This course will explore topics in the field of bioinformatics including tools and resources used by the discipline, including direct experience with the common user environment.

This is an introductory course in languages commonly used in bioinformatics and their application to biological data. We will investigate the use of multiple languages for processing sequence and "-omics" data, building analysis pipelines, integrating languages, managing a variety of biological data types, and providing effective interfaces to existing tools for analysis of these data. The course is largely based around live-code demonstration, in-class assisted coding assignments, and a student-designed final class project.

This course is a faculty-directed student project or research involving laboratory work, computer modeling, or theoretical calculations that could be considered of an original nature. The level of study is appropriate for students in their first three years of study.

This course is a faculty-directed tutorial of appropriate topics that are not part of the formal curriculum. The level of study is appropriate for student in their first three years of study.

This course allows students to assist in a class or laboratory for which they have previously earned credit. The student will assist the instructor in the operation of the course. Assistance by the student may include fielding questions, helping in workshops, and assisting in review sessions. In the case of labs, students may also be asked to help with supervising safety practices, waste manifestation, and instrumentation.

This course is an introduction to the probabilistic models and statistical techniques used in computational molecular biology. Examples include Markov models, such as the Jukes-Cantor and Kimura evolutionary models and hidden Markov models, and multivariate models use for discrimination and classification.

This course is a faculty-directed student project or research involving laboratory or field work, computer modeling, or theoretical calculations that could be considered of an original nature. The level of study is appropriate for students in their final two years of study.

This course is a faculty-directed tutorial of appropriate topics that are not part of the formal curriculum. The level of study is appropriate for student in their final two years of study.

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.

This course is a faculty-directed, graduate level tutorial of appropriate topics that are not part of the formal curriculum.