BMI 5330 Introduction to Bioinformatics
3 semester credit hours
Lecture contact hours: 2; Lab contact hours: 3
Web-based and classroom instruction
Prerequisite: Consent of instructor
The course gives a comprehensive entry-level introduction to bioinformatics. It covers a wide variety of topics in bioinformatics, including but not limited to genome analysis, transcription profiling, protein structure and proteomics. Two major goals are 1) to help students understand the scope, basic concepts and theory of bioinformatics; and 2) to become familiar with tools for bioinformatics-related data analysis. Using software tools will be a major component of the course but advanced programming skills are not required (see minimum programming skills requirements below). A laptop computer is necessary to use the bioinformatics software and tools in class and while performing the research tasks for the course project.
Upon successful completion of the course, students will:
- Describe bioinformatics including basic concepts, ethics, and its role in translational research and clinical practices.
- Identify and visualize whole genome sequences for a specific disease.
- Analyze genetic variations in healthy and diseased genomes that affect health and disease.
- Examine gene expression and gene annotations and their relation to disease phenotypes.
- Integrate proteomics and gene regulation data to determine its effect on gene expression.
- Diagram biological networks for visualizing gene expression, regulation, and protein interactions in disease.
- Review advanced topics in bioinformatics such as metagenomics, microbiomics, etc.
The minimum programming skills required for this course are the ability to:
- Open data files (tab, space, or comma delimited),
- Read the contents of a file as string and/or numeric values and load them into arrays and variables.
- Using loops to perform analysis on data that are loaded into arrays.
- Computation on strings (comparison, counting, etc.) to compare DNA and RNA sequences.
- Computation of simple statistics (e.g. means and correlations) using numerical and categorical data.
- Opening a file to write data into.
- Very importantly, you are comfortable with Linux command line and basic files to make/delete and navigate directories, explore files in the command line
- On Linux command line, you are comfortable with installing and executing programs and following tutorials for installing programs from online sources
This course does not require a specific programming language to be used. Students can use the programming language they are most comfortable working with. The most popular ones are R, matlab, java, python, and perl. The use of other programming languages is acceptable as long as the tasks above can be accomplished.