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 HANİFE SALİH DOĞAN

Course Lecturer(s):

Course Assistants:

Course Objective and Content

Course Objectives:

The objective of this course is to introduce students to the field of bioinformatics by teaching fundamental concepts, tools, and methods used in biological data analysis. The course aims to equip students with the ability to analyze DNA, RNA, and protein sequences, effectively utilize biological databases, and interpret data using bioinformatics tools. Additionally, students will develop skills in sequence alignment, genome analysis, and searching for motifs and domains in proteins using bioinformatics approaches.

Course Content:

This course provides an introduction to bioinformatics, covering fundamental concepts, tools, and applications in biological data analysis. Students will explore biological databases, sequence alignment, molecular evolution, genome analysis, protein modeling, and transcriptomics. Hands-on practice with bioinformatics software will enhance their ability to analyze and interpret biological data.

Learning Outcomes

The students who have succeeded in this course;

Learning Outcomes

1 - Knowledge
Theoretical - Conceptual
1) The student is able to explain the fundamental concepts of bioinformatics and its applications in biological research.
2) The student is able to explain the processes of genome annotation, gene prediction, and transcriptomic data interpretation.
3) The student is able to explain the applications of bioinformatics techniques in protein modeling and metagenomic studies.
2 - Skills
Cognitive - Practical
1) The student is able to explain how to access and utilize biological databases for retrieving and analyzing DNA, RNA, and protein sequences.
2) The student is able to perform sequence alignment and phylogenetic analysis using computational tools.
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 • Definition of Bioinformatics • Types of Data (DNA, RNA, Protein) • Application Areas	Lecture notes and powerpoint presentations
2)	Introduction to Biological Databases • Types of Biological Databases • Database Formats and Data Organization • Applications of Biological Databases	Lecture notes and powerpoint presentations
3)	DNA and RNA Sequence Analysis • Basic Concepts of Sequence Alignment • Global Pairwise Sequence Alignment: Needleman-Wunsch Algorithm • Local Pairwise Sequence Alignment: Smith-Waterman Algorithm • Practices with BLAST and EMBOSS Needle for Sequence Alignment	Lecture notes and powerpoint presentations
4)	Multiple Sequence Alignment Methods • Progressive Alignment Methods • Iterative Alignment Methods • Consistency-Based Alignment Methods • Scoring and Evaluation Metrics in MSA • Clustal Omega, MUSCLE, MAFFT and T-Coffee applications	Lecture notes and powerpoint presentations
5)	Molecular Evolution and Phylogenetic Trees • Introduction to Molecular Evolution • Phylogenetics: Basic Concepts • The construction of phylogenetic tree via MEGA	Lecture notes and powerpoint presentations
6)	Protein Fundamentals • Structure and Functions of Proteins • Analysis of Protein Sequences • Structural Levels of Proteins • Protein-Protein Interactions	Lecture notes and powerpoint presentations
7)	Sequence motif and domain analysis • Identification and Significance of protein motifs and domains • Practical Analysis of Motifs and Domains	Lecture notes and powerpoint presentations
8)	Midterm week
9)	Sequencing • Definition of DNA, RNA, or protein sequencing. • Historical overview and significance of sequencing. • Sequencing Techniques (Sanger Sequencing, Next-Generation Sequencing (NGS), Third-Generation Sequencing)	Lecture notes and powerpoint presentations
10)	Raw Data Interpretation and Assembly • Introduction to Raw Sequencing Data • Quality Control (QC) • Sequence Assembly • The tools used for quality control and assembly	Lecture notes and powerpoint presentations
11)	Genome analysis • Typing • Annotation • Visualization	Lecture notes and powerpoint presentations
12)	RNA-Seq and transcriptomic analysis • Introduction to RNA-Seq • Experimental Workflow of RNA-Seq • Differential Expression Analysis • RNA-Seq Applications	Lecture notes and powerpoint presentations
13)	Protein Modeling • Introduction to Protein Modeling • Types of Protein Modeling • Tools and Software for Protein Modeling	Lecture notes and powerpoint presentations
14)	• Introduction to Metagenomics • Metagenomic Data Collection • Types of Metagenomic Studies	Lecture notes and powerpoint presentations
15)	Bioinformatics and genetic diseases	Course notes

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	4	5	2	3
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.	3
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.	5
5)	Ability to design experiments, conduct experiments, collect data, analyze and interpret results to examine engineering problems or discipline-specific research topics.	3
6)	The ability to work effectively in disciplinary and multidisciplinary teams; individual work skill.	2
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.	2
9)	Conform to ethical principles, and standards of professional and ethical responsibility; be informed about the standards used in engineering applications.	1
10)	Awareness of applications in business, such as project management, risk management and change management; awareness of entrepreneurship, and innovation; information about sustainable development.	2
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.	1

Learning Activity and Teaching Methods

Assessment & Grading Methods and Criteria

Assessment & Grading

Semester Requirements	Number of Activities	Level of Contribution
Quizzes	2	% 20
Presentation	1	% 0
Midterms	1	% 40
Final	1	% 40
total		% 100
PERCENTAGE OF SEMESTER WORK		% 60
PERCENTAGE OF FINAL WORK		% 40
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