Bioinformatics MS

Bioinformatics, MS degree, typical course sequence

Course Sem. Cr. Hrs.
First Year
BIOL-625
Ethics in Bioinformatics
This course will be focused on individual and organizational responsibilities in bioinformatics research, product development, product commercialization and clinical and consumer genetic testing. (This course is restricted to students in the BIOINFO-MS, BIOINFO-BS/MS program.) Lecture 3 (Fall).
3
BIOL-630
Bioinformatics Algorithms
Bioinformatics Algorithms will focus on the types of analyses, tools, and databases that are available and commonly used in Bioinformatics. The labs will apply the lecture material in the analysis of real data through computer programming. (This course is restricted to students in the BIOINFO-MS, BIOINFO-BS/MS program.) Lab 3, Lecture 2 (Fall).
3
BIOL-635
Bioinformatics Seminar
The course provides opportunities for students and faculty to develop and share professional interests while discussing current trends and developments in bioinformatics. Material for this course will be drawn from the current scientific literature. (This course is restricted to students in the BIOINFO-MS, BIOINFO-BS/MS program.) Lecture 3 (Fall).
3
BIOL-671
Database Management for the Sciences
Students will learn to create and maintain efficient relational databases for use in modeling and analysis in the sciences. Topics will include an introduction to relational algebra, SQL, and advanced relational designs. (Graduate Science) Lecture 2, Studio 2 (Spring).
3
BIOL-672
Computational Statistics and Data Science Methods  
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. (Prerequisites: STAT-145 or equivalent course or graduate student standing.) Lecture 2, Studio 2 (Fall, Spring).
3
BIOL-694
Molecular Modeling and Proteomics
This course will explore two facets of protein molecules: their separation and their structure. The structure component will build upon information from earlier bioinformatics courses. Protein separation techniques will be addressed in lectures with descriptions of 2D gel electrophoresis and chromatography. Algorithms of protein secondary structure prediction will be implemented. Experimental techniques for tertiary structure determination such as NMR will be covered. The course will also include the analysis of inter-molecular interactions, such as ligand/receptor pairing, by employing software that permits modeling of molecular docking experiments. (Prerequisites: BIOL-330 or equivalent course or graduate student standing.) Lab 2, Lecture 2 (Spring).
3
BIOL-790
Research and Thesis
Masters-level research by the candidate on an appropriate topic as arranged between the candidate and the research advisor. (This course requires permission of the Instructor to enroll.) Thesis (Fall, Spring, Summer).
2
 
Graduate Electives*
6
Second Year
BIOL-790
Research and Thesis
Masters-level research by the candidate on an appropriate topic as arranged between the candidate and the research advisor. (This course requires permission of the Instructor to enroll.) Thesis (Fall, Spring, Summer).
4
Total Semester Credit Hours
30

* Any graduate-level course deemed related to the field of Bioinformatics by the program director.