Master of Science in Bioinformatics – Portland

Program Overview

The Master of Science in Bioinformatics provides cross-disciplinary training that prepares graduates to succeed in multiple roles in this new and growing field. 

Combining challenging academics in biology, computer science, and information technology with real-world experience, the Master of Science in Bioinformatics helps students integrate the knowledge, skills, experience, and confidence they need to achieve their goals and make a difference in the world. The program consists of core coursework in computational methods, programming, and statistics, enhanced by electives in molecular biology, biochemistry, molecular modeling, web development, database design and management, data mining, and other related topics. It is structured to provide students with the skills and knowledge to develop, evaluate, and deploy bioinformatics and computational biology applications. The program is designed to prepare students for employment in the biotechnology sector, where the need for knowledgeable life scientists with quantitative and computational skills has exploded in the past decade. 

All Roux Institute programs provide content relevant to the urgent and emerging needs of industry in Maine and the rapidly evolving regional, national, and global economy. Opportunities for experiential learning will be concentrated in Portland, the state of Maine, and the Northeast region. Students are encouraged to pursue co-ops and special virtual Experiential Network projects with the institute’s founding corporate partners, a group of leading employers in Maine. 

  • Portland

    Location

  • Full-Time
    Part-Time

    Commitment

  • 2-3 years

    Duration of Program

Unique Features

  • Courses run in the evening to accommodate working professionals.
  • Students gain up to six months of work experience through co-op positions.
  • With only one additional class, a student can also earn a graduate certificate in data science.
  • 94% employment after graduation in industry or research in the past three years.
  • The program is delivered through a hybrid model of online and on-site learning.
  • Choose from seven concentrations for further specialization:
    • Bioinformatics and Chemoinformatics
    • Bioinformatics Enterprise
    • Biotechnology
    • Data Analytics
    • Health Informatics
    • Medical Health Informatics
    • Omics

Program Objectives

  • Attain core knowledge in bioinformatics programming.
  • Integrate knowledge from biological, computational, and mathematical disciplines.
  • Gain professional work experience via co-op.

Scholarships and aid

$49.6K

Tuition

Estimated Total Tuition

This is based on tuition rates for Academic Year 2019 - 20 and does not include any fees or other expenses. Some courses and labs have tuition rates that may increase or decrease total tuition. Tuition and fees are subject to revision by the president and Board of Trustees at any time.

Generous scholarships

The Roux Institute is offering generous scholarships in its inaugural year to select applicants through its Resilience Scholarship Program. Employees of the institute’s employer partners are eligible for an Employer Partner Scholarship. And Northeastern alumni receive a Double Husky Scholarship — a tuition discount of 25 percent.

Learn more about the Resilience Scholarship Program

Corporate tuition benefits

Many employers subsidize education for their employees. Speak with yours about any tuition benefits your company may offer.

Special military scholarships

For military veterans and servicemembers, a limited number of donor-funded scholarships are available even after all other aid has been awarded to help with commuting costs, childcare, and other costs of living.

Learn more about military scholarships

Federal aid

You can apply for federal aid grants and loans through the Free Application for Federal Student Aid, or FAFSA.

Learn about the FAFSA

Admission Requirements

  • Online application
  • Application fee
  • Unofficial transcripts for all institutions attended; official transcripts required upon acceptance of admission offer
  • Statement of purpose (500–1,000 words): identifies your educational goals and expectations from the program. Please be aware that the university’s academic policy on plagiarism applies to your statement of purpose.
  • Two letters of recommendation from individuals who have either academic or professional knowledge of your capabilities such as a faculty member, colleague, or mentor, preferably one from your current employer
  • GRE scores recommended but not required.
  • Proof of English proficiency for all applicants from one of the following:
    • Degree earned or in progress at a U.S. institution
    • Degree earned or in progress at an institution where English is the only medium of instruction
    • Official exam scores from either the TOEFL iBT, IELTS, or PTE exam

Admission Dates

Fall 2020: August 15, 2020 (domestic students only)

Spring 2021: December 1, 2020 (domestic students only)

Program Curriculum

General Requirements

Master of Science in BIoinformatics General Requirements

Courses and their associated credit hours are listed below.

