Introduction to Bioinformatics (SE446) Course Detail

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

Pre-requisite Course(s)
N/A

Course Language	English
Course Type	Elective Courses
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	Gains adequate knowledge in mathematics, science, and relevant engineering disciplines and acquires the ability to use theoretical and applied knowledge in these fields to solve complex engineering problems.
2	Gains the ability to identify, formulate, and solve complex engineering problems and the ability to select and apply appropriate analysis and modeling methods for this purpose.
3	Gains the ability to design a complex system, process, device, or product under realistic constraints and conditions to meet specific requirements and to apply modern design methods for this purpose.
4	Gains the ability to select and use modern techniques and tools necessary for the analysis and solution of complex engineering problems encountered in engineering applications and the ability to use information technologies effectively.
5	Gains the ability to design experiments, conduct experiments, collect data, analyze results, and interpret findings for investigating complex engineering problems or discipline specific research questions.
6	Gains the ability to work effectively in intra-disciplinary and multi-disciplinary teams and the ability to work individually.
7	a) Gains the ability to communicate effectively in written and oral form, b) Gains acquires proficiency in at least one foreign language, the ability to write effective reports and understand written reports, prepare design and production reports, make effective presentations, and give and receive clear and intelligible instructions.
8	Gains awareness of the need for lifelong learning and the ability to access information, follow developments in science and technology, and to continue to educate him/herself
9	a)Gains the ability to behave according to ethical principles, awareness of professional and ethical responsibility. b) Gains knowledge of the standards utilized in energy systems engineering applications.
10	Gains knowledge on business practices such as project management, risk management and change management; awareness about entrepreneurship, innovation; knowledge on sustainable development.
11	a) Gain awareness of the effects of Energy Systems Engineering applications on health, environment and safety in universal and societal dimensions. b) Gain knowledge of the problems of the era reflected in the field of engineering; gain 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