| MATHEMATICS (TURKISH, PHD) | |||||
| PhD | TR-NQF-HE: Level 8 | QF-EHEA: Third Cycle | EQF-LLL: Level 8 | ||
| Course Code | Course Name | Semester | Theoretical | Practical | Credit | ECTS |
| GEN2008 | Introduction to Bioinformatics | Fall | 3 | 0 | 3 | 9 |
| The course opens with the approval of the Department at the beginning of each semester |
| Language of instruction: | En |
| Type of course: | Departmental Elective |
| Course Level: | |
| Mode of Delivery: | Face to face |
| Course Coordinator : | Dr. Öğr. Üyesi ELIZABETH HEMOND |
| Course Lecturer(s): |
Dr. Öğr. Üyesi SERKAN AYVAZ Prof. Dr. SÜREYYA AKYÜZ |
| 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 Sequencing | ||
| 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 | ||
| 14) | Review | ||
| Course Notes: | Course notes or relevant hand-outs will be supplied. |
| 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 |
| Attendance | % 0 | |
| Laboratory | % 0 | |
| Application | % 0 | |
| Field Work | % 0 | |
| Special Course Internship (Work Placement) | % 0 | |
| Quizzes | % 0 | |
| Homework Assignments | 2 | % 15 |
| Presentation | % 0 | |
| Project | 1 | % 20 |
| Seminar | % 0 | |
| Midterms | 1 | % 25 |
| Preliminary Jury | % 0 | |
| Final | 1 | % 40 |
| Paper Submission | % 0 | |
| Jury | % 0 | |
| Bütünleme | % 0 | |
| Total | % 100 | |
| PERCENTAGE OF SEMESTER WORK | % 40 | |
| PERCENTAGE OF FINAL WORK | % 60 | |
| Total | % 100 | |
| Activities | Number of Activities | Workload | |
| Course Hours | 14 | 42 | |
| Laboratory | |||
| Application | |||
| Special Course Internship (Work Placement) | |||
| Field Work | |||
| Study Hours Out of Class | 14 | 94 | |
| Presentations / Seminar | |||
| Project | 1 | 30 | |
| Homework Assignments | |||
| Quizzes | |||
| Preliminary Jury | |||
| Midterms | 2 | 60 | |
| Paper Submission | |||
| Jury | |||
| Final | 1 | 2 | |
| Total Workload | 228 | ||
| No Effect | 1 Lowest | 2 Low | 3 Average | 4 High | 5 Highest |
| Program Outcomes | Level of Contribution |