ECONOMICS
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
MBG1002	Introduction to Bioinformatics	Spring	3	0	3	5

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 Lecturer(s):	Prof. Dr. SÜREYYA AKYÜZ Dr. Öğr. Üyesi SERKAN AYVAZ
Recommended Optional Program Components:	There is none.
Course Objectives:	This course aims to prepare the students to work in the interdisciplinary area, bioinformatics that marry the advances in high-performance computing with the exploiting information resources of the human genome and related data.

Learning Outcomes

The students who have succeeded in this course;
1. Recognize the working in interdisciplinary teams of biologists, biochemists, medical researchers, geneticists, and computer engineers.
2. Perform sophisticated searches over enormous databases, and to interpret results.
3. Perform genomic comparisons, display genes and large genomic regions in Genome Browsers.
4. Recognize the basic bioinformatics problems and their solutions, including: fragment assembly, gene finding, protein folding and microarray studies.
5. Anayze the results in probabilistic terms using statistical significance.
6. Recognize the sequencing techniques, inherent computational problems, possible solutions.
7. Define Markov Model building and its usage for gene prediction.
8. Define computational methods for analysis of microarray data, and discuss the interpretations of gene expression from this data.
9. Discuss ethical, legal, and social issues associated with the Human Genome Project and its outcomes.

Course Content

Bioinformatics is a rapidly growing field that integrates molecular biology, statistics, and computer science. This course is devoted to the mathematical models and computer algorithms of DNA and protein sequence analysis. In this course, the students will learn many of the popular tools for performing bioinformatics analysis and you will be introduced to the thinking that drives these algorithms. Various existing bioinformatics methods will be critically described and the strengths and limitations of each will be discussed.

Weekly Detailed Course Contents

Week	Subject	Related Preparation
1)	Introduction: Probability and statistics in a nut shell.
2)	Analysis of nucleic acid and protein sequences.
3)	Molecular Biology Databases on the Web.
4)	Bioinformatics softwares on the internet
5)	How the Genome is Studied, Maps and Sequences, The Human Genome Project
6)	Sequencing: Next Gen, Exome, Shotgun
7)	Fragment Assembly Problem; Sequence Alignment Models: Shortest Common Superstring, Reconstruction, Multicontig, Graph Model
8)	Restriction mapping: a) Double Digest Problem, b) Partial Digest Problem
9)	Computational Gene Hunting, Gene finding methods; sequence patterns, Hidden Markov Models.
10)	Bioinformatics approaches to gene expression
11)	Protein folding problem
12)	Genome Rearrangements
13)	Suffix trees I
14)	Suffix trees II

Sources

Course Notes / Textbooks:	Biyoinformatik ders notları haftalık olarak verilecektir. Course material will be supplied weekly.
References:	1) Pevsner J., Bioinformatics and Functional Genomics, Wiley-Liss, 2009 2) Mount D.W., Bioinformatics: Sequence and Genome Analysis (2nd edition), Cold Spring Harbor Laboratory Press, 2004 3) Krane D.E., Raymer M.L., Fundamental Concepts of Bioinformatics, Benjamin Cummings, 2003 4) Setubal C., Meidanis J., Introduction to Computational Molecular Biology, PWS Publishing, 1997"

Evaluation System

Semester Requirements	Number of Activities	Level of Contribution
Homework Assignments	2	% 15
Project	1	% 20
Midterms	1	% 25
Final	1	% 40
Total		% 100
PERCENTAGE OF SEMESTER WORK		% 40
PERCENTAGE OF FINAL WORK		% 60
Total		% 100

ECTS / Workload Table

Activities	Number of Activities	Duration (Hours)	Workload
Course Hours	14	3	42
Study Hours Out of Class	14	5	70
Project	1	10	10
Midterms	1	2	2
Final	1	2	2
Total Workload			126

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)	As a world citizen, she is aware of global economic, political, social and ecological developments and trends.
2)	He/she is equipped to closely follow the technological progress required by global and local dynamics and to continue learning.
3)	Absorbs basic economic principles and analysis methods and uses them to evaluate daily events.
4)	Uses quantitative and statistical tools to identify economic problems, analyze them, and share their findings with relevant stakeholders.
5)	Understands the decision-making stages of economic units under existing constraints and incentives, examines the interactions and possible future effects of these decisions.
6)	Comprehends new ways of doing business using digital technologies. and new market structures.
7)	Takes critical approach to economic and social problems and develops analytical solutions.
8)	Has the necessary mathematical equipment to produce analytical solutions and use quantitative research methods.
9)	In the works he/she contributes, observes individual and social welfare together and with an ethical perspective.
10)	Deals with economic problems with an interdisciplinary approach and seeks solutions by making use of different disciplines.
11)	Generates original and innovative ideas in the works she/he contributes as part of a team.