Bioinformatics Curriculum

Certificate Program Learning Outcomes

  1. Understand both basic and advanced concepts of Bioinformatics.

  2. Understand the wide range of possible bioinformatics applications in the health services sector and the agriculture industry and use bioinformatics tools in a Unix computer environment for analysis of high throughput sequencing and other omics data.

  3. Perform problem solving in academic and industrial environments on projects related with generation and analysis of omics data.

  4. Work effectively as a member of a bioinformatics team.

  5. Organize and pursue a scientific or industrial research project in fields such as biological databases; algorithms in bioinformatics; biocomputing; basic probability and statistics in bioinformatics; genome architecture and functional genomics; and omics applications in precision health and agriculture.

  6. Write scientific and industrial R&D reports to a professional standard, equivalent to that of a level one scientist.

  7. Derive and apply solutions from knowledge of sciences, technology, and mathematics.

  8. Apply skills in computer science and programming for life scientists without prior computational experience through use of Unix systems, algorithms, data structures, string manipulation, basic concepts of software development and programming languages.

  9. Demonstrate knowledge in basic concepts of biological and genetic information and master further data analysis skills in a focused topic area, such as bioinformatic algorithms, genomics and medical informatics. Demonstrate competency with programming languages such as Perl and R.

Students Typing at Their Computers

Course Work

The University of Lethbridge Graduate Certificate in Bioinformatics includes 4 courses and a total of 12.0 credit hours.  These include introductory, intermediate and advanced bioinformatics courses (9.0 credit hours in total) and your choice of an elective (3.0 credit hours).

Your choice of an elective from the following list:

- Biochemistry 5990 - Independent Study

- Computer Science 5310 - Studies in Computational Intelligence

- Neuroscience 5850 - Workshop in Computational Neuroscience

Full Course Descriptions

Biochemistry 5400 - Essentials of Bioinformatics

3.0 credit hours

Introduction to the analysis and interpretation of omics data. Introduction to biological databases and simple programming languages that will enable students to conduct basic analysis of genomes, transcriptomes, epigenomes, and proteomes.

Biochemistry 5410 - Practical Bioinformatics

3.0 credit hours

Practical exercises and lectures on basic digital literacy. Extensive practice in Unix systems and basic programming languages with an emphasis on those relevant for omics related applications such as R and Perl. Interactive tasks to help students acquire a basic understanding of the principles of generation and analysis of genetic information.

Biochemistry 5420 - Advanced Bioinformatics

3.0 credit hours

Next-generation sequence analysis of biomolecules, and analysis and interpretation of omics data. Computational biomarker and drug discovery, various aspects of personalized medicine, biobanks, and ethics related to bioinformatics. Designing and implementing a bioinformatics analysis project, and the communication and project management skills associated with this.

Biochemistry 5990 - Independent Study

3.0 credit hours

A course for which credit is earned through individual study under the supervision of an instructor.

Computer Science 5310 - Studies in Computational Intelligence

3.0 credit hours

This course will discuss the algorithmic and machine learning (ML) techniques in Bioinformatics, their applications and the software tools. Primer on molecular biology, motif finding, median string, global and local sequence alignment, partial and double digest problem, genome rearrangements, phylogeny problems (large and small parsimony), RNA folding. We will also examine protein folding, comparative genomics, SNPs, analysis of microarray data using ML. Programming activities will include implementation of some of the algorithms on multi-core machines, GPUs, and clusters.

Neuroscience 5850 - Workshop in Computational Neuroscience

3.0 credit hours

This workshop aims to give students an introduction to the theory and practical application of computational methods for the analysis of neurobiological data. The workshop is composed of lectures, hands-on use of key analysis methods, and a course project.

General topics: Introduction to MATLAB, Data visualization, Overview of analysis methods for neurobiological signals, Fundamental concepts in signal processing, Working with discrete data (action potentials, behavioral events, etc.), Methods for working with multiple simultaneous signals (e.g. EEG signals), Analysis of imaging data.