SOCIOLOGY
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
GEN4059	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
Recommended Optional Program Components:	There is none.
Course Objectives:	The goal of this course is to provide an understanding of the fundamental computational methods used in bioinformatics and set of algorithms that have important applications in bioinformatics and also have several other applications outside of bioinformatics.

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 through 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

Sources

Course Notes / Textbooks:	Relevant course notes or hand-outs will be supplied.
References:	1)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	% 25
Midterms	1	% 25
Final	1	% 40
Total		% 100
PERCENTAGE OF SEMESTER WORK		% 35
PERCENTAGE OF FINAL WORK		% 65
Total		% 100

ECTS / Workload Table

Activities	Number of Activities	Duration (Hours)	Workload
Course Hours	14	3	42
Study Hours Out of Class	14	7	98
Midterms	1	2	2
Final	1	2	2
Total Workload			144

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 learn and compare major sociology perspectives, both classical and contemporary, and apply all of them to analysis of social conditions.
2)	To be able to identify the basic methodological approaches in building sociological and anthropological knowledge at local and global levels
3)	To be able to use theoretical and applied knowledge acquired in the fields of statistics in social sciences.
4)	To have a basic knowledge of other disciplines (including psychology, history, political science, communication studies and literature) that can contribute to sociology and to be able to make use of this knowledge in analyzing sociological processes
5)	To have a knowledge and practice of scientific and ethical principles in collecting, interpreting and publishing sociological data also develop ability how to share this data with experts and lay people, using effective communication skills
6)	To develop competence in analyzing and publishing sociological knowledge by using computer software for quantitative and qualitative analysis; and develop an attitute for learning new techniques in these fields.
7)	To identify and to have a knowledge of the theories related to urban and rural sociology and demography, and political sociology, sociology of gender, sociology of body, visual sociology, sociology of work, sociology of religion, sociology of knowledge and sociology of crime.
8)	To have knowledge of how sociology is positioned as a scientific discipline from a philosophical and historical perspective
9)	To have the awareness of social issues in Turkish society, to develop critical perspective in analysing these issues and to have a knowledge of the works of Turkish sociologists and to be able to transfer this knowledge
10)	To have the awareness of social issues and global societal processes and to apply sociological analysis to development and social responsibility projects
11)	To have the ability to define a research question, design a research project and complete a written report for various fields of sociology, either as an individual or as a team member.
12)	To be able to transfer the knowledge gained in the areas of sociology to the level of secondary school.