ADVERTISING
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 :	Assist. Prof. CEMALETTİN BEKPEN
Course Lecturer(s):	Prof. Dr. SÜREYYA AKYÜZ Assist. Prof. SERKAN AYVAZ
Recommended Optional Program Components:	There is none.
Course Objectives:	Bioinformatics (computational molecular biology)involves applying computational methods for managing and analyzing information about the sequence, structure and function of biological molecules and systems. This introductory course will cover statistical and algorithmic concepts to address common,yetdifficultquestionsthatarisewhileanalyzingbiologicaldata.Biologicaldatacan be categorized based on the levels of information that exist in living cells: DNA, RNA, proteins, metabolites, and other small molecules. This course is organized into modules, each section focuses on a particular type of biological data, biological questions that are associated with these data, and the computational approaches to address these questions. The goals of this course are to provide an understanding of: 1_The types of biological data 2_The computational problems that arise while analyzing biological data 3_A set of algorithms that have important applications in computational biology, but which have key applications outside of biology as well. 4_Core set of widely used algorithms in computational biology

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 to Bioinformatics
2)	Genomics data mining (Biological Databases)
3)	Sequencing (Methods and Sequencing technologies)
4)	Sequence Search
5)	Genomic Variation
6)	Sequence Alignment
7)	Molecular Phylogenetic
8)	Whole Genome Sequencing and Mapping
9)	Review for the midterm exam
10)	Downstream Analysis of Variant Detection (SNPs, SVs, CNVs)
11)	Omics Data Analysis (Transcriptomics, Proteomics)
12)	Omics Data Analysis (Epigenomics, Paleogenomics)
13)	Omics Data Analysis (Functional Genomics, Metagenomics)
14)	General Review

Sources

Course Notes / Textbooks:	Biyoinformatik ders notları haftalık olarak verilecektir. Course material will be supplied weekly. 1) Pevsner J. 2015. Bioinformatics and Functional Genomics, 3rd Ed. Wiley Blackwell.
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	1	% 20
Midterms	1	% 30
Final	1	% 50
Total		% 100
PERCENTAGE OF SEMESTER WORK		% 50
PERCENTAGE OF FINAL WORK		% 50
Total		% 100

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 prepare students to become communication professionals by focusing on strategic thinking, professional writing, ethical practices, and the innovative use of both traditional and new media	2
2)	To be able to explain and define problems related to the relationship between facts and phenomena in areas such as Advertising, Persuasive Communication, and Brand Management
3)	To critically discuss and interpret theories, concepts, methods, tools, and ideas in the field of advertising
4)	To be able to follow and interpret innovations in the field of advertising
5)	To demonstrate a scientific perspective in line with the topics they are curious about in the field.
6)	To address and solve the needs and problems of the field through the developed scientific perspective
7)	To recognize and understand all the dynamics within the field of advertising
8)	To analyze and develop solutions to problems encountered in the practical field of advertising