Genetics and Bioengineering (English)
Bachelor	TR-NQF-HE: Level 6	QF-EHEA: First Cycle	EQF-LLL: Level 6

General course introduction information

Course Code:

GBE305

Course Name:

Bioinformatics

Course Semester:

Fall

Course Credits:

Theoretical	Practical	Credit	ECTS
2	2	3	8

Language of instruction:

Course Requisites:

Does the Course Require Work Experience?:

Type of course:

Compulsory

Course Level:

Bachelor

TR-NQF-HE:6. Master`s Degree

QF-EHEA:First Cycle

EQF-LLL:6. Master`s Degree

Mode of Delivery:

Face to face

Course Coordinator :

Dr.Öğr.Üyesi FATMA TUBA AKDENİZ

Course Lecturer(s):

Dr.Öğr.Üyesi FATMA TUBA AKDENİZ
Dr.Öğr.Üyesi METİN YAZAR

Course Assistants:

Course Objective and Content

Course Objectives:

This course covers computational techniques for the mining of large amounts of information produced by recent developments in biology, such as genome sequencing and microarray technologies. The aim of the course;
• Gaining the DNA and protein sequence alignment methods and mathematical calculation skills,
• Teaching sequence motifs / patterns, and the algorithms behind them
• Teaching the basis of phylogenetic trees and the relationships between these trees and protein families and gene sequences,
• protein structures: how and why researchers make predictions, adjustments, classifications, and so on
• Microarray data analysis: normalization, clustering

Course Content:

The course is designed to provide knowledge to bioengineers to solve the biological questions using bioinformatics and learn to use several databases. Students use online tools to analyze DNA, RNA and protein as well as learn how to use publicly available databases.

Learning Outcomes

The students who have succeeded in this course;

Learning Outcomes

1 - Knowledge
Theoretical - Conceptual
1) Teaching concepts such as the birth of science of bioinformatics and the need for such a branch of science. Giving information about the most basic bioinformatics applications.
2) 3.1) Teaching the databases that are open to the public on the internet, teaching the concepts such as how to get the information requested from these databases and how to compare them with other data. In addition, each database has its own advantages, disadvantages, differences from others.
3) 4.1) To teach the problems related to bioinformatics and to teach the general terms and concepts with these problems. Besides, teaching pairwise sequence alignment and dynamic programming operations and their differences.
4) 5.1) How to make paritywise alignment operations and teach the mathematical logic behind these operations. Describing the concepts of P, E and Z score, which are the nominal terms of bioinformatics
5) 6.1) The creation of suffix tree and suffix arrays, The historical developments of these structures and why they are needed at bioinformatic applications.
6) 7.1) Multiple alignment techniques and comparison of these techniques with sequences, extraction of profile models. In addition, the establishment of point matrices and the scoring of sequences using these matrices
7) 8.1) Mathematical description of hidden markov models, use of these models and application processes. Procedures for showing profiles between sequences using these models
8) To be familiar with general terms and concepts about system biology. Establishment of relationships between nodes and edges and general layout networks
2 - Skills
Cognitive - Practical
3 - Competences
Communication and Social Competence
Learning Competence
Field Specific Competence
Competence to Work Independently and Take Responsibility

Lesson Plan

Week	Subject	Related Preparation
1)	Introduction of bioinformatics
3)	• Introduction to biology, biological databases, and high-throughput data sources. Overview of bioinformatics problems • Pairwise Sequence Alignment algorithms: Dynamic programming
4)	• Statistical significance of alignments - Part I • Statistical significance of alignments - Part II • E-value p-value z-score
5)	• Suffix Trees, Suffix Arrays • Microarray selection, tree construction, pattern matching
6)	• Patterns, Profiles, and Multiple Alignments • Specific scoring matrices,
7)	• Hidden Markov models • Sequence pattern discovery
8)	• Multiple sequence alignment algorithms • Alignment profiles
9)	• Introduction to protein structures • Structure Prediction
10)	Structural Alignment of Proteins Functional Genomics
11)	• Microarray data normalization, analysis • Clustering techniques
12)	• Introduction to Systems Biology • Gene regulatory networks
13)	• Protein ağlarının yapısı ve analizi • Monte Carlo Örneklemesi, Grafiklerdeki Rasgele Yürüyüşler

Sources

Course Notes / Textbooks:

M. Zvelebil and J. O. Baum, Understanding Bioinformatics, Garland Science, 2008

References:

