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Bachelor	TR-NQF-HE: Level 6	QF-EHEA: First Cycle	EQF-LLL: Level 6

Course Introduction and Application Information

Course Code	Course Name	Semester	Theoretical	Practical	Credit	ECTS
MBG2003	Computation for Biological Sciences I	Spring	2	2	3	7

This catalog is for information purposes. Course status is determined by the relevant department at the beginning of semester.

Basic information

Language of instruction:	English
Type of course:	Non-Departmental Elective
Course Level:	Bachelor’s Degree (First Cycle)
Mode of Delivery:	Face to face
Course Coordinator :	Assist. Prof. CEMALETTİN BEKPEN
Course Lecturer(s):	Assist. Prof. SERKAN AYVAZ Prof. Dr. SÜREYYA AKYÜZ
Course Objectives:	This course covers the methods and tools for learning foundations of computational biology combining theory with practice. We cover both foundational topics in computational biology, and current research frontiers. We study fundamental techniques, recent advances in the field, and work directly with current large-scale biological datasets.

Learning Outcomes

The students who have succeeded in this course;
At the end of the course, students will be able to:

Genomes: Biological sequence analysis, comparative genomics, RNA structure, sequence alignment, Next Generation Sequences, Whole Genome Mapping, Transcriptomics

Networks: Gene expression, clustering / classification, motifs, Bayesian networks, microRNAs, regulatory genomics, epigenomics

Evolution: Gene / species trees, phylogenomics, coalescent, personal genomics, population genomics, human ancestry, recent selection, disease mapping

Course Content

Evaluation and analysis of general biological and genome sequencing data using related computational tools efficiently.

Weekly Detailed Course Contents

Week	Subject	Related Preparation
1)	Introduction
2)	Genomics data mining (Biological Databases)	Practical Lab Assignment 1
3)	Sequencing (Methods and Sequencing technologies)
4)	Whole Genome Mapping and Personal Genomics
5)	Downstream Analysis of Variant Detection and Methods Single Nucleotide Variations (SNPs) Structural Variations (SVs) Copy Number Variation (CNVs) (Part I)	Practical Lab Assignment 2 (Based on Galaxy server, please check www.usegalaxy.org)
6)	Genomic Variation How and Why,we detect Genomic Variation(Genome Wide Analysis (GWAS)
7)	Downstream Analysis of Variant Detection and MethodsSingle Nucleotide Variations (SNPs)Structural Variations (SVs)Copy Number Variation (CNVs)(Part II)	Practical Lab Assignment 3 (Based on Galaxy server, please check www.usegalaxy.org)
8)	RNA_Seq Analysis and Transcriptomics (Part I)
9)	Review for the midterm exam	Midterm Exam
10)	RNA_Seq Analysis and Transcriptomics (Part II)
11)	Genome Assembly and Annotation	Practical Lab Assignment 4 (Based on Galaxy server, please check www.usegalaxy.org)
12)	Comparative Computational Biology Methods
13)	Comparative Genomics
14)	Final Review

Sources

Course Notes / Textbooks:	Ders notları verilecektir. Course notes will be supplied.
References:	Computational Biology Series Editors: Dress, A., Linial, M., Troyanskaya, O., Vingron, M. ISSN: 1568-2684, 2009

Evaluation System

Semester Requirements	Number of Activities	Level of Contribution
Project	1	% 40
Midterms	1	% 20
Final	1	% 40
Total		% 100
PERCENTAGE OF SEMESTER WORK		% 20
PERCENTAGE OF FINAL WORK		% 80
Total		% 100

ECTS / Workload Table

Activities	Number of Activities	Duration (Hours)	Workload
Course Hours	14	4	56
Study Hours Out of Class	14	7	98
Project	5	4	20
Midterms	1	2	2
Final	1	2	2
Total Workload			178

Contribution of Learning Outcomes to Programme Outcomes

No Effect	1 Lowest	2 Low	3 Average	4 High	5 Highest

	Program Outcomes	Level of Contribution
1)	To prepare students to become communication professionals by focusing on strategic thinking, professional writing, ethical practices, and the innovative use of both traditional and new media	2
2)	To be able to explain and define problems related to the relationship between facts and phenomena in areas such as Advertising, Persuasive Communication, and Brand Management
3)	To critically discuss and interpret theories, concepts, methods, tools, and ideas in the field of advertising
4)	To be able to follow and interpret innovations in the field of advertising
5)	To demonstrate a scientific perspective in line with the topics they are curious about in the field.
6)	To address and solve the needs and problems of the field through the developed scientific perspective
7)	To recognize and understand all the dynamics within the field of advertising
8)	To analyze and develop solutions to problems encountered in the practical field of advertising