Computational Methods

BINF 6308 - Bioinformatics Computational Methods 14.00
Offers the first semester of a two-semester sequence on the use of computers in bioinformatics research. Offers students an opportunity to work with current methods and computational algorithms used in contemporary sequence analysis. Teaches practical skills necessary to manage and mine the vast biological information being generated and housed in public databases. Emphasizes the use of Python as the primary computer language and requires students to learn and understand basic computer logic and syntax, including an introduction to scalars, arrays, hashes, decision statements, loops, subroutines, references, and regular expressions. A focus on fundamental skills, including the command line interface found in the Linux operating system, is designed to prepare students for second-semester applications.
BINF 6309 - Bioinformatics Computational Methods 24.00
Designed to build upon the core topics covered in BINF 6308, i.e., use of the computer as a tool for bioinformatics research. Builds upon the Python language fundamentals covered during the first semester but requires students to apply these fundamentals to a semester-long project. The project includes protein family analysis, multiple sequence analysis, phylogeny, and protein structure analysis. Additionally, students have an opportunity to learn to build, load, connect, and query custom MySQL databases, and parse command line flags.

Research and Seminar

BIOL 6381 - Ethics in Biological Research2.00
Discusses ethical issues relevant to research in the biological sciences. Requires student presentations.
BIOT 5219 - The Biotechnology Enterprise2.00
Exposes students to a broad spectrum of concepts and issues that are common to biotechnology companies. Provides an overview of innovation, intellectual property, planning, government regulation, and strategic alliances. Introduces biotechnology entrepreneurship; management; and the legal aspects of science, technology, and research in the biotechnology context.

Statistics and Programming

BINF 6200 - Bioinformatics Programming4.00
Focuses on the fundamental programming skills required in the bioinformatics industry. Focuses on Python and R as the main programming language used. Topics include string operations, file manipulation, regular expressions, object-oriented programming, data structures, testing, program design, and implementation. Includes substantial out-of-classroom assignments.
MATH 7340 - Statistics for Bioinformatics4.00
Introduces the concepts of probability and statistics used in bioinformatics applications, particularly the analysis of microarray data. Uses statistical computation using the open-source R program. Topics include maximum likelihood; Monte Carlo simulations; false discovery rate adjustment; nonparametric methods, including bootstrap and permutation tests; correlation, regression, ANOVA, and generalized linear models; preprocessing of microarray data and gene filtering; visualization of multivariate data; and machine-learning techniques, such as clustering, principal components analysis, support vector machine, neural networks, and regression tree.

Co-op

BINF 6500 - Professional Development for Co-op0.00
Introduces the cooperative education program. Offers students an opportunity to develop job-search and career-management skills; to assess their workplace skills, interests, and values and to discuss how they impact personal career choices; to prepare a professional resumé; and to learn proper interviewing techniques. Explores career paths, choices, professional behaviors, work culture, and career decision making.
BINF 6964 - Co-op Work Experience0.00
Provides eligible students with an opportunity for work experience. May be repeated without limit.

Concentrations

Choose one from the following:

