Bachelor TR-NQF-HE: Level 6 QF-EHEA: First Cycle EQF-LLL: Level 6

Course Introduction and Application Information

Course Code Course Name Semester Theoretical Practical Credit ECTS
MBG4059 Computational Methods in Bioinformatics Fall 3 0 3 6
This catalog is for information purposes. Course status is determined by the relevant department at the beginning of semester.

Basic information

Language of instruction: English
Type of course: Non-Departmental Elective
Course Level: Bachelor’s Degree (First Cycle)
Mode of Delivery: Face to face
Course Coordinator : Dr. Öğr. Üyesi ELIZABETH HEMOND
Course Objectives: The goal of this course is to provide an understanding of the fundamental computational methods used in bioinformatics and the set of algorithms that have important applications both inside and outside of the bioinformatics field.

Learning Outcomes

The students who have succeeded in this course;
1. Recognize the fundamental models of computation useful in modeling nucleic acid and protein sequences.
2. Design and implement algorithms useful for analyzing various molecular biology data.
3. Discuss Genetic Algorithm and its applications in bioinformatics.
4. Discuss Greedy Algorithms and its applications in bioinformatics.
5. Discuss Gibbs sampling and its applications in bioinformatics.
6. Recognize Expectation Maximization and its applications in bioinformatics.
7. Recognize Hidden Markov models and its applications in bioinformatics.
8. Define Bayesian networks and its applications in bioinformatics.
9. Define graphs and its applications in bioinformatics.

Course Content

This course will provide a broad and thorough background in computational methods and algorithms that are widely used in bioinformatics applications. Various existing methods will be critically described and the strengths and limitations of each will be discussed.

Weekly Detailed Course Contents

Week Subject Related Preparation
1) A brief introduction to computational complexity and algorithm design techniques
2) Exact sequence search algorithms
3) Rabin-Karp algorithm, pattern matching, suffix trees
4) Elements of dynamic programming, Manhattan tourist problem, k-band algorithm
5) Approximate string matching, divide and conquer algorithms
6) Branch and bound search
7) Genetic Algorithm
8) Greedy Algorithms
9) Gibbs sampling
10) Expectation Maximization
11) Hidden Markov models
12) Bayesian networks
13) Graphs
14) Review


Course Notes / Textbooks: Haftalık ders notları iletilecektir.
Weekly course notes will be provided.
References: An Introduction to Bioinformatics Algorithms (Computational Molecular Biology), Neil Jones and Pavel Pevzner, MIT Press, 2004.

Evaluation System

Semester Requirements Number of Activities Level of Contribution
Homework Assignments 2 % 10
Project 1 % 15
Midterms 1 % 25
Final 1 % 50
Total % 100
Total % 100

ECTS / Workload Table

Activities Number of Activities Duration (Hours) Workload
Course Hours 14 3 42
Study Hours Out of Class 14 6 84
Presentations / Seminar 5 4 20
Midterms 1 2 2
Final 1 2 2
Total Workload 150

Contribution of Learning Outcomes to Programme Outcomes

No Effect 1 Lowest 2 Low 3 Average 4 High 5 Highest
Program Outcomes Level of Contribution
1) To be able to critically interpret and discuss the theories, the concepts, the traditions, and the developments in the history of thought which are fundamental for the field of new media, journalism and communication.
2) To be able to attain written, oral and visual knowledge about technical equipment and software used in the process of news and the content production in new media, and to be able to acquire effective abilities to use them on a professional level.
3) To be able to get information about the institutional agents and generally about the sector operating in the field of new media, journalism and communication, and to be able to critically evaluate them.
4) To be able to comprehend the reactions of the readers, the listeners, the audiences and the users to the changing roles of media environments, and to be able to provide and circulate an original contents for them and to predict future trends.
5) To be able to apprehend the basic theories, the concepts and the thoughts related to neighbouring fields of new media and journalism in a critical manner.
6) To be able to grasp global and technological changes in the field of communication, and the relations due to with their effects on the local agents.
7) To be able to develop skills on gathering necessary data by using scientific methods, analyzing and circulating them in order to produce content.
8) To be able to develop acquired knowledge, skills and competence upon social aims by being legally and ethically responsible for a lifetime, and to be able to use them in order to provide social benefit.
9) To be able to operate collaborative projects with national/international colleagues in the field of new media, journalism and communication.
10) To be able to improve skills on creating works in various formats and which are qualified to be published on the prestigious national and international channels.