{"id":48,"date":"2009-05-05T17:12:39","date_gmt":"2009-05-05T21:12:39","guid":{"rendered":"https:\/\/www.bu.edu\/bioinformatics\/?page_id=48"},"modified":"2025-08-12T15:09:05","modified_gmt":"2025-08-12T19:09:05","slug":"masters","status":"publish","type":"page","link":"https:\/\/www.bu.edu\/bioinformatics\/admissions\/programs\/masters\/","title":{"rendered":"Master’s Program"},"content":{"rendered":"

The Graduate Program in Bioinformatics offers\u00a0the\u00a0<\/span>Master of Science<\/strong>\u00a0<\/span>[MS] degree, with a curriculum created to prepare students for the most cutting-edge industrial positions in the field.<\/p>\n

The Program is flexible – students can enroll as full-time and finish the degree in as little as 12 months or enroll as part-time and take courses over a longer period of time.\u00a0
\n<\/strong><\/p>\n

<\/strong>BU Bioinformatics Program Merit Scholarships<\/h2>\n

Tuition assistance is available in the form of MS Merit Scholarships, which provide partial tuition to full-time MS students.\u00a0 These scholarships are awarded at the time of admission and\u00a0do not require a separate application<\/b>. These awards are made to both international students and U.S. citizens with\u00a0an outstanding\u00a0academic record, and who have demonstrated enthusiasm and knowledge in the field of Bioinformatics.<\/p>\n

MS Program Requirements<\/h2>\n

The master\u2019s degree requires a total of 32 credits. MS candidates must demonstrate mastery of the core subject matter (no lower than a \u201cB\u201d in core courses) to fulfill\u00a0their degree requirements. Students must also demonstrate a working knowledge of computational methods available to the modern bioinformatician through the successful completion of a 400-hour internship\u00a0project, followed by a written and oral report which will serve as a master\u2019s thesis. Fulfillment of core course equivalents will be determined based on documented previous academic and\/or work experience. If an\u00a0alternate course has been accepted as a core equivalent, the\u00a0academic advisor will recommend a\u00a0courses to fulfill the requirements.<\/p>\n

For further information on requirements please see our Frequently Asked Questions page<\/a><\/p>\n

Internship Projects<\/h2>\n

MS students in the BU Bioinformatics Program have completed internships at more than 100 labs, hospitals, universities and biotech companies, including New England Biolabs, the MIT Lincoln Laboratory, the Hospital Corporation of America, Novartis, Takeda, and Biogen.<\/p>\n

Core Courses<\/h2>
<\/p>\n

ENG BF 527: Bioinformatics Foundations<\/strong><\/p>\n

The field of bioinformatics involves managing and analyzing large biological datasets to better understand complex living systems and improve human health. Key bioinformatics tasks include analyzing genomic sequences and structures, studying evolutionary relationships, interpreting omics experiments, and analyzing gene networks. This course introduces graduate students to foundational problems in bioinformatics and provides hands-on experience with the databases and computational tools used to address these challenges.<\/p>\n

The course emphasizes practical skill development in computer labs, enabling students to apply bioinformatics methods in their research. Familiarity with basic molecular biology is recommended. While prior programming knowledge is beneficial, it is not required. Enrollment is currently limited to master\u2019s students in the Bioinformatics Program. 4 cr.<\/p>\n

ENG BF 528: Genomic Data Analysis<\/strong><\/p>\n

Bioinformatics is an interdisciplinary field devoted to managing and analyzing large-scale biological data, such as the DNA sequence of the entire human genome, and has revolutionized our understanding of molecular biology. This course will expose students to modern translational bioinformatics studies with a specific focus on Next Generation Sequencing (NGS) technologies. The analysis of the data produced by these techniques requires both the computational skills to develop and apply bioinformatics algorithms along with the biological knowledge to translate the results into clinically relevant findings.\u00a0 Lecture topics include but are not limited to, genome editing techniques, gene sets and gene set enrichment, whole genome sequencing, and transcriptomics. This course emphasizes practical and hands-on experience developing Nextflow pipelines that perform end-to-end analysis of sequencing data. All work in the course is project-based and will involve replicating key findings from published RNA-sequencing, ChIP-sequencing, and Single Cell RNA-sequencing experiments. The class will also introduce a set of best practices and tools for the development of reproducible and portable computational workflows including version control, and environment management. 4 cr.<\/p>\n