BINF 6400 - Genomics in Bioinformatics
Introduces the field of genomics. With the completion of the Human Genome Project several years ago, there has been an explosion of genetic data collected. Focuses on the bioinformatics tools necessary to analyze large-scale genomic data. Covers topics such as phylogenetic trees, molecular evolution, gene expression profiling, heterogeneous genomic data, as well as next-generation sequencing (NGS) data.
BIOL 6299 - Molecular Cell Biology for Biotechnology
Integrates biochemistry and molecular biology in the cellular context. Includes the organization and replication of genomes, principles and methods for genetic manipulation, the regulation of gene expression, and the structure and function of organelles. Emphasizes protein synthesis, including translation, post-translational modifications, and translocations of proteins within the cells and secretion.
CHEM 6500 - Cheminformatics
Introduces the subject of cheminformatics. Focuses on informatic, or computer, methods to solve chemical problems. Focuses on the approaches to mine data, looking at structural similarities, and evaluating compound designs and libraries for diversity and other characteristics. In addition, briefly discusses molecular modelling of proteins.
BIOT 5225 - Managing and Leading a Biotechnology Company
Covers managing projects and personnel in a technology-based organization Such activities are best carried out by those who combine the technical knowledge of their industry with the insight into the best practices for working with groups of highly educated, and often very experienced people. The biotechnology industry is strongly dependent on the concept that knowledge is always shared and ownership is collective. As the fundamental organizational mantra is teamwork, the principles of managing in this environment are key to achieving important goals. How to accomplish this and make decisions that drive innovation and success have common threads with other technology based industries, but with the added complexity of the scientific challenges facing the biotechnology industry. Restricted to students in the Bouvé College of Health Sciences and in the College of Science or by permission of the program office.
BIOT 5226 - Biotechnology Entrepreneurship
Biotechnology by its very nature is an innovative multidisciplinary industry. This is especially true for the biopharmaceutical industry in which the process of discovering new drugs and new drug targets requires novel approaches to solving difficult questions about disease processes and human health. This course focuses on the essential nature of innovation in the biotech industry, exposes students to the basics of creating startup organizations, explains the key role of business planning in enterprise creation, describes means for assessing risks, making choices from available options and how to measure success. Various business models, outsourcing work and establishing strategic partnerships are examined. Restricted to students in the Bouvé College of Health Sciences and in the College of Science or by permission of the program office.
BIOT 5227 - Launching your Science: Biotechnology Entrepreneurship
Provides a foundation for making financial decisions in the biotechnology industry. Examines accounting methods, forecasting, corporate valuation, exit strategies and drug pipeline economics. Introduces concepts for marketing pharmaceutical products.
BIOT 5120 - Foundations in Biotechnology
Provides an interdisciplinary, state-of-the-art introduction to biotechnology. Covers the molecular foundations of biotechnology, molecular microbiology, receptor pharmacology, drug development processes, biotech process development and scale-up, drug approval and regulatory affairs, genomics, microarray analysis, proteomics, computational biology, molecular modeling, analytical biotechnology, and bioterrorism and biotechnology.
BIOT 5631 - Cell Culture Processes for Biopharmaceutical Production
Covers the principles and concepts involved in the development of mammalian and other types of cell culture processes for the manufacturing of biopharmaceutical products such as monoclonal antibodies and recombinant proteins. Topics include protein expression and clone generation, batch and perfusion processes and media development, bioreactor operations and scale-up, and innovations in cell culture processes. Regulatory concepts include quality assurance in a cGMP environment.
BIOL 6299 - Molecular Cell Biology for Biotechnology
Integrates biochemistry and molecular biology in the cellular context. Includes the organization and replication of genomes, principles and methods for genetic manipulation, the regulation of gene expression, and the structure and function of organelles. Emphasizes protein synthesis, including translation, post-translational modifications, and translocations of proteins within the cells and secretion.
DA 5020 - Collecting, Storing, and Retrieving Data
Studies how to build large-scale information repositories of different types of information objects so that they can be selected, retrieved, and transformed for analytics and discovery, including statistical analysis. Analyzes how traditional approaches to data storage can be applied alongside modern approaches that use nonrelational data structures. Through case studies, readings on background theory, and hands-on experimentation, offers students an opportunity to learn how to select, plan, and implement storage, search, and retrieval components of large-scale structured and unstructured information repositories. Emphasizes how to assess and recommend efficient and effective large-scale information storage and retrieval components that provide data scientists with properly structured, accurate, and reliable access to information needed for investigation.
INSH 5302 - Information Design and Visual Analytics
Introduces the systematic use of visualization techniques for supporting the discovery of new information as well as the effective presentation of known facts. Based on principles from art, graphic design, perceptual psychology, and rhetoric, offers students an opportunity to learn how to successfully choose appropriate visual languages for representing various kinds of data to support insights relevant to the user’s goals. Covers visual data mining techniques and algorithms for supporting the knowledge-discovery process; principles of visual perception and color theory for revealing patterns in data, semiotics, and the epistemology of visual representation; narrative strategies for communicating and presenting information and evidence; and the critical evaluation and critique of data visualizations. Requires proficiency in R.
