COURSE UNIT TITLE

: BIOENFORMATIC

Description of Individual Course Units

Course Unit Code Course Unit Title Type Of Course D U L ECTS
BIY 4117 BIOENFORMATIC ELECTIVE 2 0 0 5

Offered By

Biology

Level of Course Unit

First Cycle Programmes (Bachelor's Degree)

Course Coordinator

PROFESSOR DOCTOR FERHAT MATUR

Offered to

Course Objective

This course is designed to introduce future biologists to bioinformatics tools and analysis methods. Upon completion of the course, students should be more comfortable working with the vast amounts of genomic data and online tools that will be relevant to their work in the coming decades.

Learning Outcomes of the Course Unit

1   have a solid understanding of the field of bioinformatics sequence analysis and many topics of molecular biology
2   have a good skill set and extensive experience using common sequence analysis tools and databases
3   have a good exposure to a variety of sequence analysis problems and understand how to solve them
4   be able to address their own sequence analysis problems or design sequence analysis software
5   know how to convey what they have learned in clearly composed documents or brief demonstrations

Mode of Delivery

Face -to- Face

Prerequisites and Co-requisites

None

Recomended Optional Programme Components

None

Course Contents

Week Subject Description
1 introduction
2 Genomic sequences
3 Sequence alignment
4 Database searching; BLAST.
5 Advanced BLAST: PSI-BLAST, Genomic DNA. Find-a-gene project
6 Multiple sequence alignment
7 Midterm
8 mRNA and gene expression introduction
9 Statistics for differential expression, multiple testing.
10 Sequence variation, phenologs, comparative genomics
11 Molecular phylogeny and evolution
12 Journal article discussion
13 Journal article discussion
14 Journal article discussion
15 Final exam

Recomended or Required Reading

Lesk, A.M. 2002. Introduction to Bioinformatics. Oxford University Press. Orengo, C., Jones, D. and Thornton, J. 2003. Bioinformatics: Genes, Proteins and Computers. Garland Science/BIOS Scientific Publishers, New York. Krawetz, S.A. and Womble, D.D. 2003. Introduction to Bioinformatics: a Theoretical and Practical Approach. Claveria JM, Notredame C, Bioinformatic for dummies. 2007. Willey publishing Humana Press, Totowa, New Jersey.Irizarry, R. A. (2017). Data analysis for the life sciences with R. Boca Raton, CRC Press, Taylor & Francis Group. Gentleman, R. (2009). R programming for bioinformatics. Boca Raton, CRC Press. Sinha, P. P. (2014). Bioinformatics with R Cookbook. Packt Publishing Ltd.

Planned Learning Activities and Teaching Methods

The course is taught in a lecture, class presentation and discussion format. All class members are expected to attend and both the lecture and take part in the discussion sessions. Besides the taught lecture, group presentations are to be prepared by the groups assigned for that week and presented to open a discussion session.

Assessment Methods

SORTING NUMBER SHORT CODE LONG CODE FORMULA
1 MTE MIDTERM EXAM
2 FIN FINAL EXAM
3 FCG FINAL COURSE GRADE MTE * 0.50 + FIN * 0.50
4 RST RESIT
5 FCGR FINAL COURSE GRADE (RESIT) MTE * 0.50 + RST * 0.50


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

Further Notes About Assessment Methods

None

Assessment Criteria

Student will be evaluated with presentations, homework presentation, final exam and attending class per week

Language of Instruction

Turkish

Course Policies and Rules

Attendance to at least 80% for the lectures is an essential requirement of this course and is the responsibility of the student. It is necessary that attendance to the lecture and homework delivery must be on time. Any unethical behavior that occurs either in presentations or in exams will be dealt with as outlined in school policy

Contact Details for the Lecturer(s)

To be announced.

Office Hours

To be announced

Work Placement(s)

None

Workload Calculation

Activities Number Time (hours) Total Work Load (hours)
Lectures 13 2 26
Preparations before/after weekly lectures 14 2 28
Preparation for final exam 1 14 14
Preparation for midterm exam 1 14 14
Preparation for quiz etc. 4 3 12
Preparing assignments 13 2 26
Final 1 2 2
Midterm 1 2 2
Quiz etc. 4 1 4
TOTAL WORKLOAD (hours) 128

Contribution of Learning Outcomes to Programme Outcomes

PO/LOPO.1PO.2PO.3PO.4PO.5PO.6PO.7PO.8PO.9PO.10PO.11PO.12PO.13PO.14
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LO.25555555
LO.355555
LO.45555555
LO.545555