Information Package / Course Catalogue
Introduction to Bioinformatics
Course Code: CSE451
Course Type: Area Elective
Couse Group: First Cycle (Bachelor's Degree)
Education Language: English
Work Placement: N/A
Theory: 2
Prt.: 2
Credit: 3
Lab: 0
ECTS: 6
Objectives of the Course

The main objective of this course is to provide an understanding of bioinformatics from a computer engineering perspective and to develop the necessary perspective for conducting analyses.

Course Content

Within the scope of this course, the theoretical foundations of acquiring biological data, which form the basis of bioinformatics, will be covered; the obtained data will then be analyzed and interpreted from a bioinformatics perspective using statistical methods, machine learning algorithms, and artificial intelligence techniques.

Name of Lecturer(s)
Learning Outcomes
1.Understand the fundamentals of bioinformatics
2.Interpret biological systems from a computer engineering perspective
3.Learn the analysis of large-scale biological data
4.Analyze genomic signal datasets using artificial intelligence and machine learning algorithms
5.Learn the multimodal data analysis applications
Recommended or Required Reading
1.Gautam B. Singh, “Fundamentals of Bioinformatics and Computational Biology, 2nd Ed.”; Springer Cham, 2014, ISBN: 978-3-031-75696-2
2.David A. Hendrix, “Applied Bioinformatics”, Hendrix Lab - Oregon State University
3.Supratim Choudhuri, “Bioinformatics for Beginners”; Elsevier Inc., 2014, ISBN: 978-0-12-410471-6
4.Khalid Raza, “Machine Learning in Single-Cell RNA-seq Data Analysis”; Springer, 2024, ISBN: 978-9819767021
Weekly Detailed Course Contents
Week 1 - Preparation Work
Introduction to Bioinformatics
Week 2 - Preparation Work
Introduction to Bioinformatics (cont.)
Week 3 - Preparation Work
Fundamentals of Sequence Alignment and Phylogenetics
Week 4 - Preparation Work
NGS Technologies
Week 5 - Preparation Work
Transcriptomics and RNA-Seq Data Processing I
Week 6 - Preparation Work
RNA-Seq Data Processing II
Week 7 - Intermediate Exam
Midterm
Week 8 - Intermediate Exam
Midterm
Week 9 - Preparation Work
Differential Gene Expression Analysis
Week 10 - Preparation Work
Functional Enrichment Analysis
Week 11 - Preparation Work
Introduction to Single-Cell RNA-Seq
Week 12 - Preparation Work
Single-Cell Data Analysis
Week 13 - Preparation Work
Advanced Transcriptomics Approaches I
Week 14 - Preparation Work
Advanced Transcriptomics Approaches II
Week 15 - Preparation Work
Proteomics, Epigenomics, and Multi-Omics
Week 16 - Final Exam
Final
Assessment Methods and Criteria
Type of AssessmentCountPercent
Attending Lectures6%5
Assignment1%10
Midterm Examination1%25
Final Examination1%60
Workload Calculation
ActivitiesCountPreparationTimeTotal Work Load (hours)
Lecture - Theory142370
Lecture - Practice53225
Assignment1055
Seminar61218
Midterm Examination112113
Final Examination115116
TOTAL WORKLOAD (hours)147
Contribution of Learning Outcomes to Programme Outcomes
PÇ-1
PÇ-2
PÇ-3
PÇ-4
PÇ-5
PÇ-6
PÇ-7
PÇ-8
PÇ-9
PÇ-10
PÇ-11
OÇ-1
3
1
5
OÇ-2
4
3
4
3
5
OÇ-3
2
4
2
3
OÇ-4
3
2
4
OÇ-5
3
1
5
Adnan Menderes University - Information Package / Course Catalogue
2026