CNG465 INTRODUCTION TO BIOINFORMATICS

Course Code:3550465
METU Credit (Theoretical-Laboratory hours/week):3 (3.00 - 0.00)
ECTS Credit:6.0
Department:Computer Engineering
Language of Instruction:English
Level of Study:Undergraduate
Course Coordinator:Assoc.Prof.Dr. YELİZ YEŞİLADA
Offered Semester:Fall or Spring Semesters.

Course Objectives

The main objective of the course is to provide the student with a solid foundation for conducting further research in bioinformatics. By the end of the course, the students will have learned:

  • the bioinformatics terminology,
  • main bioinformatics problems,
  • and the key methods and tools used in bioinformatics

Course Content

This course covers computatioanl techniques for mining the large amount of information produced by recent advances in biology, such as genome sequencing and microarray technologies. Main topics of the course include: DNA and protein sequence alignment, phylogenetic trees, protein structure prediction, motif finding, microarray data analysis, gene/protein networks.


Course Learning Outcomes

At the end of this course, students will be able to:

  • Understand main computational problems in life sciences.
  • Understand the main terminology used in bioinformatics.
  • Apply statistical analyses on results of algorithms.
  • Understand key methods and tools used in bioinformatics.
  • Design and implement a computational solution to a molecular biology problem

Program Outcomes Matrix

Level of Contribution
#Program Outcomes0123
1Employ knowledge of mathematics, science and engineering to formulate solution to real life computing problems
2Design and conduct experiments, as well as analyze, evaluate and interpret data
3Design systems, components, and/or processes by specifying the requirements and determining the realistic constraints such as ethical and environmental
4Judge professional and ethical principles and integrate them in the working environment
5Have the ability to communicate effectively
6Recognize the need for, and an ability to engage in life-long learning

0: No Contribution 1: Little Contribution 2: Partial Contribution 3: Full Contribution