ADVERTISING
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
MBG2004	Computation for Biological Sciences II	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):	Prof. Dr. SÜREYYA AKYÜZ
Course Objectives:	This class intended to provide advanced information to computational tools for biology. 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;
• Genomes: Biological sequence analysis, comparative genomics, RNA structure, sequence alignment,

• Pathways and Network analysis: Gene expression, clustering / classification, motifs, Bayesian networks, microRNAs, regulatory genomics, epigenomics

• Molecular Evolution: Gene / species trees, phylogenomics, coalescent, personal genomics, population genomics, human ancestry, recent selection, disease mapping
• Comparative Genomics/Transcriptomics: Compare current NGS mapping methodologies and genomes both theoretically and practically,

• Genome Assembly: With the advances in sequencing technologies it has become much more feasible, and affordable, to assemble and annotate the genomic sequence of most organisms, including large eukaryote genomes. However, high quality genome assembly and annotation still represent a major challenge. In this class, we will discuss current methods and technologies to perform best assembly.

• Current advances in systems and integrative biology (Review)

Course Content

Advanced level sequence data, searching and alignment, structural data, genome sequencing, genome analysis, genetic variation, gene and protein expression, and biological networks and pathways. We will evaluate the results of these analyzes by using advances functional genomics, transcriptomics, enrichment methods

Weekly Detailed Course Contents

Week	Subject	Related Preparation
1)	Introduction	Read the Syllabus and Describe Project Details
2)	Advanced Comparative Genomics
3)	Human Genomic Variations (Population Genetics approach)	Practical Lab Assignment 1 by using https://usegalaxy.org/
4)	Variant Detection and Methods Single Nucleotide Variations (SNPs) Annotation
5)	SNP Genotyping Methods and Micro Array Analyses	Practical Lab Assignment 2 by using https://usegalaxy.org
6)	Advanced Variant Detection and Methods Structural Variations (SVs) Copy Number Variation (CNVs)
7)	Gene Ontology and Gene Annotation	Practical Lab Assignment 3 by using https://usegalaxy.org/
8)	Advance Transcriptomics (RNA-Seq) Gene set enrichment analysis
9)	Midterm Review	Midterm Sınavı
10)	Genome/Transcriptomics Assembly and Annotation	Practical Lab Assignment 4 by using https://usegalaxy.org/
11)	Advance Molecular Phylogenetics/Phylogenomics
12)	Molecular Evolution/PaleoGenomics	Final Project by using https://usegalaxy.org/
13)	MetaGenomics
14)	Review “Current advances in systems and integrative biology? ”	Final Project submission and Final Project Exam

Sources

Course Notes / Textbooks:	Ders notları haftalık olarak verilecektir. Course material will be provided weekly.
References:	1) Durbin, Richard, Sean R. Eddy, Anders Krogh, et al. Biological Sequence Analysis: Probabilistic Models of Proteins and Nucleic Acids. Cambridge University Press, 1998. 2) Jones, Neil C., and Pavel Pevzner. An Introduction to Bioinformatics Algorithms. MIT Press, 2004. ISBN: 9780262101066.

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