DERS TANITIM BİLGİLERİ (İNGİLİZCE)
Course Information
Course Name
Cod
e
Semeste
r
Theory
(hours/wee
k)
Introduction
to
Bioinformatic
s
SE
446
1-2
3
Application
(hours/wee
k)
Laboratuary
(hours/wee
k)
Nationa
l Credit
ECT
S
3
5.5
Prequisites
English
Course
Language
Technical Elective
Course Type
Face to Face
Mode of
Delivery
(face to
face,
distance
learning)
Learning
Lecture
and
Teaching
Strategies
Instructor(s)
The objective of the course is to provide necessary knowledge and skills related to
Course
computational techniques for mining the large amount of biological data. In this
Objective
Learning
Outcomes
Course
Content
References
course the applications of the computational techniques in bioinformatics will be
introduced.
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
DNA and protein sequence alignment. Phylogenetic trees. Protein structure
prediction. Motif finding. Microarray data analysis. Gene/protein networks.
Course Book
 M. Zvelebil and J. O. Baum, Understanding Bioinformatics, Garland Science,
2008.
Other Sources
 N. C. Jones and P. A. Pevzner, An Introduction to Bioinformatics Algorithms,
MIT press, 2004.
 M. Lesk, Introduction to Bioinformatics, Oxford University Press, 2002.
 D. Mount, Bioinformatics: Sequence and genome analysis, Cold Spring
Harbor Laboratory Press, 2001.

T. Jiang, Y. Xu, and M. Zhang, eds. Current Topics in Computational
Molecular Biology, MIT press, 2002.
Weekly Course Outline
Weeks
1. Week
2. Week
3. Week
4. Week
5. Week
6. Week
7. Week
8. Week
9. Week
10. Week
11. Week
12. Week
13. Week
14. Week
15. Week
16. Week
Topics
Introduction
Producing and Analyzing Sequence
Alignments
Pairwise Sequence Alignment and
Database Searching
Pairwise Sequence Alignment and
Database Searching
Patterns, Profiles, and Multiple Alignments
Patterns, Profiles, and Multiple Alignments
Recovering Evolutionary History
Building Phylogenetic Trees
Obtaining Secondary Structure from
Sequence
Predicting Secondary Structures
Modeling Protein Structure
Clustering Methods and Statistics
Clustering Methods and Statistics
Systems Biology
Final Exam
Final Exam
Pre-study
Chapters 1,2,3 (main text)
Chapter 4
Chapter 5
Chapter 5
Chapter 6
Chapter 6
Chapter 7
Chapter 8
Chapter 11
Chapter 12
Chapter 13
Chapter 16
Chapter 16
Chapter 17
Assesment Methods
Course Activities
Attendance
Laboratory
Application
In-class Activity
Specific Practical Training (if any)
Assignments
Presentation
Projects
Seminars
Midterms
Final Exam
Total
Percentage of semester activities contributing grade
success
Number
Percentage %
3
30
2
1
40
30
100
70
30
100
Percentage of final exam contributing grade success
Total
Workload and ECTS Calculation
Activities
Number
Duration
(Hours)
Total Work Load
16
3
48
16
4
64
3
6
18
2
10
10
1
15
15
Course Duration (x14)
Laboratory
Application
Specific practical training (if any)
Field Activities
Study Hours Out of Class (Preliminary
work, reinforcement, ect)
Presentation / Seminar Preparation
Projects
Homework assignment
Midterms ( Study duration )
Final ( Study duration )
165
Total Workload
Matrix of the Course Learning Outcomes Versus Program Outcomes
Program Outcomes
1
2
3
4
5
6
7
8
An ability to apply knowledge of computing, sciences and
mathematics to solve software engineering problems.
An ability to analyze and model a domain specific problem,
identify and define the appropriate software requirements for its
solution.
An ability to design, implement and evaluate a software system,
component, process or program to meet specified requirements.
An ability to use the modern techniques and engineering tools
necessary for software engineering practices.
An ability to gather/acquire, analyze and interpret data to
understand software requirements.
The ability to demonstrate the necessary organizational and
business skills to work effectively in inter/inner disciplinary
teams or individually.
An ability to communicate effectively in Turkish and English.
Recognition of the need for, and the ability to access
information, to follow recent developments in science and
technology and to engage in life-long learning.
Contribution Level*
1
2
3
4
5
X
X
X
X
X
9
10
11
12
13
An understanding of professional, legal, ethical and social issues
and responsibilities.
Skills in project and risk management, awareness about
importance of entrepreneurship, innovation and long-term
development, and recognition of international standards and
methodologies.
An understanding about the impact of software engineering
solutions in a global societal and legal context.
An ability to apply algorithmic principles, mathematical
foundations, and computer science theory in the modeling and
design of computer-based systems with the tradeoffs involved in
design choices.
The ability to apply engineering approach to the development of
software systems by analyzing, designing, implementing,
verifying, validating and maintaining software systems.
1: Lowest, 2: Low, 3: Average, 4: High, 5: Highest
X
X
X