COURSE INTRODUCTION AND APPLICATION INFORMATION

Course Name
Programming for Bioinformatics
Code
Semester
Theory
(hour/week)
Application/Lab
(hour/week)
Local Credits
ECTS
CE 405
Fall/Spring
3
0
3
5
Prerequisites
None
Course Language
English
Course Type
Elective
Course Level
First Cycle
Mode of Delivery -
Teaching Methods and Techniques of the Course
Course Coordinator
Course Lecturer(s) -
Assistant(s) -
Course Objectives Following the dissemination of the first results of the Human Genome Project in 2004 life sciences researchers have access to genome related information (DNA sequence, protein sequence etc.) that would revolutionize the clinical practice as we know it. However this information is kept in various databases and in various formats so that one has to employ special algorithms and tools for the analysis. This course aims to provide an introduction to the terminology, problems, algorithms and tools related to bioinformatics, which is one of the hottest research topics of computer science recently.
Learning Outcomes The students who succeeded in this course;
  • will be able to define the bioinformatics problems,
  • will be able to explain biological databases as well as the data formats,
  • will be able to discuss the famous bioinformatics algorithms,
  • will be able to classify statistical analysis techniques of genomic information,
  • will be able to write Python codes using bioinformatics algorithms.
Course Description The course covers bioinformatics tools/software related with biological sequence (DNA, RNA, protein) analysis, molecular structure prediction, functional genomics, pharmacogenomics and proteomics, biological pathway analysis.
Related Sustainable Development Goals

 



Course Category

 Core Courses
Major Area Courses
Supportive Courses
Media and Managment Skills Courses
Transferable Skill Courses

 

WEEKLY SUBJECTS AND RELATED PREPARATION STUDIES

Week Subjects Required Materials
1 Introduction to Bioinformatics Lecturer notes
2 Basic biology information, Central dogma, DNA and RNA structure, gene, and proteins. Hairpins, loops, alpha helix and beta sheet. Bioinformatics for beginners, Genes, Genomes, Molecular Evolution, Databases and Analytical Tools. Supratim Choudhuri. Elservier, 2014 Chp. 1 N. C. Jones and P. A. Pevzner, An Introduction to Bioinformatics Algorithms, MIT press, 2004 Ch.3
3 Variables, data types, operators, return, if/else block. Importing modules, imported functions, declarations Bioinformatics Programming Using Python: Practical Programming for Biological Data, Mitchell L Model, O’Reilly, 2009. ISBN: 9781449382902. Chapter 1,2
4 Lists, dictionaries, tuples, interactive user input, comment blocks, for, while loops, break and continue, iterators, Time, sys, os modules, file reading and writing Bioinformatics Programming Using Python: Practical Programming for Biological Data, Mitchell L Model, O’Reilly, 2009. ISBN: 9781449382902. Chapter 3,4
5 Classes. Regular expressions and regex module. Biopython module, pairwise alignment. Accessing ncbi. FASTA and Genbank file formats Bioinformatics Programming Using Python: Practical Programming for Biological Data, Mitchell L Model, O’Reilly, 2009. ISBN: 9781449382902. Chapter 5
6 Ara sınav / Midterm
7 Finding k-mers in in DNA sequences Lecture Notes
8 Numpy, scipy and matplotlib modules. Matrices and sparse matrices. Lecture Notes
9 Needleman-Wunsch, Waterman-Smith-Bayer, and other sequence alignment algorithms Lecture Notes
10 Gotoh alignment and affine gap cost algorithm Lecture Notes
11 K-means clustering and Hierarchical clustering Python for Bioinformatics, Sebastian Bass, CRC Press, 2016. ISBN: 9781584889304. Chapter 10
12 Ara Sınav / Midterm II
13 Analysis of next generation data Lecture Notes
14 RNA folding and motif analysis Bioinformatics for beginners, Genes, Genomes, Molecular Evolution, Databases and Analytical Tools. Supratim Choudhuri. Elservier, 2014 Chapter 7
15 Multi sequence alignment and phylogenetic tree construction Python for Bioinformatics, Sebastian Bass, CRC Press, 2016. ISBN: 9781584889304. Chapter 23
16 Review of the Semester  
Course Notes/Textbooks

