COURSE UNIT TITLE

: BIOINFORMATICS

Description of Individual Course Units

Course Unit Code Course Unit Title Type Of Course D U L ECTS
KIM 5104 BIOINFORMATICS ELECTIVE 3 0 0 8

Offered By

Graduate School of Natural and Applied Sciences

Level of Course Unit

Second Cycle Programmes (Master's Degree)

Course Coordinator

PROFESSOR DOCTOR LEVENT ÇAVAŞ

Offered to

M.Sc.Biochemistry
Chemistry
Chemistry
Ph.D in Biochemistry

Course Objective

Bioinformatics is a multidisciplinary field related to solving of biological data based problems by using techniques in applied mathematics, statistics, computer science, artificial intelligence. The main aim of this course is to teach some algorithms developed on the gene finding, motif extraction, protein structure analysis, drug design, protein-protein interactions to students.

Learning Outcomes of the Course Unit

1   Explain bioinformatics,
2   Know the databases used in bioinformatics and obtain data,
3   Use softwares developed in bioinformatics,
4   Follow and compare published papers in bioinformatics,
5   Work in multidisciplinary groups in bioinformatics field.

Mode of Delivery

Face -to- Face

Prerequisites and Co-requisites

None

Recomended Optional Programme Components

None

Course Contents

Week Subject Description
1 1.Introduction to bioinformatics
2 2.Data Banks in Bioinformatics-1
3 3.Data Banks in Bioinformatics-2
4 4.Immune Epitop Analysis
5 5.Protein Analysis-Prot Param and Secondary Structure analysis
6 6.Multiple Sequence Analysis-Clustal Omega
7 7.Bioactive peptides and in silico analysis
8 8.Protein structure and drug design
9 9 Sample Examples
10 10.Determination of unfolded and transmembrane regions in proteins
11 11.Allergen Proteins and in silico analysis
12 12.Current papers in Bioinformatics and student presentations
13 13.Current papers in Bioinformatics and student presentations
14 14. Current papers in Bioinformatics and student presentations

Recomended or Required Reading

1. Gibas C., ve Jambeck P., (2005). Developing Bioinformatics Computer Skills
2. Jones N.C. ve Pevzner P.A. (2005). An Introduction to Bioinformatics Algorithms (Computational Molecular Biology)
3. Lesk A.M. (2005). Introduction to Bioinformatics. Second Edition. Oxford Press.
4. Web based bioinformatics data bases (Expasy and Clustal Omega etc).
5. Swiss Model, CB-Docking

Planned Learning Activities and Teaching Methods

1. Lecture,
2. Questions-answers,
3. Active learning strategies,
4. Presentations.

Assessment Methods

SORTING NUMBER SHORT CODE LONG CODE FORMULA
1 MTE MIDTERM EXAM
2 ASG ASSIGNMENT
3 FIN FINAL EXAM
4 FCG FINAL COURSE GRADE MTE * 0.25 +ASG * 0.35 +FIN * 0.40
5 RST RESIT
6 FCGR FINAL COURSE GRADE (RESIT) MTE * 0.25 +ASG * 0.35 + RST * 0.40


*** Resit Exam is Not Administered in Institutions Where Resit is not Applicable.

Further Notes About Assessment Methods

None

Assessment Criteria

The questions based on the relationship between learning objects and learning targets will be asked to students.

Language of Instruction

English

Course Policies and Rules

Due to the COVID-19 pandemics, participations in the lectures are not compulsory.

Contact Details for the Lecturer(s)

Dokuz Eylul University,
Faculty of Science, Department of Chemistry
Email: levent.cavas@deu.edu.tr

Office Hours

Wednesday 17.00-18.00

Work Placement(s)

None

Workload Calculation

Activities Number Time (hours) Total Work Load (hours)
Lectures 11 3 33
Preparations before/after weekly lectures 14 3 42
Preparation for midterm exam 1 20 20
Preparation for final exam 1 30 30
Reading relevant papers from Bioinformatics based journals 4 10 40
Preparing presentations 1 21 21
Final 1 2 2
Midterm 1 2 2
TOTAL WORKLOAD (hours) 190

Contribution of Learning Outcomes to Programme Outcomes

PO/LOPO.1PO.2PO.3PO.4PO.5PO.6PO.7PO.8PO.9PO.10PO.11
LO.153552552515
LO.254552552515
LO.353552552515
LO.453552552515
LO.553552552515