BUSINESS ADMINISTRATION | |||||
Bachelor | TR-NQF-HE: Level 6 | QF-EHEA: First Cycle | EQF-LLL: Level 6 |
Course Code | Course Name | Semester | Theoretical | Practical | Credit | ECTS |
MBG1002 | Introduction to Bioinformatics | Spring Fall |
3 | 0 | 3 | 5 |
This catalog is for information purposes. Course status is determined by the relevant department at the beginning of semester. |
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 : | Dr. Öğr. Üyesi ELIZABETH HEMOND |
Course Lecturer(s): |
Prof. Dr. SÜREYYA AKYÜZ Dr. Öğr. Üyesi SERKAN AYVAZ |
Recommended Optional Program Components: | There is none. |
Course Objectives: | This course aims to prepare the students to work in the interdisciplinary area, bioinformatics that marry the advances in high-performance computing with the exploiting information resources of the human genome and related data. |
The students who have succeeded in this course; 1. Recognize the working in interdisciplinary teams of biologists, biochemists, medical researchers, geneticists, and computer engineers. 2. Perform sophisticated searches over enormous databases, and to interpret results. 3. Perform genomic comparisons, display genes and large genomic regions in Genome Browsers. 4. Recognize the basic bioinformatics problems and their solutions, including: fragment assembly, gene finding, protein folding and microarray studies. 5. Anayze the results in probabilistic terms using statistical significance. 6. Recognize the sequencing techniques, inherent computational problems, possible solutions. 7. Define Markov Model building and its usage for gene prediction. 8. Define computational methods for analysis of microarray data, and discuss the interpretations of gene expression from this data. 9. Discuss ethical, legal, and social issues associated with the Human Genome Project and its outcomes. |
Bioinformatics is a rapidly growing field that integrates molecular biology, statistics, and computer science. This course is devoted to the mathematical models and computer algorithms of DNA and protein sequence analysis. In this course, the students will learn many of the popular tools for performing bioinformatics analysis and you will be introduced to the thinking that drives these algorithms. Various existing bioinformatics methods will be critically described and the strengths and limitations of each will be discussed. |
Week | Subject | Related Preparation |
1) | Introduction: Probability and statistics in a nut shell. | |
2) | Analysis of nucleic acid and protein sequences. | |
3) | Molecular Biology Databases on the Web. | |
4) | Bioinformatics softwares on the internet | |
5) | How the Genome is Studied, Maps and Sequences, The Human Genome Project | |
6) | Sequencing: Next Gen, Exome, Shotgun | |
7) | Fragment Assembly Problem; Sequence Alignment Models: Shortest Common Superstring, Reconstruction, Multicontig, Graph Model | |
8) | Restriction mapping: a) Double Digest Problem, b) Partial Digest Problem | |
9) | Computational Gene Hunting, Gene finding methods; sequence patterns, Hidden Markov Models. | |
10) | Bioinformatics approaches to gene expression | |
11) | Protein folding problem | |
12) | Genome Rearrangements | |
13) | Suffix trees I | |
14) | Suffix trees II |
Course Notes / Textbooks: | Biyoinformatik ders notları haftalık olarak verilecektir. Course material will be supplied weekly. |
References: | 1) Pevsner J., Bioinformatics and Functional Genomics, Wiley-Liss, 2009 2) Mount D.W., Bioinformatics: Sequence and Genome Analysis (2nd edition), Cold Spring Harbor Laboratory Press, 2004 3) Krane D.E., Raymer M.L., Fundamental Concepts of Bioinformatics, Benjamin Cummings, 2003 4) Setubal C., Meidanis J., Introduction to Computational Molecular Biology, PWS Publishing, 1997" |
Semester Requirements | Number of Activities | Level of Contribution |
Homework Assignments | 2 | % 15 |
Project | 1 | % 20 |
Midterms | 1 | % 25 |
Final | 1 | % 40 |
Total | % 100 | |
PERCENTAGE OF SEMESTER WORK | % 40 | |
PERCENTAGE OF FINAL WORK | % 60 | |
Total | % 100 |
Activities | Number of Activities | Duration (Hours) | Workload |
Course Hours | 14 | 3 | 42 |
Study Hours Out of Class | 14 | 5 | 70 |
Project | 1 | 10 | 10 |
Midterms | 1 | 2 | 2 |
Final | 1 | 2 | 2 |
Total Workload | 126 |
No Effect | 1 Lowest | 2 Low | 3 Average | 4 High | 5 Highest |
Program Outcomes | Level of Contribution | |
1) | Being able to identify problems and ask right questions | |
2) | Having problem solving skills and developing necessary analytical attitude | |
3) | Comprehending theoretical arguments along with counter arguments in detail | |
4) | Gaining awareness of lifelong learning and being qualified for pursuing graduate education | |
5) | Applying theoretical concepts in project planning | |
6) | Communicating efficiently by accepting differences and carrying out compatible teamwork | |
7) | Increasing efficiency rate in business environment | |
8) | Developing innovative and creative solutions in face of uncertainty | |
9) | Researching to gather information for understanding current threats and opportunities in business | |
10) | Being aware of the effects of globalization on society and business while deciding | |
11) | Possessing digital competence and utilizing necessary technology | |
12) | Communicating in at least one foreign language in academic and daily life | |
13) | Possessing managing skills and competence | |
14) | Deciding with the awareness of the legal and ethical consequences of business operations | |
15) | Expressing opinions that are built through critical thinking process in business and academic environment |