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	Knowledge of mathematics, natural sciences, engineering fundamentals, computing, and topics specific to the relevant engineering discipline; the ability to use this knowledge in the solution of complex engineering problems.
2	The ability to identify, formulate, and analyze complex engineering problems using knowledge of basic sciences, mathematics, and engineering, and considering the UN Sustainable Development Goals relevant to the problem.
3	The ability to design creative solutions for complex engineering problems; the ability to design complex systems, processes, devices, or products to meet current and future requirements, considering realistic constraints and conditions.
4	The ability to select and use appropriate techniques, resources, and modern engineering and IT tools, including prediction and modeling, for the analysis and solution of complex engineering problems, with an awareness of their limitations.
5	The ability to use research methods for the investigation of complex engineering problems, including literature search, designing and conducting experiments, collecting data, and analyzing and interpreting results.
6	Knowledge of the effects of engineering practices on society, health and safety, the economy, sustainability, and the environment within the scope of the UN Sustainable Development Goals; awareness of the legal consequences of engineering solutions.
7	Acting in accordance with engineering professional principles, knowledge of ethical responsibility; awareness of acting impartially without discrimination on any grounds and being inclusive of diversity.
8	The ability to work effectively individually and in intra-disciplinary and multi-disciplinary teams (face-to-face, remote, or hybrid) as a team member or leader.
9	"The ability to communicate effectively orally and in writing on technical topics, considering the various differences of the target audience (such as education, language, profession).
10	Knowledge of practices in business life such as project management and economic feasibility analysis; awareness of entrepreneurship and innovation.
11	The ability to engage in life-long learning, including independent and continuous learning, adapting to new and emerging technologies, and thinking inquisitively regarding technological changes.

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