Graduate Study - Faculty of Engineering and Natural Sciences
Genetics and Bioengineering Master With Thesis

Code Name Level Year Semester
GBE 514 Advanced Topics in Bioinformatics Graduate 1 Spring
Status Number of ECTS Credits Class Hours Per Week Total Hours Per Semester Language
Compulsory 6 98

Instructor Assistant Coordinator
Şenol Doğan, Assist. Prof. Dr. Assist Prof Senol Dogan Şenol Doğan, Assist. Prof. Dr.
[email protected] [email protected] no email

Advanced topics in bioinformatics let students understand the deep data analysis and combine their wet lab experience with dry lab data analysis. Since many computer programs and tools are provided everyday to the field, genetics and bioengineering students also should be familiar with the computer programs, algorithms, statistical calculation methods and future perspective.

The main course objective of the course is to engage the students deep and detail data analysis by different data types, database and computer programs, tools. Data mining is one of the rising topics in the science field especially biological science. Too big data is provided by databases or university providers. The data is accessible from any part of the world. Our students also should be aware of the data and access methods. First of all, the data will be provided by instructor to the students to do their basic research and understanding. At the end of the course, each student should extract some kind of result report according to their selection data analysis.

  1. General Information about the course
  2. Data and Data types
  3. Selection data types to do reserach
  4. Presentation their first data research
  5. Practical work on data
  6. Data mining and extract the valuable data result
  7. Computer programs and tools for data analysis
  8. Midterm
  9. Preparation for result presentation
  10. Database and usage of the databases
  11. How to access the worldwide database
  12. Result, table and figure preparatin based on the data result
  13. Suitable bioinformatics journals and their format
  14. Presentation full reserach paper
  15. Final


    • Interactive Lectures
    • Presentation
    • Case Studies
    Description (%)
    Method Quantity Percentage (%)
    Term Paper130
    Total: 100
    Learning outcomes
    • Data and database
    • Meaning of big data and data mining
    • Selection of research data
    • Gene expression, Methylation and microRNA data analysis
    • Computer programs, tools and algorithms
    • Practical extracting the valuable data
    • Preparation of research proposal
    • Presentation data analysis

      ECTS (Allocated based on student) WORKLOAD
      Activities Quantity Duration (Hour) Total Work Load
      Lecture (14 weeks x Lecture hours per week)14228
      Laboratory / Practice (14 weeks x Laboratory/Practice hours per week)14228
      Midterm Examination (1 week)122
      Final Examination(1 week)122
      Preparation for Midterm Examination11515
      Preparation for Final Examination11515
      Assignment / Homework/ Project144
      Seminar / Presentation144
      Total Workload: 98
      ECTS Credit (Total workload/25): 4