| ECONOMICS | |||||
| Bachelor | TR-NQF-HE: Level 6 | QF-EHEA: First Cycle | EQF-LLL: Level 6 | ||
| Course Code | Course Name | Semester | Theoretical | Practical | Credit | ECTS |
| MBG2003 | Computation for Biological Sciences I | Fall | 2 | 2 | 3 | 7 |
| 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 : | 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. |
|
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 |
| Evaluation and analysis of general biological and genome sequencing data using related computational tools efficiently. |
| 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 |
| 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 |
| 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 | |
| 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 | ||
| No Effect | 1 Lowest | 2 Low | 3 Average | 4 High | 5 Highest |
| Program Outcomes | Level of Contribution | |
| 1) | As a world citizen, she is aware of global economic, political, social and ecological developments and trends. | 2 |
| 2) | He/she is equipped to closely follow the technological progress required by global and local dynamics and to continue learning. | 2 |
| 3) | Absorbs basic economic principles and analysis methods and uses them to evaluate daily events. | 2 |
| 4) | Uses quantitative and statistical tools to identify economic problems, analyze them, and share their findings with relevant stakeholders. | 2 |
| 5) | Understands the decision-making stages of economic units under existing constraints and incentives, examines the interactions and possible future effects of these decisions. | 1 |
| 6) | Comprehends new ways of doing business using digital technologies. and new market structures. | 2 |
| 7) | Takes critical approach to economic and social problems and develops analytical solutions. | 1 |
| 8) | Has the necessary mathematical equipment to produce analytical solutions and use quantitative research methods. | 2 |
| 9) | In the works he/she contributes, observes individual and social welfare together and with an ethical perspective. | 2 |
| 10) | Deals with economic problems with an interdisciplinary approach and seeks solutions by making use of different disciplines. | 1 |
| 11) | Generates original and innovative ideas in the works she/he contributes as part of a team. | 2 |