TYPICAL STUDENT BACKGROUNDS
MS in Biology with a focus in Bioinformatics & Genomics
9 months coursework + 9 months paid internship
PRIORITY APPLICATION DEADLINE
Summer Term: February 15
Bioinformatics is data science applied to biology. This interdisciplinary field intersects biology, computer science, and math. A bioinformatician’s work happens primarily at the computer, and includes acquiring, cleaning, managing, aggregating, storing, mining, analyzing, visualizing, and communicating insights about large amounts of data. Due to the nature of the work, many bioinformaticians enjoy flexible work hours and have the option to work remotely.
We are in the postgenomic era, where the bottleneck is no longer in the creation of genomic sequence data, but rather in the extraction and distillation of meaning from the raw sequence data. Decoding the enormous amount of information within genomic data requires the ability to perform the bioinformatics, as well as an understanding of the inner workings of areas such as genetics and nucleic acid, both in form and function. To truly understand what the data are and where they come from, bioinformaticians delve into the exquisite and complex field of genomics.
The societal impacts of bioinformatics range from foundational scientific insights, to environmental remedies, to improvements in human health outcomes. A bioinformatician might work within the fields of precision medicine, immunology, cancer biology, microbial ecology, evolutionary biology, neuroscience, or bioengineering, to name a few. Their skills are transferrable to a large number of data science fields.
Students in this track typically enter with a bachelor’s degree in Biology, Biochemistry, Computer Science, Mathematics, Physics, or related discipline. Students will earn a Master of Science in Biology with a focus in Bioinformatics and Genomics.
All programs begin coursework during the summer rather than fall term. To find out more, contact Allie Hardman at firstname.lastname@example.org.
Common Job Titles for Bioinformatics Graduates
Students who complete the Bioinformatics Track work in a wide variety of data-centric roles in government, clinical, and academic labs; and in research and development in industry. Alumni from this track develop skills which have been successfully transferred to a wide variety of data science and analyst roles. A selection of roles is highlighted below. These example positions share core competencies: significant overlap of responsibilities between different roles is possible.
Data Scientist, Research Data Specialist: Manage data, and design and build data structures that enable efficient storage, retrieval, and manipulation of data. Aggregate and deploy analysis on different data types. Example: Integrating omics and population meta data into a database and create a predictive model. Build a machine learning classifier to identify tumor vs non-tumor cells.
Bioinformatics Software Engineer, Software Test Engineer, Programming Analyst: Develop portable and reproducible software at a larger scale for bioinformatics or biological research. Create, modify, manage, and implement custom analysis pipelines tailored to the needs of the consumer, but do not deal extensively with questions underlying the biology. Create and optimize software for life science applications with customers in research on clinical diagnostics, or engineer software for biological instrumentation. Example: Write programs for sequence alignment, variant calling, or metabolomic data analysis. Develop software to analyze proprietary diagnostic assays. Write software for biological instrumentation (sequencer, cell imager, etc).
Student and Alumni Success Metrics
Students who have completed 9-month paid internships since 1998
Average annualized internship compensation for offers in past year
Graduates who are employed in their field within three months
First 3 Terms: Summer, Fall, and Winter Terms
Students complete core coursework and optional electives. Students have the opportunity to attend a scientific meeting (Genomics in Action), present a poster, network, and interview with partners to line-up internships.
Second 3 terms: Spring, Summer, and Fall Terms
The majority of students fulfill their internship requirement through employment with external partners beginning in April and ending in December. In this scenario, students enroll in 10 internship credits per quarter during the spring, summer, and fall terms.
The majority of bioinformatics students complete their master’s degree in 18 months.
Students in the bioinformatics track complete a total of 60 credits, including 9 months (30 credits) of coursework and 9 months (30 credits) at an internship.
- Core courses: There are seven required, core courses for a total of 28 credits.
- Professional development: All students complete a two, one-credit professional development courses.
- Internship: The culmination of the curriculum is three terms of internship credits for a total of 30 credits.
Details about the curriculum are provided in the sections below.
