Bioinformatics Master of science degree

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A bioinformatics master's degree that explores the unique combination of biotechnology, programming, and computational mathematics to combat illness and create novel technologies for industry.

The bioinformatics master's combines biotechnology, computer programming, and computational mathematics to prepare you to utilize and create technologies that will discover, treat, and cure a range of medical illnesses. The MS degree in bioinformatics provides students with a strong foundation in biotechnology, computer programming, computational mathematics, statistics, and database management. Graduates are well-prepared for academia and careers in the biotechnology, bioinformatics, pharmaceutical, and vaccine industries.

In laboratory exercises and assignments, students learn to sequence DNA and use computer programs to analyze DNA sequences and predict molecular models.

Bioinformatics is a field that has been developing over the last thirty years. It is a discipline that represents a marriage between biotechnology and computer technologies and has evolved through the convergence of advances in each of these fields. Today bioinformatics is a field that encompasses all aspects of the application of computer technologies to biological data. Computers are used to organize, link, analyze and visualize complex sets of biological data.

With the advent of high-throughput technologies such as Next Generation Sequencing and proteomics, bioinformatics has become essential to the biological sciences in general. In the past, laboratories were able to manage and analyze their experimental data in spreadsheets. Many research labs now require the expertise of dedicated bioinformatics core centers or their own in-house bioinformaticists.

Graduates of the bioinformatics master's program have entered such laboratories, both in industry and academia, as bioinformaticists. Some have also gone on to leverage their biotechnology experiences as wet lab experimentalists themselves. The diversity of skills our students cultivate has given them access to a wide range of career choices.

Based on consultation with individuals within the industry nationwide, the job market is rich with opportunities for those who obtain a graduate degree in bioinformatics, particularly when coupled with research as thesis work. This research provides exposure to real-world problems—and their solutions—not otherwise attainable in an academic setting.

The program provides you with the capability to enter the bioinformatics workforce and become leaders in the field. The curriculum is designed to fulfill the needs of students with diverse educational and professional backgrounds. Individuals entering the program typically have degrees in biology, biotechnology, chemistry, statistics, computer science, information technology, or a related field. The program accommodates this diversity by providing a comprehensive bridge program for students who need to supplement their education before entering the program. The program offers two tracks, one for students with backgrounds in the life sciences and one for those with backgrounds in the computational sciences. Regardless of the track pursued, students are prepared to become professional bioinformaticists upon graduation.

The program is offered on a full- or part-time basis to fulfill the needs of traditional students and those currently employed in the field.

Nature of work

Bioinformatics jobs come with several different areas of focus, which are less strictly hierarchical than bioscience discovery research jobs. The analyst/programmer job provides more focused computational analysis support. Analyst/programmers design and develop software, databases and interfaces used to analyze and manipulate genomic databases. They collaborate with production to develop high-throughput data processing and analysis capability and to design and implement data queries, novel algorithms, and/or visualization techniques. Analyst/programmers also maintain large-scale DNA databases, prepare data for other scientists, monitor new data from integrating sequence-based/ functional knowledge about genes to help scientists analyze and interpret gene-expression data. They also analyze DNA information and identify opportunities for innovative solutions to analyze and manage biological data. In addition, they often assist in developing software and custom scripts to automate data retrieval, manipulation, and analysis; application of statistics; and visualization tools. (Source: Vault Career Guide to Biotech; The Jobs in Lab Research)


Within the bioinformatics field employers tend to look for the following skills/strengths: fundamental training/knowledge in molecular biology, biochemistry and biotechnology, particularly, genomics, relational database administration and programming skills/e.g. using SQL, PERL, C,C++, etc. on a UNIX operating system, strong analytical abilities using relevant mathematical/statistical tools, a strong interest in utilizing computational skills to leverage the data outcomes of those working in the laboratory, meticulous, independent, patient to do the same task repetitively and multitask. (Source: Bioinformatics Career Guide)


  • Biotech and Life Sciences

  • Medical Devices

  • Pharmaceuticals

  • Health Care

Typical Job Titles

Senior Analyst/Programmer Associate Systems Analyst
Bioinformaticist Bioinformatics Analyst
Bioinformatics Engineer Developer
Computational Biologist Research Technician
Software Engineer

