Course Details

COMPUTATIONAL GENOMICS

COMP463

Course Information

Semester	Course Unit Code	Course Unit Title	T+P+L	Credit	Number of ECTS Credits
7	COMP463	COMPUTATIONAL GENOMICS	3+0+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 big data (-omics data) available in molecular biology, Learn the computational methodologies for the analysis of various biological high throughput datasets, massively parallel sequencing datasets, Learn a set of algorithms that have important applications in computational genomics, but which have key applications in other fields as well. Apply the concepts learned to a real problem and convert the molecular data into medical knowledge.
Course Content	Following the Human Genome Project, the recent revolution in genomic technologies has enabled the generation of massive amounts of “omics” data. The challenge in this new era is to develop computational methods for integrating different data types and extracting complex patterns accurately and efficiently from a large volume of data. This course will give an overview of the fundamental concepts, enabling technologies and algorithms in the field of Computational Genomics. The course helps students to understand basic concepts and machine learning based methods in related areas and will enhance the student’s ability in applying them to solve real-world problems. Newly emerging disciplines, i.e. patient stratification, precision medicine and pharmacogenomics will also be discussed in this course.
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

Resources

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

In-Term Studies	Quantity	Percentage
Quiz/Küçük Sınav	1	% 20
Ödev	2	% 30
Proje/Çizim	1	% 20
Final examination	1	% 30
Total	5	% 100

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	35	35
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 5 155

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

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

Weekly Detailed Course Contents

Week	Topics	Study Materials	Materials
1	Introduction to Computational Genomics, description of the basic concepts such as the organization of DNA, proteins, cells; in silico biology.
2	Description of -omics data, big data in molecular biology and genetics
3	Description of the Human Genome Project, How to sequence the Human Genome
4	Description of the Computational Challenges in Genome Sequencing, Next Generation Sequencing (NGS) Data Analysis
5	Description of the Suffix Trees, Suffix Arrays for Read Mapping in NGS
6	Description of the Gene discovery algorithms using Hidden Markov Models (HMMs), metagenomics
7	Description of the Functional Enrichment Methods for -omics Data Analysis, Hypergeometric Test, Pathway Based Genomics
8	Description of Network Based Genomics, Sub-network identification in protein-protein interaction (PPI) networks using simulated annealing (SA) and genetic algorithms (GA)
9	Analysis of Genome-wide Association Study (GWAS) Datasets
10	Description of Metagenomics, Epigenomics, Cancer Genomics studies
11	Description of regression, applications of regression in genomics problems
12	Discovering Gene Regulatory Signals: Expectation Maximization, Gibbs sampling and related approaches
13	Description and review of the trans-omic data analysis, Personalized Medicine, Pharmacogenomics
14	Description of Artificial Intelligence Based Methods for Precision Medicine

Contribution of Learning Outcomes to Programme Outcomes

	P1	P2	P3	P4	P6	P7	P9	P10	P11	P12	P13	P14
C1	4		4	3	4	4	5	5	4
C2	4		4	3	4	4	3	2	4			4
C3	5	4	5	5	4	4	5	2	4	4	4	4
C4	5	4	5	5	4	4	5	2	4	4	4	4

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

https://sis.agu.edu.tr/oibs/bologna/progCourseDetails.aspx?curCourse=74880&lang=en

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