Graded Coursework (Summer, Fall, and Winter Terms)
The curriculum for the Bioinformatics and Genomics Master’s Track has been developed to prepare students for entry level bioinformatics and data science positions in academic, government, clinical, and industrial labs. Students typically spend nine months on-campus developing critical foundational skills and building a community of support with the instructors and fellow students in the program. Students apply these skills, and develop new skills, on the job during a nine-month paid internship. The coursework is set up for students to engage with a project completely, from design to conclusion, so they understand and appreciate the data generation process. Bioinformaticians must be able to communicate with, and function within, a multidisciplinary team that often includes bench scientists, statisticians, software engineers, and clinicians. Students participate in regular journal clubs that are interwoven into their coursework where they evaluate, present, and discuss scientific literature. Internship hiring partners value our students’ ability to communicate to different audiences and their contextual knowledge of genomics experiments. These skills complement the fundamental technical skills that are reinforced through repeated relevant practice, such as programming algorithms that a student would use in their internship and publishing these algorithms on their public code repository.
- BI 621: Computational Methods in Genomic Analysis (4 Credits, Graded) – Summer
The primary goal of this course is to teach students to think algorithmically. Students learn how to write scripts using logic in both the Bash and Python programming languages. Students manage and analyze next generation sequencing (NGS) data. Students also learn how to navigate the UNIX/Linux command line and utilize command line tools on both their own computers, and UO’s high performance computer cluster Talapas.
Think Python: How to Think Like a Computer Scientist. Allen B Downey. O’ Reilly. Second Edition. 2015. It is available as a free ebook here.
A Primer for Computational Biology. Shawn T. O’Neil. Oregon State University Press. We lend students this book from the Bioinformatics Library. It is also available as a free ebook here.
- BI 622: Genomics Techniques (4 Credits, Graded) – Summer
In this course, students learn about experimental design, genomics history and technology, and the molecular techniques for preparing high quality nucleic acid sequencing libraries for both short and long read sequencing. The course also aims to improve students’ scientific communication skills (both written and oral).
Barker, Kathy. At the Bench: A Laboratory Navigator. Cold Spring Harbor, NY: Cold Spring Harbor Laboratory, 2005. Print. We lend students this book from the Bioinformatics Library.
Watson, Baker, Bell, Gann, Levine, and Losick. Molecular Biology of the Gene. 7th ed. Cold Spring Harbor: Cold Spring Harbor Laboratory, 2014. Print. We lend students this book from the Bioinformatics Library.
- BI 623: Topics in Genomic Analysis (4 Credits, Graded) – Summer
Students will be introduced to wide-ranging topics including phylogenetics, comparative genomics, transcriptome assembly and assessment, metagenomics, RNA-seq preprocessing, alignment and enumeration, and differential gene expression analysis, as well as an introduction to the statistics used in these analyses. Students will learn how to write scripts to manage and visualize data using the integrated development environment RStudio with the R programming language, create professional reports using R Markdown, and continue honing Python scripting skills.
- BI 624: Genomics Research Lab (4 credits, Graded) – Fall
In this course, students will write algorithms to analyze NGS data. In addition to expanding upon topics presented in the third summer class (BI 623), students will also be exposed to new topics in genomics analysis, including single-cell RNA-sequencing and statistical classification methods. Students begin working on team projects using real world data supplied by UO and external partner labs. They also practice collaborating on code and version control using GitHub and git. Scientific communication to both expert and general audiences is emphasized.
- BI 625: Advanced Genomics Analysis (4 credits, Graded) – Winter
Students continue to build on the projects from the fall term, as well as gain exposure to a number of special topics/projects throughout the term including object-oriented programming, structure query language (SQL), PacBio genome assembly, creating custom figures using Python graphics libraries, and the use of containers and cloud computing. Student groups design and present a poster to prospective employers during the track’s annual scientific conference Genomics in Action.
- BI 610: Advanced Biological Statistics I & II (4 credits each, Graded) – Fall and Winter
This two-quarter course aims to provide students with practical understanding of, and experience with, core concepts and methods in modern data analysis. The focus is on biological data, but skills will be transferable to other disciplines. Students will become familiar with major topics in univariate and multivariate statistics, analysis of large data sets, and Bayesian analysis. There is a particular emphasis on modeling and conceptual understanding of statistical noise and uncertainty. The course is advanced in that it moves through the material quickly with the goal of providing a solid foundation for subsequent learning. Students will learn to use the powerful statistical programming language R, and the flexible modeling language Stan.
