The emerging field of Bioinformatics is an interdisciplinary research area that teaches how to analyze large-scale datasets and apply that analysis to solve problems in a variety of professional, medical, and scholarly fields.
Bachelor of Science in Bioinformatics
TThe Bachelor of Science in Bioinformatics will train students in basic concepts and skills to perform computational analysis of biological data - including the human genome. Students will be able to participate in research with Institute faculty and other faculty in departments across the campus. Institute core and adjunct faculty teach many of the informatics (INFO) classes offered each semester.
Learn more about the career opportunities available for bioinformatics majors here.
For more information about the Bioinformatics program, contact Elliot Lefkowitz, Program Co-director
Admissions Criteria
Due to the interdisciplinary nature of the Bioinformatics program, admission criteria is higher than those for either biology or computer science.
Learn more about Admissions Criteria
Plan of Study
The plan of study for this program includes courses in biology, chemistry, computer sciences, mathematics, informatics and core curriculum courses.
Click here for the Plan of Study
Undergraduate Informatics Courses
Click here for course listing of Informatics courses offered at the undergraduate level.
Below is a listing of classes offered during the upcoming semesters.
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Spring 2024
INFO 101: Intro Bioinformatics Sem
CRN: 42206
Min CR: 1
Schedule Type: Lecture/Lab
Instructor: Elliot J Lefkowitz
DescriptionFaculty-led seminar course that exposes students to cutting edge research topics and career opportunities in the field of bioinformatics. Students will read assigned articles and be prepared for discussion. Subject matter varies by term and students will take this course during multiple semesters for a maximum of two credits.
INFO 403: Bioinformatics-II
CRN: 45983
Min CR: 3
Schedule Type: Lecture
Instructor: Ryan L Melvin (P)
PrerequisitesUndergraduate level INFO 302 Minimum Grade of C and Undergraduate level CS 303 Minimum Grade of C
DescriptionDevelopment of computational algorithms to solve biological questions with a significant problem-solving component. This includes computational techniques such as dynamic programming, optimization, hidden Markov models, graph algorithms, and other mathematical and statistical approaches. In addition, data mining and machine learning methods in computational biology will be covered.
INFO 497: Research in Bioinformatics
CRN: 43717
Min CR: 0
Max CR: 4
Schedule Type: Undergraduate Research
Instructor: Elliot J Lefkowitz (P)
PrerequisitesUndergraduate level PSDO 200 Minimum Grade of C and Undergraduate level CS 103 Minimum Grade of C
DescriptionResearch in Bioinformatics for non-honors students under the supervision of a faculty sponsor.
INFO 498: Honors Bioinformatics Research
CRN: 43718
Min CR: 0
Max CR: 4
Schedule Type: Capstone Course Undergraduate Research
Instructor: Elliot J Lefkowitz (P)
PrerequisitesUndergraduate level PSDO 200 Minimum Grade of C and Undergraduate level CS 103 Minimum Grade of C
DescriptionHonors Research is an innovative course that will provide undergraduate students with an opportunity to engage in rigorous scholarly practice of the core bioinformatics skills necessary for performing independent research. Program faculty will closely work with students to identify a project that explores an area of interest for the student based on the integration of prior learning. Students will be performing bioinformatics analyses on laboratory data or publicly available large-scale data, incorporate quality control and develop software pipelines.
INFO 499: Bioinformatics Capstone
CRN: 44674
Min CR: 3
Schedule Type: Lab Capstone Course
Instructor: Elliot J Lefkowitz (P)
PrerequisitesUndergraduate level INFO 403 Minimum Grade of C and Undergraduate level INFO 404 Minimum Grade of C and Undergraduate level PSDO 200 Minimum Grade of C
DescriptionStudents will be allowed to rotate to different program faculty or continue with their mentor from BY/CS 498 Bioinformatics Capstone Research I. With close mentoring and guidance from program faculty, the student will identify a capstone project or continue their existing bioinformatics project. The capstone project is expected to culminate in a formal scholarly work reflecting integration of the scientific knowledge gained through the project. The scholarly work may take the form or a written manuscript or semester report.
INFO 602: Algorithms in Bioinformatics
CRN: 42384
Min CR: 3
Schedule Type: Lecture
Instructor: Ryan L Melvin (P)
PrerequisitesGraduate level INFO 601 Minimum Grade of C
DescriptionThis course introduces various fundamental algorithms and computational concepts for solving questions in bioinformatics and functional genomics. These include graph algorithms, dynamic programming, combinatorial algorithms, randomized algorithms, pattern matching, classification and clustering algorithms, hidden Markov models and more. Each concept will be introduced in the context of a concrete biological or genomic application. A broad range of topics will be covered, ranging from genome annotation, genome reconstruction, microarray data analysis, phylogeny reconstruction, sequence alignments, to variant detection.
INFO 603: Biological Data Management
CRN: 46894
Min CR: 3
Schedule Type: Lecture Online
Instructor: Jake Y Chen (P)
PrerequisitesGraduate level INFO 601 Minimum Grade of C
DescriptionThe introduction of biological data management concepts, theories, and applications. Basic concepts such as relational data representation, relational database modeling, and relational database queries will be introduced in the context of SQL and relational algebra. Advanced concepts including ontology representation and database development workflow will be introduced. Emerging big data concepts and tools, including Hadoop and NoSQL, will be introduced in the context of managing semi-structured and unstructured data. Application of biological data management in biology will be covered using case studies of high-impact widely used biological databases. A class project will be required of all participants.