MBG1002 Introduction to BioinformaticsBahçeşehir UniversityDegree Programs BIOENGINEERING (ENGLISH, THESIS)General Information For StudentsDiploma SupplementErasmus Policy StatementNational QualificationsBologna Commission
BIOENGINEERING (ENGLISH, THESIS)
Master TR-NQF-HE: Level 7 QF-EHEA: Second Cycle EQF-LLL: Level 7

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: Departmental Elective
Course Level:
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) An understanding of the advanced concepts of Mathematics (calculus, analysis, linear algebra, differential equations, statistics), Natural Sciences (physics, chemistry, biology), and Engineering Sciences (electronics, material science, mechanics, thermal and fluid systems, control, signal and image processing, microcontrollers) relevant to Biomedical Engineering.
2) An ability to use at an advanced level the techniques, skills, and modern engineering tools (including software) necessary for engineering practice.
3) The capability of designing and conducting advanced experiments and of analyzing and evaluating data.
4) An ability to design the components of complex systems and processes under realistic constraints.
5) Acquisition of the skills needed to develop products (device, system, process) which are used in diagnosis, prevention, treatment and cure of diseases.
6) An ability to communicate knowledge and opinion efectively, both oral and in writing.
7) An ability to assume initiative and individual resposibility, and to cooperate with team-mates from other disciplines.
8) A kowledge of the current needs and problems of society, and an awareness of the social and global impact of engineering solutions.
9) Assimilation of the ethics and responsibilities of the profession.
10) Recognition of the importance of life-long learning, and participation therein.