This is the OER material for lecture and lab for the first part of BIO3352 (Fall 2019).

Course Description

This course is a continuation of Bioinformatics I. Topics include gene expression, microarrays, next- generation sequencing methods, RNA-seq, large genomic projects, protein structure and stability, protein folding, and computational structure prediction of proteins; proteomics; and protein-nucleic acid interactions. The lab component includes R-based statistical data analysis on large datasets, introduction to big data analysis tools, protein visualization software, internet-based tools and high-level programming languages.

Prerequisites: BIO 3350 and (MAT 1372 or MAT 2572)

Course Objectives

Upon completion of the course, the students will be able to:

  1. Use well-established and widely used bioinformatics tools and platforms (e.g., ClustalW, R, GSEA, Gene Ontology).
  2. Use basic programming languages (e.g., Unix bash commands, AWK) to perform straightforward computational tasks.
  3. Understand the theory and statistical background of commonly available bioinformatics tools, so that they are able to judge the validity of the results provided by these tools.
  4. Navigate through internet-based biological databases and genomic browsers.
  5. Use online resources to search for scientific literature in the field of bioinformatics.
  6. Comprehend specific methodologies and results described in current bioinformatics literature.