- Multivariable calculus, differential equations, linear algebra
- Algorithms and data structure, discrete mathematics
- Statistics or biostatistics; probability theory
- Biochemistry, genetics and cell biology
- Health sciences (pharmacology, physiology, or related areas)
A student might be admitted without meeting the prerequisite requirements, but a plan must be in place to make up deficiencies within the first year. (See below under "How to Prepare Yourself" for further suggestions on how to fill gaps.)
Submitting Your Application
All materials are submitted electronically through the graduate school online application system. Admission decisions are communicated to applicants using this system. The BICB graduate program accepts applications from December 15 through April 1 for the Ph.D. program for fall semester. A limited number of fellowships are available for Ph.D. graduate students. For full consideration, please submit your application no later than February 15. Applications for the M.S. program are accepted throughout the year for either fall or spring.
A decision for admission notice will be e-mailed to you once your application is carefully reviewed by the program's admission committee and your transcripts and credentials (test reports, diploma copies, etc.) are authenticated by Graduate School officials. If admitted, Ph.D. applicants can expect a decision by April for the following fall semester. Decisions for the M.S. program are made on an ongoing basis.
In addition to completing the online application form you must submit a personal statement, which describes your past experiences and career aspirations, and why you wish to pursue graduate studies in biomedical informatics and computational biology. Please indicate the names of the BICB graduate faculty whose interests overlap with yours. If you apply to the Ph.D. program, or wish to pursue a research thesis for the M.S. program, we strongly encourage you to contact these faculty members before you apply. This is particularly important for application to the Ph.D. program as we only admit students to the Ph.D. program for whom we can identify an adviser. Although there is no page limit for the personal statement, we recommend that it be 2-3 pages.
We require the general GRE test (no subject test is required), unless significant work experience in a related field can be demonstrated, in which case the applicants should request a waiver in the personal statement. Scores should be sent directly to the U of M Graduate School by the Educational Testing Service (Institution #Code 6874, University of Minnesota). TOEFL scores are generally required of all applicants whose first language is not English. The scores should be sent to the same institution code (6874). See Graduate School web page for further information, in particular for the operational standards for admission and exemptions.
For applications to the BICB Ph.D. program, three letters of recommendation from persons familiar with your academic and professional experience who can comment on your suitability for a research program should be uploaded electronically via graduate school online application system. You must provide their e-mail addresses in your application.
For applicants to the M.S. program, we request that you name three reviewers and ask them to complete the online evaluation through graduate school online application system. No letter of recommendation is required, though reviewers can submit them either in addition or in lieu of the evaluation form. You must provide their e-mail addresses in your application.
Unofficial Transcripts of all universities and colleges attended should be uploaded directly to the online application. Please do not mail in paper copies of your transcripts, there is no need for official transcripts or academic records for initial review. If you are admitted, the University will then request official copies of this material. Click here for more information about transcripts and credentials.
Many students have gaps on either the computational side or the life sciences side of this interdisciplinary field. With the large number of free online courses available on the web, there are now ways to prepare yourself without going back to college and taking undergraduate courses. This is particularly attractive to adult learners who plan to return to a university for an advanced degree. If online courses do not work for you, many community colleges offer courses in a cost-effective way that get you started with courses in computer science or biology. The University of Minnesota Informatics Institute maintains a website with additional information on how to prepare yourself for a career in informatics.
If your undergraduate (or other postsecondary) degree is in the life sciences, you will likely have an excellent background in biology but you may not feel comfortable with taking a graduate level course in computer science. MIT Open Courseware through OCW Scholar offers an “Introduction to Computer Science and Programming” course that is free, self-paced, and is aimed at students with little or no prior experience in programming. Follow this course up with a course on Algorithms. There are a number of universities that offer the course for free: MIT Open Courseware offers “Introduction to Algorithms” (6.006), which has course materials available for free on the website. If you want video lectures with the course, take the course at Coursera, which offers the course from both Princeton and Stanford. If you find programming difficult, you may want to take a course in a programming language (Python, R, Perl, or Java) at a local community college or local university. There are also online programming courses available from Extension Services at universities, such as UC Berkeley Extension.
If your undergraduate degree (or other postsecondary) degree is on the computational side, you will likely have an excellent background in computer science, mathematics, or statistics, but may not feel comfortable taking graduate level courses in biology and biochemistry. MIT Open Courseware makes MIT’s chemistry courses freely available. To prepare yourself for biochemistry, you need some background in organic and general chemistry. To learn the language of modern biology, MIT’s “Introduction to Biology” course is a good start. This course is enhanced with video lectures. To learn more about genomics and computational biology, the “Genomics and Computational Biology” course from MIT is still relevant, although already ten years old. Another course of interest may be the “Genomic Medicine” course from MIT, which was taught in 2004. The three biology courses are all multi-media enhanced, that is, they include video and/or audio lectures. A more recent course in “Molecular Biology and Genetics in Modern Medicine” from MIT covers basic concepts in molecular biology and genetics in a clinical context. This course is not multi-media enhanced. Coursera offers a course on “Introduction to Genetics and Evolution” from Duke University, and a course on “Introduction to Genome Science” from the University of Pennsylvania.
Explore the full range of massive online open courses (MOOCs). They are available through iTunes U, Academic Earth, Coursera, MIT Open Courseware, Udacity, and EdX. New players in this field continue to enter the market. Many of these enhance the courses with Discussion Forums, which work well when students take the courses synchronously, such as on Coursera. Because of the large number of students simultaneously enrolled in these courses (often, in the thousands), questions asked by students get answered very quickly and the dialogue can be followed by others who may have similar questions.