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 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 Has adequate knowledge in mathematics, science, and computer engineering-specific subjects; uses theoretical and practical knowledge in these areas to solve complex engineering problems. X
2 Identifies, defines, formulates, and solves complex engineering problems; selects and applies appropriate analysis and modeling methods for this purpose. X
3 Designs a complex system, process, device, or product to meet specific requirements under realistic constraints and conditions; applies modern design methods for this purpose. X
4 Develops, selects, and uses modern techniques and tools necessary for the analysis and solution of complex problems encountered in computer engineering applications; uses information technologies effectively. X
5 Designs experiments, conducts experiments, collects data, analyzes and interprets results for the investigation of complex engineering problems or research topics specific to the discipline of computer engineering. X
6 Works effectively in disciplinary and multidisciplinary teams; gains the ability to work individually. X
7 Communicates effectively in Turkish, both orally and in writing; writes effective reports and understands written reports, prepares design and production reports, makes effective presentations, gives and receives clear and understandable instructions.
8 Knows at least one foreign language; writes effective reports and understands written reports, prepares design and production reports, makes effective presentations, gives and receives clear and understandable instructions.
9 Has awareness of the necessity of lifelong learning; accesses information, follows developments in science and technology, and continuously improves oneself. X
10 Acts in accordance with ethical principles and has awareness of professional and ethical responsibility. X
11 Has knowledge about the standards used in computer engineering applications.
12 Has knowledge about workplace practices such as project management, risk management, and change management. X
13 Gains awareness about entrepreneurship and innovation.
14 Has knowledge about sustainable development.
15 Has knowledge about the health, environmental, and safety impacts of computer engineering applications in universal and societal dimensions and the contemporary issues reflected in the field of engineering. X
16 Gains awareness of the legal consequences of engineering solutions.
17 Analyzes, designs, and expresses numerical computation and digital representation systems. X
18 Uses programming languages and appropriate computer engineering concepts to solve computational problems. X

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