Professional Development (Fall and Winter Term)
- BI 630/631: Professional Communication and Development for Scientists I & II (1 credit each, Pass/No Pass) – Fall and Winter
In this course, students learn best practices for professional scientific communication. No matter how long you’ve been out in the world, working well with other people is the key to success – regardless of the sector. Hands-on training in communication, leadership, and teamwork differentiates this program from others and has helped to successfully launch the careers of Graduate Internship Program students for 20+ years. Core elements include: composing a competitive resume, giving impactful answers during behavioral and technical interviews, and building a strong professional network. Students prepare for internships through a variety of practical workshops that lead to proficiency in these important skills.
Internship (Typically Spring, Summer, and Fall Terms)Students complete paid, 9-month, master’s level internships as part of their degree requirements. Many recent internship positions have been remote/work from home.
- BI 601: Internship Credits. (Total of 30 credits at 10 credits/term, Pass/No Pass)Within an academic, clinical, industrial, or national lab setting, students gain hands-on experience in the application of their knowledge. Each term, students write a review paper (must be approved by their supervisor) to demonstrate advancement of technical knowledge and develop written communication skills. Strong communication between the program, intern, and intern host organization is emphasized and critical for success. At least two formal evaluations will be performed; one to evaluate the internship experience (between the student and UO instructor) and another to evaluate the intern’s job performance (between the manager and UO instructor). Feedback from these meetings will be distilled and conveyed to the relevant parties.
Optional Graduate Level Elective Course(s) – Fall and Winter Terms
Students may choose to take one or more optional electives during fall and/or winter terms. We recommend that students consult with program faculty when considering electives. Some examples of classes taken by our students in previous years include:
- BI 608: Introduction to Machine Learning (1 Credit, Pass/No Pass) – optional elective, term offered TBD. This workshop introduces students to machine learning and how to apply machine learning techniques to analyze biomedical image data. Students learn to construct models using the Python packages TensorFlow and Keras.
- Bi 525 – Advanced Molecular Biology Research Lab
- Bi 527 – Molecular Genetics of Human Disease
- BI 533 – Bacterial-Host Interactions
- For students with more extensive computer science background or those looking for a challenge:
- CIS 551 – Database Processing
- CIS 571 – Intro to Artificial Intelligence
- CIS 610 – Big Data and Data Science
- For more information visit:
The new, innovative, Knight Campus serves as home base for instruction, personal belonging storage, and lounging for students in the bioinformatics and genomics track. Hands-on training occurs in the new, state-of-the-art instructional lab and classroom facilities. The life sciences lab includes:
- Essential molecular biology equipment: updated pipets, centrifuges, PCR machines and more
- Several Qubit 4 fluorometers
- A Nanodrop One spectrophotometer
Students make extensive use of UO’s multimillion dollar supercomputer, Talapas, one of the fastest academic computing clusters in the Pacific Northwest. Students also utilize leading edge DNA sequencers and instrumentation in the university’s Genomics and Cell Characterization Facility. See facilities page for more information.
The program has multiple subject-matter experts who serve as instructors. Current instructors include:
Dr. Stacey Wagner, Director of Quantitative Life Science Tracks, Knight Campus Graduate Internship Program, University of Oregon
Dr. Leslie Coonrod, Sr. Lecturer and Associate Director of Quantitative Life Science Tracks, Knight Campus Graduate Internship Program, University of Oregon
Peter Batzel, Lecturer, Bioinformatics & Genomics Track, Knight Campus Graduate Internship Program, University of Oregon
Jason Sydes, Lecturer, Bioinformatics & Genomics Track, Knight Campus Graduate Internship Program, University of Oregon
Dr. Lisa Bramer, Lecturer, Bioinformatics & Genomics Track, Sr. Data Scientist, Pacific Northwest National Laboratory
Maggie Weitzman, Research Assistant, GC3F, University of Oregon
Dr. Jake Searcy, Associate Director of AI, Research Advanced Computing Services, University of Oregon
Student typically have backgrounds in Biology, Biochemistry, Computer Science, Chemistry, Mathematics, Physics, and related disciplines. Competitive applicants have previous research experience or project-based coursework experience and strong performance in upper division undergraduate course work, and experience in one or more of the following subject areas: biology, programming, mathematics. If you are interested in the program/knowing more, but have different preparation, please contact us to see if your unique experiences are a good fit. Please note that these are recommendations; we are happy to answer questions about your competitiveness for this program as all students bring different experiences.