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 3 0 0 3 5
Pre-requisite Course(s)
N/A
Course Language English
Course Type N/A
Course Level Bachelor’s Degree (First Cycle)
Mode of Delivery Face To Face
Learning and Teaching Strategies Lecture.
Course Coordinator
Course Lecturer(s)
Course Assistants
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;
  • Apply DNA and protein sequence alignment techniques
  • Build phylogenetic trees
  • Apply techniques to predict protein structure
  • Gain skills for clustering methods used in bioinformatics
  • Analyze gene/protein networks
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 Acquires sufficient knowledge in mathematics, natural sciences, and related engineering disciplines; gains the ability to use theoretical and applied knowledge in these fields in solving complex engineering problems.
2 Gains the ability to identify, define, formulate, and solve complex engineering problems; acquires the skill to select and apply appropriate analysis and modeling methods for this purpose.
3 Gains the ability to design a complex system, process, device, or product to meet specific requirements under realistic constraints and conditions, and applies modern design methods for this purpose.
4 Develops the skills to develop, select, and use modern techniques and tools necessary for the analysis and solution of complex problems encountered in industrial engineering applications; gains the ability to effectively use information technologies.
5 Gains the ability to design experiments, conduct experiments, collect data, analyze and interpret results for the investigation of complex engineering problems or discipline-specific research topics.
6 Acquires the ability to work effectively in intra-disciplinary and multidisciplinary teams, as well as individual work skills.
7 Acquires effective oral and written communication skills in Turkish; at least one foreign language proficiency; gains the ability to write effective reports, understand written reports, prepare design and production reports, make effective presentations, and give and receive clear instructions.
8 Develops awareness of the necessity of lifelong learning; gains the ability to access information, follow developments in science and technology, and continuously renew oneself.
9 Acquires the consciousness of adhering to ethical principles, and gains professional and ethical responsibility awareness. Gains knowledge about the standards used in industrial engineering applications.
10 Gains knowledge about practices in the business life such as project management, risk management, and change management. Develops awareness about entrepreneurship and innovation. Gains knowledge about sustainable development.
11 Gains knowledge about the universal and social dimensions of the impacts of industrial engineering applications on health, environment, and safety, as well as the problems reflected in the engineering field of the era. Gains awareness of the legal consequences of engineering solutions.
12 Gains skills in the design, development, implementation, and improvement of integrated systems involving human, material, information, equipment, and energy.
13 Gains knowledge about appropriate analytical and experimental methods, as well as computational methods, for ensuring system integration.

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