Course Details

BIOINFORMATICS

Course Information

Semester	Course Unit Code	Course Unit Title	T+P+L	Credit	Number of ECTS Credits
7	COMP462	BIOINFORMATICS	0+3+0	5	5

Course Details

Language of Instruction	English
Level of Course Unit	Bachelor's Degree
Department / Program	COMPUTER ENGINEERING
Type of Program	Formal Education
Type of Course Unit	Elective
Course Delivery Method	Face To Face
Objectives of the Course	Learn different types and sources of data available in bioinformatics Learn the fundamental computational problems in molecular biology and genetics Learn a core set of widely used algorithms in bioinformatics Learn a set of algorithms that have important applications in bioinformatics, but which have key applications outside of biology as well Apply the concepts learned to a real problem
Course Content	Explain the fundamental computational problems in molecular biology and genomics Explain different types and sources of data available in bioinformatics Implement a core set of widely used algorithms in bioinformatics Apply a bioinformatics method to a real problem
Course Methods and Techniques
Prerequisites and co-requisities	None
Course Coordinator	None
Name of Lecturers	Asist Prof.Dr. VEHBİ ÇAĞRI GÜNGÖR burcu.gungor@agu.edu.tr
Assistants	None
Work Placement(s)	No

Recommended or Required Reading

Planned Learning Activities and Teaching Methods

Activities are given in detail in the section of "Assessment Methods and Criteria" and "Workload Calculation"

Assessment Methods and Criteria

ECTS Allocated Based on Student Workload

Activities	Quantity	Duration	Total Work Load
Yazılı Sınav	1	30	30
Ev Ödevi	2	20	40
Sunum	1	5	5
Proje	1	30	30
Kısa Sınav	1	1	1
Okuma	1	1	1
Yüz Yüze Ders	1	3	3
Final Sınavı	1	40	40
Total Work Load			Number of ECTS Credits 5 150

Course Learning Outcomes: Upon the successful completion of this course, students will be able to:

No	Learning Outcomes
1	Explain the fundamental computational problems in molecular biology and genomics
2	Explain different types and sources of data available in bioinformatics
3	Implement a core set of widely used algorithms in bioinformatics
4	Apply a bioinformatics method to a real problem

Weekly Detailed Course Contents

Week	Topics	Study Materials	Materials
1	Description of the basic terms in Molecular Biology, Genetics and bioinformatics: a) The organization of DNA, proteins, cell; b) In silico biology
2	Review of real world applications of bioinformatics, Introduction of Fragment Assembly Problem
3	Description of Fragment Assembly Problem, Overlap-Layout-Consensus Algorithm
4	Description of Pairwise alignment of biomolecular sequences: Global alignment
5	Description of Local alignment, Semi-global alignment
6	Description of similarity search algorithms such as BLAST algorithm; description of the scoring in similarity matrices: PAM and BLOSUM matrices
7	Description of the Multiple sequence alignment: a) Iterative Methods, b) Structure Based Methods
8	Description of the scoring in multiple alignments
9	Description and review of the high-throughput biological data analysis methods: Detecting differential gene expression, multiple hypothesis testing, false-discovery-rate methods.
10	Description and review of the clustering and classification algorithms for gene expression data analysis.
11	Description and review of the clustering and classification algorithms for metagenomic data analysis.
12	Description of the protein-protein interaction, protein/DNA interaction, gene/protein interaction networks
13	Construction and analysis of large scale biological networks
14	Description and review of the machine learning approaches for integrating data in molecular biology, genetics and medicine.

Contribution of Learning Outcomes to Programme Outcomes

Contribution: 1: Very Slight 2:Slight 3:Moderate 4:Significant 5:Very Significant

https://sis.agu.edu.tr/oibs/bologna/progCourseDetails.aspx?curCourse=74872&lang=en