Introduction to Computational Biology & Biological Modeling

1 Course Unit

The goal of this course is to develop a deeper understanding of techniques and concepts used in Computational Biology. Both theoretical and practical aspects of a range of methods will be covered. Theoretical aspects will include statistical analysis, modeling, and algorithm design. This course cannot provide a comprehensive survey of the field but focuses on a select core set of topics and data types. We will discuss the genome browser, alignment algorithms, classical and non-parametric statistics, pathway analysis, dimensionality reduction, GWAS, multiple testing and machine learning, with primary focus on biomedical data. UNIX, R and Python will be utilized to learn to execute big data analysis pipelines, including RNA-Seq and DNA-Seq. UNIX and R will be taught from first principles but prior experience in Python will be assumed. You will be provided with a computational (cloud based) platform on which to do all programming and assignments. Prerequisite: Programming experience in Python required. Fall Mutually Exclusive: BIOL 4536 Prerequisite: MATH 1400 AND (BIOL 2510 OR BIOL 5510) 1 Course Unit