ECTS - Introduction to Bioinformatics
Introduction to Bioinformatics (SE446) Course Detail
| Course Name | Course Code | Season | Lecture Hours | Application Hours | Lab Hours | Credit | ECTS |
|---|---|---|---|---|---|---|---|
| Introduction to Bioinformatics | SE446 | Area Elective | 3 | 0 | 0 | 3 | 5 |
| Pre-requisite Course(s) |
|---|
| N/A |
| Course Language | English |
|---|---|
| Course Type | Elective Courses |
| Course Level | Bachelor’s Degree (First Cycle) |
| Mode of Delivery | Face To Face |
| Learning and Teaching Strategies | Lecture. |
| Course Lecturer(s) |
|
| Course Objectives | The objective of the course is to provide necessary knowledge and skills related to computational techniques for mining the large amount of biological data. In this course the applications of the computational techniques in bioinformatics will be introduced. |
| Course Learning Outcomes |
The students who succeeded in this course;
|
| Course Content | DNA and protein sequence alignment, phylogenetic trees, protein structure prediction, motive findin, microarray data analysis, gene/protein networks. |
Weekly Subjects and Releated Preparation Studies
| Week | Subjects | Preparation |
|---|---|---|
| 1 | Introduction | Chapters 1,2,3 (main text) |
| 2 | Producing and Analyzing Sequence Alignments | Chapter 4 |
| 3 | Pairwise Sequence Alignment and Database Searching | Chapter 5 |
| 4 | Pairwise Sequence Alignment and Database Searching | Chapter 5 |
| 5 | Patterns, Profiles, and Multiple Alignments | Chapter 6 |
| 6 | Patterns, Profiles, and Multiple Alignments | Chapter 6 |
| 7 | Recovering Evolutionary History | Chapter 7 |
| 8 | Building Phylogenetic Trees | Chapter 8 |
| 9 | Obtaining Secondary Structure from Sequence | Chapter 11 |
| 10 | Predicting Secondary Structures | Chapter 12 |
| 11 | Modeling Protein Structure | Chapter 13 |
| 12 | Clustering Methods and Statistics | Chapter 16 |
| 13 | Clustering Methods and Statistics | Chapter 16 |
| 14 | Clustering Methods and Statistics | Chapter 17 |
| 15 | Final Examination Period | Review of topics |
| 16 | Final Examination Period | Review of topics |
Sources
| Course Book | 1. M. Zvelebil and J. O. Baum, Understanding Bioinformatics, Garland Science, 2008 |
|---|---|
| Other Sources | 2. N. C. Jones and P. A. Pevzner, An Introduction to Bioinformatics Algorithms, MIT press, 2004 |
| 3. A. M. Lesk, Introduction to Bioinformatics, Oxford University Press, 2002 | |
| 4. D. Mount, Bioinformatics: Sequence and genome analysis, Cold Spring Harbor Laboratory Press, 2001 | |
| 5. T. Jiang, Y. Xu, and M. Zhang, eds. Current Topics in Computational Molecular Biology, MIT press, 2002 |
Evaluation System
| Requirements | Number | Percentage of Grade |
|---|---|---|
| Attendance/Participation | - | - |
| Laboratory | - | - |
| Application | - | - |
| Field Work | - | - |
| Special Course Internship | - | - |
| Quizzes/Studio Critics | - | - |
| Homework Assignments | 1 | 20 |
| Presentation | - | - |
| Project | 1 | 30 |
| Report | - | - |
| Seminar | - | - |
| Midterms Exams/Midterms Jury | 1 | 20 |
| Final Exam/Final Jury | 1 | 30 |
| Toplam | 4 | 100 |
| Percentage of Semester Work | 70 |
|---|---|
| Percentage of Final Work | 30 |
| Total | 100 |
Course Category
| Core Courses | X |
|---|---|
| Major Area Courses | |
| Supportive Courses | |
| Media and Managment Skills Courses | |
| Transferable Skill Courses |
The Relation Between Course Learning Competencies and Program Qualifications
| # | Program Qualifications / Competencies | Level of Contribution | ||||
|---|---|---|---|---|---|---|
| 1 | 2 | 3 | 4 | 5 | ||
| 1 | Engineering Knowledge: Knowledge in mathematics, science, fundamental engineering, computational science, and related engineering disciplines; the ability to apply this knowledge to solve complex engineering problems. | |||||
| 2 | Problem Analysis: The ability to identify, formulate, and analyze complex engineering problems using fundamental science, mathematics, and engineering knowledge, while keeping in mind the relevant UN Sustainable Development Goals. | |||||
| 3 | Engineering Design: The ability to design creative solutions to complex engineering problems; the ability to design complex systems, processes, devices, or products to meet current and future requirements, taking into account realistic constraints and conditions. | |||||
| 4 | Techniques and Tool Usage: The ability to select and use appropriate techniques, resources, and modern engineering and information tools, including estimation and modeling, for the analysis and solution of complex engineering problems, while being aware of their limitations. | |||||
| 5 | Research and Investigation: The ability to use research methods, including literature review, experimental design, experiment execution, data collection, analysis and interpretation of results, for the investigation of complex engineering problems. | |||||
| 6 | Global Impact of Engineering Applications: Information about the impacts of engineering applications on society, health and safety, the economy, sustainability and the environment within the framework of the UN Sustainable Development Goals; awareness of the legal consequences of engineering solutions. | |||||
| 7 | Engineering Ethics: Awareness of ethical responsibility and adherence to engineering professional principles; impartiality and inclusivity without discrimination. | |||||
| 8 | Individual and Teamwork: The ability to work effectively individually and as a team member or leader in interdisciplinary and multidisciplinary teams (face-to-face, remote, or mixed). | |||||
| 9 | Oral and Written Communication: The ability to communicate effectively orally and in writing on technical topics, taking into account the diverse differences of the target audience (education, language, profession, etc.). | |||||
| 10 | Project Management: Knowledge of business practices such as project management and economic feasibility analysis; awareness of entrepreneurship and innovation. | |||||
| 11 | Lifelong Learning: Lifelong learning skills encompassing the ability to learn independently and continuously, adapt to new and emerging technologies, and think critically about technological changes. | |||||
ECTS/Workload Table
| Activities | Number | Duration (Hours) | Total Workload |
|---|---|---|---|
| Course Hours (Including Exam Week: 16 x Total Hours) | 16 | 3 | 48 |
| Laboratory | |||
| Application | |||
| Special Course Internship | |||
| Field Work | |||
| Study Hours Out of Class | 16 | 2 | 32 |
| Presentation/Seminar Prepration | |||
| Project | |||
| Report | |||
| Homework Assignments | 3 | 5 | 15 |
| Quizzes/Studio Critics | |||
| Prepration of Midterm Exams/Midterm Jury | 2 | 10 | 20 |
| Prepration of Final Exams/Final Jury | 1 | 15 | 15 |
| Total Workload | 130 | ||