Introduction to Bioinformatics (SE446) Course Detail

Course Name	Course Code	Season	Lecture Hours	Application Hours	Lab Hours	Credit	ECTS
Introduction to Bioinformatics	SE446		3	0	0	3	5

Pre-requisite Course(s)
N/A

Course Language	English
Course Type	N/A
Course Level	Bachelor’s Degree (First Cycle)
Mode of Delivery	Face To Face
Learning and Teaching Strategies	Lecture.
Course Coordinator
Course Lecturer(s)
Course Assistants
Course Objectives	The objective of the course is to provide necessary knowledge and skills related to computational techniques for mining the large amount of biological data. In this course the applications of the computational techniques in bioinformatics will be introduced.
Course Learning Outcomes	The students who succeeded in this course; Apply DNA and protein sequence alignment techniques Build phylogenetic trees Apply techniques to predict protein structure Gain skills for clustering methods used in bioinformatics Analyze gene/protein networks
Course Content	DNA and protein sequence alignment, phylogenetic trees, protein structure prediction, motive findin, microarray data analysis, gene/protein networks.

Weekly Subjects and Releated Preparation Studies

Week	Subjects	Preparation
1	Introduction	Chapters 1,2,3 (main text)
2	Producing and Analyzing Sequence Alignments	Chapter 4
3	Pairwise Sequence Alignment and Database Searching	Chapter 5
4	Pairwise Sequence Alignment and Database Searching	Chapter 5
5	Patterns, Profiles, and Multiple Alignments	Chapter 6
6	Patterns, Profiles, and Multiple Alignments	Chapter 6
7	Recovering Evolutionary History	Chapter 7
8	Building Phylogenetic Trees	Chapter 8
9	Obtaining Secondary Structure from Sequence	Chapter 11
10	Predicting Secondary Structures	Chapter 12
11	Modeling Protein Structure	Chapter 13
12	Clustering Methods and Statistics	Chapter 16
13	Clustering Methods and Statistics	Chapter 16
14	Clustering Methods and Statistics	Chapter 17
15	Final Examination Period	Review of topics
16	Final Examination Period	Review of topics

Sources

Course Book	1. M. Zvelebil and J. O. Baum, Understanding Bioinformatics, Garland Science, 2008
Other Sources	2. N. C. Jones and P. A. Pevzner, An Introduction to Bioinformatics Algorithms, MIT press, 2004
	3. A. M. Lesk, Introduction to Bioinformatics, Oxford University Press, 2002
	4. D. Mount, Bioinformatics: Sequence and genome analysis, Cold Spring Harbor Laboratory Press, 2001
	5. T. Jiang, Y. Xu, and M. Zhang, eds. Current Topics in Computational Molecular Biology, MIT press, 2002

Evaluation System

Requirements	Number	Percentage of Grade
Attendance/Participation	-	-
Laboratory	-	-
Application	-	-
Field Work	-	-
Special Course Internship	-	-
Quizzes/Studio Critics	-	-
Homework Assignments	1	20
Presentation	-	-
Project	1	30
Report	-	-
Seminar	-	-
Midterms Exams/Midterms Jury	1	20
Final Exam/Final Jury	1	30
Toplam	4	100

Percentage of Semester Work	70
Percentage of Final Work	30
Total	100

Course Category

Core Courses	X
Major Area Courses
Supportive Courses
Media and Managment Skills Courses
Transferable Skill Courses

The Relation Between Course Learning Competencies and Program Qualifications

#	Program Qualifications / Competencies	Level of Contribution
#	Program Qualifications / Competencies	1	2	3	4	5
1	Adequate knowledge of subjects related to mathematics, natural sciences, and Electrical and Electronics Engineering discipline; ability to apply theoretical and applied knowledge in those fields to the solution of complex engineering problems.			X
2	An ability to identify, formulate, and solve complex engineering problems, ability to choose and apply appropriate models and analysis methods for this.			X
3	An ability to design a system, component, or process under realistic constraints to meet desired needs, and ability to apply modern design approaches for this.			X
4	The ability to select and use the necessary modern techniques and tools for the analysis and solution of complex problems encountered in engineering applications; the ability to use information technologies effectively
5	Ability to design and conduct experiments, collect data, analyze and interpret results for investigating complex engineering problems or discipline-specific research topics.
6	An ability to function on multi-disciplinary teams, and ability of individual working.					X
7	Ability to communicate effectively orally and in writing; knowledge of at least one foreign language; active report writing and understanding written reports, preparing design and production reports, the ability to make effective presentation the ability to give and receive clear and understandable instructions.
8	Awareness of the necessity of lifelong learning; the ability to access knowledge, follow the developments in science and technology and continuously stay updated.
9	Acting compliant with ethical principles, professional and ethical responsibility, and knowledge of standards used in engineering applications.
10	Knowledge about professional activities in business, such as project management, risk management, and change management awareness of entrepreneurship and innovation; knowledge about sustainable development.
11	Knowledge about the impacts of engineering practices in universal and societal dimensions on health, environment, and safety. the problems of the current age reflected in the field of engineering; awareness of the legal consequences of engineering solutions.

ECTS/Workload Table

Activities	Number	Duration (Hours)	Total Workload
Course Hours (Including Exam Week: 16 x Total Hours)	16	3	48
Laboratory
Application
Special Course Internship
Field Work
Study Hours Out of Class	16	2	32
Presentation/Seminar Prepration
Project
Report
Homework Assignments	3	5	15
Quizzes/Studio Critics
Prepration of Midterm Exams/Midterm Jury	2	10	20
Prepration of Final Exams/Final Jury	1	15	15
Total Workload			130