Graduate Course Descriptions

The following directory lists the graduate courses which the University expects to offer, although the University in no way guarantees that all such courses will be offered in any given academic year, and reserves the right to alter the list if conditions warrant. Click on the links below for a list of courses in that subject area. You may then click “View Classes” to see scheduled classes for individual courses.

5325. Computational Genomics Practicum

2.00 credits

Prerequisites: None.

Grading Basis: Graded

A practical introduction to computational genomics focusing on methods for processing/analyzing Next Generation Sequencing (NGS) data. 1. Programming: Introduction to the Linux command line, elements of Python and R programming. 2.Genomics software tools for performing sequence read-alignments, transcript-expression profiling, and robust procedures for gauging differential gene expression. 3. Methods for genome assembly, genome variation detection, motif-finding, and data-visualization. 4. Statistical topics include: probability distributions, central limit theorem, hypothesis testing, linear models, and dimensionality reduction.

No classes found.