DA 5030 - Introduction to Data Mining/Machine Learning
Introduces the fundamental techniques for data mining, combining elements from CS 6140 and CS 6220. Discusses several basic learning algorithms, such as regression and decision trees, along with popular data types, implementation and execution, and analysis of results. Lays the data analytics program foundation of how learning models from data work, both algorithmically and practically. The coding can be done in R, Matlab or Python. Students must demonstrate ability to set up data for learning, training, testing, and evaluating.
HINF 5101 - Introduction to Health Informatics and Health Information Systems
Introduces the history and current status of information systems in healthcare: information architectures, administrative and clinical applications, evidence-based medicine, information retrieval, decision support systems, security and confidentiality, bioinformatics, information system cycles, the electronic health record, key health information systems and standards, and medical devices. Requires enrollment in Graduate Health Informatics Program.
HINF 5102 - Data Management in Healthcare
Explores issues of data representation in healthcare systems, including patient and provider identification, audit trails, authentication, and reconciliation. Discusses underlying design of repositories for electronic health records (EHRs) and computerized provider order entry (CPOE) systems. Includes an overview of privacy issues, legislation, regulations, and accreditation standards unique to healthcare.
HINF 6220 - Database Design, Access, Modeling, and Security
Designed to provide an introduction to the theory and application of database management systems. Topics covered include the relational model, basic and intermediate query formulation using structured query language, database design using the entity relational model, and database normalization and optimization. In addition to these traditional topics, this course covers a sample of emerging topics relevant to the healthcare professional, including personal health information, privacy and security considerations, XML as a data model, and clinical data warehousing and mining.
HINF 5105 - The American Healthcare System
Covers the organization, financing, and outcomes of the U.S. healthcare system. Studies opportunities and challenges to improve the cost and quality of healthcare and expand adequate coverage to all. Non–health informatics students may be able to take the course with permission of the program director.
HINF 5110 - Global Health Information Management
Studies the challenges of managing health information systems in the United States, Canada, India, China, the United Kingdom, Saudi Arabia, Singapore, Taiwan, Ghana, and Malawi. Differences in healthcare systems and national regulations make the process slightly different in each country. By exploring environments with varying degrees of regulation, students have an opportunity to think critically about the impact that a nation’s environment has on health information management. Discusses case studies to encourage students to think about health informatics from a managerial perspective across private companies, government, and nongovernment organizations.
HINF 5200 - Theoretical Foundations in Personal Health Informatics
Offers an introduction to and foundation for personal health informatics by reviewing major theories and models of health behavior change and health education at individual, interpersonal, and community levels in a wide variety of settings and populations. Health behavior change is arguably our greatest hope for reducing the burden of preventable physical and mental disease and death around the world. A thorough understanding of health behavior change theories is thus essential to developing and translating personal health interface technologies into practice and policy that can result in more powerful interventions and more robust theories. Emphasizes cultural and health disparities, global applications, advances in health communications, and the use of electronic media (e-health) and mobile media (m-health). Open to students with senior standing with permision of instructor.
BINF 6400 - Genomics in Bioinformatics
Introduces the field of genomics. With the completion of the Human Genome Project several years ago, there has been an explosion of genetic data collected. Focuses on the bioinformatics tools necessary to analyze large-scale genomic data. Covers topics such as phylogenetic trees, molecular evolution, gene expression profiling, heterogeneous genomic data, as well as next-generation sequencing (NGS) data.
BINF 6410 - Proteomics in Bioinformatics
Introduces protein mass spectrometry and the state-of-the-art instrumentation used today. Proteomics data has become an integral part of the biopharmaceutical characterization and approval process. Topics include the current bioinformatic tools used to analyze raw data, protein identification, posttranslational modifications, targeted proteomics, and quantitative proteomics. Covers freely available bioinformatics tools, such as NCBI, UniProt, and ExPASy.
BINF 6420 - Omics in Bioinformatics
Focuses on some of the omics, other than genomics and proteomics, in relation to the bioinformatic tools that exist to analyze data. Provides a brief background on each field of study and then focuses on the current bioinformatics tools used. Topics include transcriptomics (transcription and gene expression), metabolomics (metabolism), glycomics (carbohydrates), lipomics (lipids), and phenomics (phenotypic data). Does not cover genomics and proteomics.

Experiential Learning

Learning integrated with professional experience is a hallmark of Northeastern and the Roux Institute. Students gain a clear understanding of real-world industry needs in Portland, the state of Maine, and the Northeast—and valued workplace skills like communication and teamwork—through assignments at companies and nonprofit organizations. Students can complete a six-week virtual project relevant to their studies through the university’s Experiential Network (XN) of employers. Or they can apply for four- and six-month, full-time co-op positions. All opportunities enable students to build their resumés, expand their professional networks, and chart a path to in-demand careers. 

Contact us to explore your options.

Sometimes it helps to talk things through. Complete the form and our team will connect with you to discuss your questions and options.