CHILD DEVELOPMENT (TURKISH)
Bachelor TR-NQF-HE: Level 6 QF-EHEA: First Cycle EQF-LLL: Level 6

Course Introduction and Application Information

Course Code Course Name Semester Theoretical Practical Credit ECTS
MBG1002 Introduction to Bioinformatics Fall 3 0 3 5
This catalog is for information purposes. Course status is determined by the relevant department at the beginning of semester.

Basic information

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 : Dr. Öğr. Üyesi ELIZABETH HEMOND
Course Lecturer(s): Prof. Dr. SÜREYYA AKYÜZ
Dr. Öğr. Üyesi SERKAN AYVAZ
Recommended Optional Program Components: There is none.
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.

Learning Outcomes

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.

Course Content

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.

Weekly Detailed Course Contents

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
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 I
14) Suffix trees II

Sources

Course Notes / Textbooks: Biyoinformatik ders notları haftalık olarak verilecektir.
Course material will be supplied weekly.
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"

Evaluation System

Semester Requirements Number of Activities Level of Contribution
Homework Assignments 2 % 15
Project 1 % 20
Midterms 1 % 25
Final 1 % 40
Total % 100
PERCENTAGE OF SEMESTER WORK % 40
PERCENTAGE OF FINAL WORK % 60
Total % 100

ECTS / Workload Table

Activities Number of Activities Duration (Hours) Workload
Course Hours 14 3 42
Study Hours Out of Class 14 5 70
Project 1 10 10
Midterms 1 2 2
Final 1 2 2
Total Workload 126

Contribution of Learning Outcomes to Programme Outcomes

No Effect 1 Lowest 2 Low 3 Average 4 High 5 Highest
           
Program Outcomes Level of Contribution
1) To gain both theoretical and practical knowledge about physical, cognitive, social-emotional aspects of child development. 4
2) To display actions in professional practice based on ethical principles and values. 5
3) To adopt the principle of lifelong learning, using efficient ways for accessing information. 5
4) To know the stages of child development and to be able to use models / theories efficiently for supporting children's cognitive, affective and psycho-motor development. 5
5) To plan, implement and evaluate professional projects, research and events with a sense of social responsibility, 5
6) To be able to use effective communication methods in counseling and child and family-based guidance. 3
7) To be sensitive to the child and family-related issues taking into account the child's stages of development, and to implement strategies for personal development of child and education methods which are vital for leading effective and productive life. 5
8) To use the education and communication materials according to the child development stage, and to create proper educational environment. 5
9) To take responsibilities in the field of child development and education using interdisciplinary approach, and to use information technologies, and to engage in projects and activities. 5
10) To use health information technologies for research in the field of child development. 5
11) To be able to monitor occupational information using at least one foreign language, to collaborate and communicate with colleagues at international level. 5
12) To become a good example for colleagues and society, and represent efficiently the professional identity using advanced knowledge about child development. 5