Curriculum for Bioinformatics MS

Bioinformatics, MS degree, typical course sequence

Course Sem. Cr. Hrs.
First Year
Ethics in Bioinformatics
This course will be focused on individual and organizational responsibilities in bioinformatics research, product development, product commercialization and clinical and consumer genetic testing. (This course is restricted to students in the BIOINFO-MS, BIOINFO-BS/MS program.) Lecture 3 (Fall).
Bioinformatics Algorithms
Bioinformatics Algorithms will focus on the types of analyses, tools, and databases that are available and commonly used in Bioinformatics. The labs will apply the lecture material in the analysis of real data through computer programming. (This course is restricted to students in the BIOINFO-MS, BIOINFO-BS/MS program.) Lab 3, Lecture 2 (Fall).
Bioinformatics Seminar
The course provides opportunities for students and faculty to develop and share professional interests while discussing current trends and developments in bioinformatics. Material for this course will be drawn from the current scientific literature. (This course is restricted to students in the BIOINFO-MS, BIOINFO-BS/MS program.) Lecture 3 (Fall).
Database Management for the Sciences
Students will learn to create and maintain efficient relational databases for use in modeling and analysis in the sciences. Topics will include an introduction to relational algebra, SQL, and advanced relational designs. (Graduate Science) Lecture 2, Studio 2 (Spring).
Computational Statistics and Data Science Methods  
This course will introduce traditional multivariate statistical methods and multi-model inference, as well as iterative computational algorithms (i.e. Bayesian methods and machine learning) appropriate for graduate students conducting or planning to conduct a graduate research project. The course will focus on the proper application of methods to a sample data sets using statistical programming software and graphics and will forego the more in-depth analytical mathematical exposition that you might see in a math course, so that we can cover a larger variety of methods and spend more time implementing them in code. Practical examples will often derive from the fields of biology, environmental science, or medicine, however the statistical methods we cover will also have much broader application within modern data science. The ultimate goal will be to learn when and where to correctly apply a given method to real questions about real data. Class time will be devoted to introductory lecture, programming language demonstrations with a common dataset, and open discussions of potential applications, including in-class studio hours to help with homework. Students should be prepared to learn to write code scripts that will manipulate statistical tests and graphical output. However, no background experience with programming is assumed. All software used in the course is open-source and students will be required to set up and run weekly assignments on their own laptop computer or on a computer borrowed from the library or RIT’s computer lab. (Prerequisites: STAT-145 or equivalent course or graduate student standing.) Lecture 2, Studio 2 (Fall, Spring).
Molecular Modeling and Proteomics
This course will explore two facets of protein molecules: their separation and their structure. The structure component will build upon information from earlier bioinformatics courses. Protein separation techniques will be addressed in lectures with descriptions of 2D gel electrophoresis and chromatography. Algorithms of protein secondary structure prediction will be implemented. Experimental techniques for tertiary structure determination such as NMR will be covered. The course will also include the analysis of inter-molecular interactions, such as ligand/receptor pairing, by employing software that permits modeling of molecular docking experiments. (Prerequisites: BIOL-330 or equivalent course or graduate student standing.) Lab 2, Lecture 2 (Spring).
Research and Thesis
Masters-level research by the candidate on an appropriate topic as arranged between the candidate and the research advisor. (This course requires permission of the Instructor to enroll.) Thesis (Fall, Spring, Summer).
Graduate Electives*
Second Year
Research and Thesis
Masters-level research by the candidate on an appropriate topic as arranged between the candidate and the research advisor. (This course requires permission of the Instructor to enroll.) Thesis (Fall, Spring, Summer).
Total Semester Credit Hours

* Any graduate-level course deemed related to the field of Bioinformatics by the program director.

Admission Requirements

To be considered for admission to the MS program in bioinformatics, candidates must fulfill the following requirements:

  • Complete a graduate application.
  • Hold a baccalaureate degree (or equivalent) from an accredited university or college in biology, biotechnology, biochemistry, chemistry, computer science, information technology, statistics, or a related discipline.
  • Submit official transcripts (in English) of all previously completed undergraduate and graduate course work.
  • Have a minimum cumulative GPA of 3.2 (or equivalent)
  • Submit two letters of recommendation from academic or professional sources.
  • International applicants whose native language is not English must submit scores from the TOEFL, IELTS, or PTE. A minimum TOEFL score of 79 (internet-based) is required. A minimum IELTS score of 6.5 is required. The English language test score requirement is waived for native speakers of English or for those submitting transcripts from degrees earned at American institutions.

Learn about admissions, cost, and financial aid 

Latest News

  • February 25, 2020

    photo of graduate student Alexandria Shumway

    Student to Student: Brittle stars

    While interested in science, Alexandria Shumway had never heard of bioinformatics before attending RIT. But after branching out and trying a new major, she discovered it was the perfect fit.

  • October 3, 2019

    Student sits in front of microscope and computer.

    Student Spotlight: Pursuing research opportunities in Germany

    Alexandria Shumway was selected to do research abroad over the summer through the Deutscher Akademischer Austauschdienst (DAAD) RISE program, or German Academic Exchange Service. Through this program, the fifth-year bioinformatics and computational biology (BS) and bioinformatics (MS) student traveled to Kiel, Germany, to complete her research at the Christian-Albrecht University of Kiel.

  • August 6, 2019

    Student in lab coat works with pipette.

    RIT expands genomics research

    RIT’s genomics research capabilities have evolved significantly over the past year. The university has invested heavily in revamping and equipping its Genomics Research Lab Cluster. The overhauled genomics facilities will boost capabilities for researchers in multiple disciplines, including bioinformatics, biotechnology and environmental science.