Graduate Study - Faculty of Engineering and Natural Sciences
3+2 Information Technology

Code Name Level Year Semester
CEN 576 Computational Methods in Bioinformatics Graduate 1 Spring
Status Number of ECTS Credits Class Hours Per Week Total Hours Per Semester Language
7.5 48 English

Instructor Assistant Coordinator
Ercan Gökgöz, PhD, Research Assistant A. Turan Özcerit, Assist. Prof. Dr.
[email protected] no email

The objective of this course is to train the students in computational methods in cell & molecular biology, and bioinformatics. Emphasis would be laid on understanding basic tools & algorithms in different areas of bioinformatics. The course includes an integrated project which involves the application of the above aspects. Bioinformatics Tools and databases for Molecular and Genome Analyses will be taught in detail. In this connection following topics will be covered:
DNA sequence analysis, promoter analysis and identification of transcription factor binding sites; methods for the unsupervised analysis, validation and visualization of structures discovered in bio-molecular data -- prediction of secondary and tertiary protein structures; gene expression data analysis; mathematical modelling and simulation of biological systems

  1. Introduction to Bioinformatics
  2. DNA Sequence Databases
  3. DNA Sequence Comparison
  4. Genome Browsers
  5. Non-Coding RNA Transcripts
  6. Gene Prediction Methods
  7. Gene Annotation Methods
  8. Regulatory Motif Analysis
  9. Molecular Marker Discovery and Genetic Map Visualisation
  10. Sequence Based Gene Expression Analysis
  11. Protein Sequence Databases
  12. Protein Structure Prediction
  13. Classification of Information About Proteins
  14. Project presentations

  1. Introduction to MATLAB
  2. DNA Sequence Databases
  3. DNA Sequence Comparison

  1. Genome Browsers
  2. Non-Coding RNA Transcripts
  3. Gene Prediction Methods
  4. Gene Annotation Methods
  5. Regulatory Motif Analysis
  6. Molecular Marker Discovery and Genetic Map Visualisation
  7. Sequence Based Gene Expression Analysis
  8. Protein Sequence Databases
  9. Protein Structure Prediction
  10. Classification of Information About Proteins
  11. Projects

  • Lectures
  • Excersises
  • Presentation
  • Project
Description (%)
Method Quantity Percentage (%)
Midterm Exam(s)125
Final Exam175
Total: 100
Learning outcomes
  • Evaluate basic theories, processes and outcomes of computing;
  • Apply bioinformatics and biological techniques and relevant tools to the specification, analysis, design, implementation and testing of a simple computing product. For example identification of genes involved in specific biological process in the cell.
  • Knowledge and critical understanding of the well-established principles of bioinformatics, and of the way in which those principles have developed as technology has progressed
  • Knowledge of all of the main development methods relevant to the field of computing, and ability to evaluate critically the appropriateness of different approaches to solving problems in the field of genetics and genetic engineering.
  • D. Edwards, J. Stajich, D.Hansen, Bioinformatics- Tools and Applications 2009,Springer

ECTS (Allocated based on student) WORKLOAD
Activities Quantity Duration (Hour) Total Work Load
Lecture (14 weeks x Lecture hours per week)16348
Laboratory / Practice (14 weeks x Laboratory/Practice hours per week) 0
Midterm Examination (1 week) 0
Final Examination(1 week) 0
Preparation for Midterm Examination 0
Preparation for Final Examination 0
Total Workload: 48
ECTS Credit (Total workload/25): 2