Computer Engineering (Master) (without Thesis) (English)
Master TR-NQF-HE: Level 7 QF-EHEA: Second Cycle EQF-LLL: Level 7

Course Introduction and Application Information

Course Code: AO5017
Course Name: Computational Biology
Semester: Fall
Spring
Course Credits:
ECTS
6
Language of instruction: English
Course Condition:
Does the Course Require Work Experience?: No
Type of course: Departmental Elective
Course Level:
Master TR-NQF-HE:7. Master`s Degree QF-EHEA:Second Cycle EQF-LLL:7. Master`s Degree
Mode of Delivery: E-Learning
Course Coordinator: Dr. Öğr. Üy. SELİM KALAYCI
Course Lecturer(s): Selim Kalayci
Course Assistants:

Course Objective and Content

Course Objectives: The course aims to provide an overview of technical knowledge and tools related to computational biology. The course will form the basis for the creation, processing and interpretation of datasets used in computational biology.
Course Content: The course includes basic concepts of genetics and genomics, next generation sequencing technologies, DNA sequencing, RNA sequencing, basic biology/bioinformatics databases and datasets, basic bioinformatics tools necessary for processing biological data, biological networks and creating and processing biological networks.

Learning Outcomes

The students who have succeeded in this course;
1) 1.Define basic genetics and genomics concepts;
2) 2.Recognize DNA sequencing and processing steps and computational tools used;
3) 3. Recognize RNA sequencing and processing steps and computational tools used;
4) 4. Recognize and use datasets required for protein identification and processing;
5) 5. Describe the processes of creating biological networks and use the necessary tools for processing these networks.

Course Flow Plan

Week Subject Related Preparation
1) Introduction to computational biology concepts, DNA, genetics, genomics
2) Human genome project, omics technologies
3) DNA sequencing and data analysis
4) Sequence alignment, gene variants and processing of variants
5) Genome wide association studies and related databases
6) RNA sequencing and data analysis
7) Midterm exam
8) Differential expression gene analysis and related databases
9) Proteins and related databases
10) Biological networks
11) Types of biological networks and their analysis
12) Machine learning and computational biology
13) Student paper presentations
14) Student paper presentations
15) Final exam

Sources

Course Notes / Textbooks: Selected Papers
References: Selected Papers

Course - Program Learning Outcome Relationship

Course Learning Outcomes

1

2

3

4

5
Program Outcomes
1) Being able to develop and deepen their knowledge at the level of expertise in the same or a different field, based on undergraduate level qualifications. 2 2 2 2 2
2) To be able to use the theoretical and applied knowledge at the level of expertise acquired in the field. 2 2 2 2 2
3) To be able to interpret and create new knowledge by integrating the knowledge gained in the field with the knowledge from different disciplines. 3 3 3 3 3
4) To be able to solve the problems encountered in the field by using research methods. 1 1 1 1 1
5) Being able to independently carry out a work that requires expertise in the field.
6) To be able to develop new strategic approaches for the solution of complex and unpredictable problems encountered in applications related to the field and to produce solutions by taking responsibility. 1 1 1 1 1
7) To be able to critically evaluate the knowledge and skills acquired in the field of expertise and to direct their learning. 2 2 2 2 2
8) To be able to systematically transfer current developments in the field and their own studies to groups in and outside the field, in written, verbal and visual forms, by supporting them with quantitative and qualitative data. 3 3 3 3 3
9) To be able to communicate orally and in writing using a foreign language at least at the B2 General Level of the European Language Portfolio.
10) To be able to use information and communication technologies at an advanced level along with computer software at the level required by the field. 3 3 3 3 3
11) To be able to supervise and teach these values ​​by observing social, scientific, cultural and ethical values ​​in the stages of collecting, interpreting, applying and announcing the data related to the field. 1 1 1 1 1
12) To be able to use the knowledge, problem solving and/or application skills they have internalized in their field in interdisciplinary studies. 3 3 3 3 3

Course - Learning Outcome Relationship

No Effect 1 Lowest 2 Average 3 Highest
       
Program Outcomes Level of Contribution
1) Being able to develop and deepen their knowledge at the level of expertise in the same or a different field, based on undergraduate level qualifications. 2
2) To be able to use the theoretical and applied knowledge at the level of expertise acquired in the field. 2
3) To be able to interpret and create new knowledge by integrating the knowledge gained in the field with the knowledge from different disciplines. 3
4) To be able to solve the problems encountered in the field by using research methods. 1
5) Being able to independently carry out a work that requires expertise in the field.
6) To be able to develop new strategic approaches for the solution of complex and unpredictable problems encountered in applications related to the field and to produce solutions by taking responsibility. 1
7) To be able to critically evaluate the knowledge and skills acquired in the field of expertise and to direct their learning. 2
8) To be able to systematically transfer current developments in the field and their own studies to groups in and outside the field, in written, verbal and visual forms, by supporting them with quantitative and qualitative data. 3
9) To be able to communicate orally and in writing using a foreign language at least at the B2 General Level of the European Language Portfolio.
10) To be able to use information and communication technologies at an advanced level along with computer software at the level required by the field. 3
11) To be able to supervise and teach these values ​​by observing social, scientific, cultural and ethical values ​​in the stages of collecting, interpreting, applying and announcing the data related to the field. 1
12) To be able to use the knowledge, problem solving and/or application skills they have internalized in their field in interdisciplinary studies. 3

Assessment & Grading

Semester Requirements Number of Activities Level of Contribution
Quizzes 3 % 15
Presentation 1 % 20
Midterms 1 % 25
Final Pratik 2 % 40
total % 100
PERCENTAGE OF SEMESTER WORK % 100
PERCENTAGE OF FINAL WORK %
total % 100

Workload and ECTS Credit Calculation

Activities Number of Activities Workload
Course Hours 14 42
Project 14 42
Midterms 16 25
Final 14 41
Total Workload 150