Suggested but not required text: (1) Bioinformatics for beginners, Genes, Genomes, Molecular Evolution, Databases and Analytical Tools. Supratim Choudhuri. Elservier, 2014 (2) N. C. Jones and P. A. Pevzner, An Introduction to Bioinformatics Algorithms, MIT press, 2004 (3) J. Xiong, Essential Bioinformatics, Cambridge University Press, 2006. (4) S. Bassi, Python for Bioinformatics, CRC Press , 2010.
Suggested Readings/Materials Related Research Papers

 

EVALUATION SYSTEM

Semester Activities Number Weigthing
Participation
1
10
Laboratory / Application
Field Work
Quizzes / Studio Critiques
Portfolio
Homework / Assignments
4
10
Presentation / Jury
Project
1
30
Seminar / Workshop
Oral Exam
Midterm
2
30
Final Exam
1
20
Total

Weighting of Semester Activities on the Final Grade
8
80
Weighting of End-of-Semester Activities on the Final Grade
1
20
Total

ECTS / WORKLOAD TABLE

Semester Activities Number Duration (Hours) Workload
Course Hours
(Including exam week: 16 x total hours)
16
3
48
Laboratory / Application Hours
(Including exam week: 16 x total hours)
16
Study Hours Out of Class
16
1
16
Field Work
Quizzes / Studio Critiques
Portfolio
Homework / Assignments
4
2
Presentation / Jury
Project
1
25
Seminar / Workshop
Oral Exam
Midterms
2
17
Final Exams
1
19
    Total
150

 

COURSE LEARNING OUTCOMES AND PROGRAM QUALIFICATIONS RELATIONSHIP

#
 Program Competencies/Outcomes
* Contribution Level
1
2
3
4
5
1

To have adequate knowledge in Mathematics, Science and Industrial Engineering; to be able to use theoretical and applied information in these areas to model and solve Industrial Engineering problems.

 X
2

To be able to identify, formulate and solve complex Industrial Engineering problems by using state-of-the-art methods, techniques and equipment; to be able to select and apply proper analysis and modeling methods for this purpose.

 X
3

To be able to analyze a complex system, process, device or product, and to design with realistic limitations to meet the requirements using modern design techniques. 

 X
4

To be able to choose and use the required modern techniques and tools for Industrial Engineering applications; to be able to use information technologies efficiently.
5

To be able to design and do simulation and/or experiment, collect and analyze data and interpret the results for investigating Industrial Engineering problems and Industrial Engineering related research areas.
6

To be able to work efficiently in Industrial Engineering disciplinary and multidisciplinary teams; to be able to work individually.

 X
7

To be able to communicate effectively in Turkish, both orally and in writing; to be able to author and comprehend written reports, to be able to prepare design and implementation reports, to present effectively; to be able to give and receive clear and comprehensible instructions
8

To have knowledge about contemporary issues and the global and societal effects of Industrial Engineering practices on health, environment, and safety; to be aware of the legal consequences of Industrial Engineering solutions.

 X
9

To be aware of professional and ethical responsibility; to have knowledge of the standards used in Industrial Engineering practice.
10

To have knowledge about business life practices such as project management, risk management, and change management; to be aware of entrepreneurship and innovation; to have knowledge about sustainable development.
11

To be able to collect data in the area of Industrial Engineering; to be able to communicate with colleagues in a foreign language.

 X
12

To be able to speak a second foreign at a medium level of fluency efficiently.
13

To recognize the need for lifelong learning; to be able to access information, to be able to stay current with developments in science and technology; to be able to relate the knowledge accumulated throughout the human history to Industrial Engineering.

 X

*1 Lowest, 2 Low, 3 Average, 4 High, 5 Highest