ENG BF 550: Foundations of Programming, Data Analytics, and Machine Learning in Python<\/strong><\/p>\n

This course is for students trained in life sciences with minimal exposure to programming, statistics, and data analysis. The goal of the course is to develop both practical skills and theoretical foundations in handling data sets and developing simple computational solutions to problems arising in biomedical research. 4 cr.<\/p>\n

ENG BF 751: Omics Fundamentals<\/strong><\/p>\n

Modern research in the life sciences is an increasingly interdisciplinary endeavor where new fields of study have developed at the interfaces of biology, chemistry, physics, mathematics, and computer science.\u00a0 In many instances, the development of these new fields goes hand in hand with development of new experimental approaches to surveying the content of the cell.\u00a0 This course is a tour of experimental methods utilized in the generation of many types of biological data relevant to bioinformatic analyses.\u00a0 We cover experimental design, methodological limitations, and data quality concerns for widely used techniques for collecting genomic, metagenomic, transcriptomic, proteomic, and metabolomic data (and others).\u00a0 This includes reiterating the biochemical principles and properties of biomolecules that directly affect data collection and analysis, developing familiarity with the critical reading of primary literature across bioinformatic subdisciplines, and through the final project, in depth research and scientific communication about the experimental generation of data for specific analysis techniques.\u00a0 This course expands on undergraduate learning to provide an advanced and in-depth consideration of the central dogma and its machines, including topics such as epigenetic regulation, stochasticity in gene expression patterns, and human genomics and genome wide association studies. 4 cr.<\/p>\n

ENG BE 768: Biological Database Systems<\/h4>\n

Describes relational data models and database management systems; teaches the theories and techniques of constructing relational databases to store various biological data, including sequences, structures, genetic linkages and maps, and signal pathways. Introduces relational database query language SQL and the ORACLE database management system, with an emphasis on answering biologically important questions. Summarizes currently existing biological databases. Describes web-based programming tools to make databases accessible. Addresses questions in data integration and security. The future directions for biological database development are also discussed. 4 cr.<\/p>\n

ENG\u00a0BF 831: Translational Bioinformatics Seminar<\/strong><\/h4>\n

After commencing briefly with general introductory material (published reviews and other relevant back-ground information), students will proceed to examine, discuss and evaluate recent papers that directly illustrate the use of bioinformatics either in pre-clinical or clinical research settings. Papers will be drawn from high-impact journals such as Nature, Science, PNAS, Cell, and Science Translational Medicine. Students will take turns presenting the papers to the class and provide a critical review of each. They will also complete a term paper in the form of a research proposal directed to the goal of using bioinformatics to advance a medical intervention (e.g. prognostic, diagnostic or therapeutic). Brief guest presentations by researchers in BUSM laboratories will be arranged as appropriate. Previous guest facilitators have included faculty from the Department of Biostatistics, Section of Computational Biomedicine, and Department of Mathematics and Statistics as well as industry leaders. 2 cr.<\/p>\n

ENG BF 541: Bioinformatics Internship<\/strong><\/p>\n

Each student must complete an internship with a minimum of 400 hours of on-the-job experience (e.g., 10 weeks full-time work in the summer). The format is very flexible, and part-time internships running concurrently with classes or employment are acceptable. While most internships will take place in industrial settings, suitable projects can also be carried out in non-profit or academic research laboratories. In every case the student must submit the MS Internship Approval from to the Program Director before commencing an internship. For this purpose, \u201cbioinformatics\u201d means extensive use of computational tools to analyze, display and\/or archive biological information (usually at the molecular level). The project supervisor must be familiar with the tools employed, and if possible, the position should involve regular interaction with \u201cwet-bench\u201d scientists. 2 cr.<\/p>\n

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Apply Today<\/a><\/h2>\n

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 <\/p>\n","protected":false},"excerpt":{"rendered":"

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