• D.E. Krane and M.L. Raymer, Fundamental Concepts of Bioinformatics, Pearson Education, 2003.
• N. C. Jones and P. A. Pevzner, An Introduction to Bioinformatics Algorithms, MIT press, 2004.
• C.A. Orengo, D.T. Jones and J.M.Thornton, Bioinformatics: Genes, Proteins and Computers, Roultledge, 2003.
• A. M. Lesk, Introduction to Bioinformatics, Oxford University Press, 2002.
• D. Mount, Bioinformatics: Sequence and genome analysis, Cold Spring Harbor Laboratory Press, 2001.
• P. A. Pevzner, Computational Molecular Biology: An Algorithmic Approach, MIT press, 2000.
• P. Baldi and S. Brunak, Bioinformatics: the machine learning approach (2nd edition), MIT press, 2001.
• T. Jiang, Y. Xu, and M. Zhang, eds. Current Topics in Computational Molecular Biology, MIT press, 2002.
• S. Karlin, Frontiers of Bioinformatics: Unsolved Problems and Challenges, National Academy Press, 2005

Course-Program Learning Outcome Relationship

Learning Outcomes	1							8
Program Outcomes
1) Sufficient knowledge in mathematics, science and engineering related to their branches; and the ability to apply theoretical and practical knowledge in these areas to model and solve engineering problems.
2) The ability to identify, formulate, and solve complex engineering problems; selecting and applying appropriate analysis and modeling methods for this purpose.
3) The ability to design a complex system, process, device or product under realistic constraints and conditions to meet specific requirements; the ability to apply modern design methods for this purpose. (Realistic constraints and conditions include such issues as economy, environmental issues, sustainability, manufacturability, ethics, health, safety, social and political issues, according to the nature of design.)
4) Ability to develop, select and use modern techniques and tools necessary for engineering applications; ability to use information technologies effectively.
5) Ability to design experiments, conduct experiments, collect data, analyze and interpret results to examine engineering problems or discipline-specific research topics.
6) The ability to work effectively in disciplinary and multidisciplinary teams; individual work skill.
7) Effective communication skills in Turkish oral and written communication; at least one foreign language knowledge; ability to write effective reports and understand written reports, to prepare design and production reports, to make effective presentations, to give and receive clear and understandable instructions.
8) Awareness of the need for lifelong learning; access to knowledge, ability to follow developments in science and technology, and constant self-renewal.
9) Conform to ethical principles, and standards of professional and ethical responsibility; be informed about the standards used in engineering applications.
10) Awareness of applications in business, such as project management, risk management and change management; awareness of entrepreneurship, and innovation; information about sustainable development.
11) Information about the universal and social health, environmental and safety effects of engineering applications and the ways in which contemporary problems are reflected in the engineering field; awareness of the legal consequences of engineering solutions.

Course - Learning Outcome Relationship

No Effect	1 Lowest	2 Low	3 Average	4 High	5 Highest

	Program Outcomes	Level of Contribution
1)	Sufficient knowledge in mathematics, science and engineering related to their branches; and the ability to apply theoretical and practical knowledge in these areas to model and solve engineering problems.	2
2)	The ability to identify, formulate, and solve complex engineering problems; selecting and applying appropriate analysis and modeling methods for this purpose.	1
3)	The ability to design a complex system, process, device or product under realistic constraints and conditions to meet specific requirements; the ability to apply modern design methods for this purpose. (Realistic constraints and conditions include such issues as economy, environmental issues, sustainability, manufacturability, ethics, health, safety, social and political issues, according to the nature of design.)	1
4)	Ability to develop, select and use modern techniques and tools necessary for engineering applications; ability to use information technologies effectively.	2
5)	Ability to design experiments, conduct experiments, collect data, analyze and interpret results to examine engineering problems or discipline-specific research topics.
6)	The ability to work effectively in disciplinary and multidisciplinary teams; individual work skill.
7)	Effective communication skills in Turkish oral and written communication; at least one foreign language knowledge; ability to write effective reports and understand written reports, to prepare design and production reports, to make effective presentations, to give and receive clear and understandable instructions.	1
8)	Awareness of the need for lifelong learning; access to knowledge, ability to follow developments in science and technology, and constant self-renewal.
9)	Conform to ethical principles, and standards of professional and ethical responsibility; be informed about the standards used in engineering applications.
10)	Awareness of applications in business, such as project management, risk management and change management; awareness of entrepreneurship, and innovation; information about sustainable development.
11)	Information about the universal and social health, environmental and safety effects of engineering applications and the ways in which contemporary problems are reflected in the engineering field; awareness of the legal consequences of engineering solutions.

Learning Activity and Teaching Methods

Assessment & Grading Methods and Criteria

Written Exam (Open-ended questions, multiple choice, true-false, matching, fill in the blanks, sequencing)
Homework
Application

Assessment & Grading

Semester Requirements	Number of Activities	Level of Contribution
Attendance	1	% 5
Homework Assignments	6	% 5
Midterms	1	% 40
Final	1	% 50
total		% 100
PERCENTAGE OF SEMESTER WORK		% 50
PERCENTAGE OF FINAL WORK		% 50
total		% 100

Workload and ECTS Credit Grading

Activities	Number of Activities	Duration (Hours)	Workload
Course Hours	14	2	28
Laboratory	14	2	28
Homework Assignments	6	1	6
Midterms	1	2	2
Final	1	2	2
Total Workload			66