Course Details

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Course Information
SemesterCourse Unit CodeCourse Unit TitleT+P+LCreditNumber of ECTS CreditsLast Updated Date
1ECE561BIOINFORMATICS3+0+037,514.05.2025

 
Course Details
Language of Instruction English
Level of Course Unit Doctorate's Degree
Department / Program ELECTRICAL AND COMPUTER ENGINEERING
Type of Program Formal Education
Type of Course Unit Elective
Course Delivery Method Face To Face
Objectives of the Course O1. Learn different types and sources of data available in bioinformatics,
O2. Learn the fundamental computational problems in molecular biology and genomics,
O3. Learn a core set of widely used algorithms in bioinformatics,
O4. Learn a set of algorithms that have important applications in bioinformatics, but which have key applications outside of biology as well.
O5. Apply the concepts learned to a real problem
Course Content W1 Description of the basic terms in Molecular Biology, Genetics and bioinformatics: a) The organization of DNA, proteins, cell; b) In silico biology
W2 Review of real world applications of bioinformatics, Introduction of Fragment Assembly Problem
W3 Description of Fragment Assembly Problem, Overlap-Layout- Consensus Algorithm
W4 Description of Pairwise alignment of biomolecular sequences: Global alignment
W5 Description of Local alignment, Semi-global alignment
W6 Description of similarity search algorithms such as BLAST algorithm; description of the scoring in similarity matrices: PAM and BLOSUM matrices
W7 Description of the Multiple sequence alignment: a) Iterative Methods, b) Structure Based Methods LO1, LO3
W8 Description of the scoring in multiple alignments
W9 Description and review of the high-throughput biological data analysis methods: Detecting differential gene expression, multiple hypothesis testing, false-discovery-rate methods.
W10 Description and review of the clustering and classification algorithms for gene expression data analysis.
W11 Description of the protein-protein interaction, protein/DNA interaction, gene/protein interaction networks
W12 Construction and analysis of large scale biological networks
W13 Identification of Drug-Repurposing candidates using Biological Networks
W14 Description and review of the machine learning approaches for integrating data in molecular biology, genetics and medicine.
Course Methods and Techniques
Prerequisites and co-requisities None
Course Coordinator Associate Prof.Dr. Samet Güler
Name of Lecturers Associate Prof.Dr. BURCU GÜNGÖR burcu.gungor@agu.edu.tr
Assistants Research Assist. ...
Work Placement(s) No

Recommended or Required Reading
Resources Durbin, R., Eddy, S. R., Krogh, A., & Mitchison, G. (1998). Biological sequence analysis: Probabilistic models of proteins and nucleic acids. Cambridge University Press.
Compeau, P. E. C., & Pevzner, P. A. (2015). Bioinformatics algorithms: An active learning approach (Vols. 1 & 2). Active Learning Publishers.

Course Category
Mathematics and Basic Sciences %75
Engineering %10
Science %10
Health %5

Planned Learning Activities and Teaching Methods
Activities are given in detail in the section of "Assessment Methods and Criteria" and "Workload Calculation"

Assessment Methods and Criteria
In-Term Studies Quantity Percentage
Quiz/Küçük Sınav 1 % 25
Ödev 3 % 30
Proje/Çizim 1 % 20
Final examination 1 % 25
Total
6
% 100

 
ECTS Allocated Based on Student Workload
Activities Quantity Duration Total Work Load
Yazılı Sınav 1 20 20
Ev Ödevi 3 20 60
Sunum 1 30 30
Proje 1 50 50
Kısa Sınav 1 1 1
Okuma 2 11 22
Yüz Yüze Ders 1 3 3
Final Sınavı 1 40 40
Total Work Load   Number of ECTS Credits 7,5 226

 
Course Learning Outcomes: Upon the successful completion of this course, students will be able to:
NoLearning Outcomes
1 Explain the fundamental computational problems in molecular biology and genomics
2 Explain different types and sources of data available in bioinformatics
3 Implement a core set of widely used algorithms in bioinformatics
4 Apply a bioinformatics method to a real problem

 
Weekly Detailed Course Contents
WeekTopicsStudy MaterialsMaterials
1 Description of the basic terms in Molecular Biology, Genetics and bioinformatics: a) The organization of DNA, proteins, cell; b) In silico biology
2 Review of real world applications of bioinformatics, Introduction of Fragment Assembly Problem
3 Description of Fragment Assembly Problem, Overlap-Layout-Consensus Algorithm
4 Description of Pairwise alignment of biomolecular sequences: Global alignment
5 Description of Local alignment, Semi-global alignment
6 Description of similarity search algorithms such as BLAST algorithm, description of the scoring in similarity matrices: PAM and BLOSUM matrices
7 Description of the Multiple sequence alignment: a) Iterative Methods, b) Structure Based Methods
8 Description of the scoring in multiple alignments
9 Description and review of the high-throughput biological data analysis methods: Detecting differential gene expression, multiple hypothesis testing, false-discovery-rate methods
10 Description and review of the clustering and classification algorithms for gene expression data analysis.
11 Description of the protein-protein interaction, protein/DNA interaction, gene/protein interaction networks
12 Construction and analysis of large scale biological networks
13 Identification of Drug-Repurposing candidates using Biological Networks
14 Description and review of the machine learning approaches for integrating data in molecular biology, genetics and medicine

 
Contribution of Learning Outcomes to Programme Outcomes
P1 P2 P3 P4 P5 P6 P7 P8 P9 P10 P11
C1 4 4 1 4 4 4 1 4 4 1 2
C2 2 1 3 2 4 3 1 1 1 1 4
C3 4 4 4 5 1 2 3 2 2 2 1
C4 4 1 5 5 3 3 5 3 3 4 4

  Contribution: 1: Very Slight 2:Slight 3:Moderate 4:Significant 5:Very Significant

  
  https://sis.agu.edu.tr/oibs/bologna/progCourseDetails.aspx?curCourse=77884&lang=en
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