CE 405 | Course Introduction and Application Information - Department of Genetics and Bioengineering

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	Group Work Problem Solving Lecture / Presentation
National Occupation Classification	-
Course Coordinator	Öğr. Gör. İlker KORKMAZ
Course Lecturer(s)	Doç. Dr. Osman DOLUCA
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

#	Content	PC Sub	* Contribution Level
#	Content	PC Sub	1	2	3	4	5
1	will be able to define the bioinformatics problems,
2	will be able to explain biological databases as well as the data formats,
3	will be able to discuss the famous bioinformatics algorithms,
4	will be able to classify statistical analysis techniques of genomic information,
5	will be able to write Python codes using bioinformatics algorithms.

Course Description

The course covers bioinformatics tools/software related to biological sequence (DNA, RNA, protein) analysis, molecular structure prediction, functional genomics, pharmacogenomics and proteomics, biological pathway analysis.

Course Category	Core Courses
	Major Area Courses
	Supportive Courses
	Media and Management Skills Courses
	Transferable Skill Courses

Week	Subjects	Related Preparation	Learning Outcome
1	Introduction to Bioinformatics	Bioinformatics for beginners, Genes, Genomes, Molecular Evolution, Databases and Analytical Tools. Supratim Choudhuri. Elsevier, 2014 Chp. 1
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	Midterm
7	Finding k-mers in in DNA sequences	Data Algorithms, Mahmoud Parsian, O’Reilly, ISBN: 9781491906187, Chapter 17
8	Numpy, scipy and matplotlib modules. Matrices and sparse matrices.	Bioinformatics Programming Using Python: Practical Programming for Biological Data, Mitchell L Model, O’Reilly, 2009. ISBN: 9781449382902. Chapter 10
9	Needleman-Wunsch, Waterman-Smith-Bayer, and other sequence alignment algorithms	Multiple Biological Sequence Alignment: Scoring Functions, Algorithms and Evaluation, Ken Nguyen, Xuan Guo, Yi Pan, ISBN: 978-1-118-22904-0, Chapter 2
10	Gotoh alignment and affine gap cost algorithm	Multiple Biological Sequence Alignment: Scoring Functions, Algorithms and Evaluation, Ken Nguyen, Xuan Guo, Yi Pan, ISBN: 978-1-118-22904-0, Chapter 3
11	K-means clustering and Hierarchical clustering	Python for Bioinformatics, Sebastian Bass, CRC Press, 2016. ISBN: 9781584889304. Chapter 10
12	Midterm II
13	RNA folding and motif analysis	Bioinformatics for beginners, Genes, Genomes, Molecular Evolution, Databases and Analytical Tools. Supratim Choudhuri. Elsevier, 2014 Chapter 7
14	Multi sequence alignment and phylogenetic tree construction	Python for Bioinformatics, Sebastian Bass, CRC Press, 2016. ISBN: 9781584889304. Chapter 23
15	Semester Review
16	Final Exam

Course Notes/Textbooks

Bioinformatics for beginners, Genes, Genomes, Molecular Evolution, Databases and Analytical Tools. Supratim Choudhuri. Elsevier, 2014

Suggested Readings/Materials

N. C. Jones and P. A. Pevzner, An Introduction to Bioinformatics Algorithms, MIT press, 2004

J. Xiong, Essential Bioinformatics, Cambridge University Press, 2006.

S. Bassi, Python for Bioinformatics, CRC Press , 2010.

Multiple Biological Sequence Alignment: Scoring Functions, Algorithms and Evaluation, Ken Nguyen, Xuan Guo, Yi Pan, ISBN: 978-1-118-22904-0

Data Algorithms, Mahmoud Parsian, O’Reilly, ISBN: 9781491906187

Semester Activities	Number	Weighting	LO 1	LO 2	LO 3	LO 4	LO 5
Participation	1	10
Laboratory / Application
Field Work
Quizzes / Studio Critiques
Portfolio
Homework / Assignments	4	10
Presentation / Jury
Project	1	30
Seminar / Workshop
Oral Exams
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

Semester Activities	Number	Duration (Hours)	Workload
Theoretical Course Hours (Including exam week: 16 x total hours)	16	3	48
Laboratory / Application Hours (Including exam week: '.16.' x total hours)	16		0
Study Hours Out of Class	14	1	14
Field Work			0
Quizzes / Studio Critiques			0
Portfolio			0
Homework / Assignments	4	2	8
Presentation / Jury			0
Project	1	25	25
Seminar / Workshop			0
Oral Exam			0
Midterms	2	18	36
Final Exam	1	19	19
		Total	150

#	PC Sub	Program Competencies/Outcomes	* Contribution Level
#	PC Sub	Program Competencies/Outcomes	1	2	3	4	5
1	To have adequate knowledge in Mathematics, Science and Genetics and Bioengineering; to be able to use theoretical and applied information in these areas on complex engineering problems.		-	-	X	-	-
2	To be able to identify, define, formulate, and solve complex Genetics and Bioengineering problems; to be able to select and apply proper analysis and modeling methods for this purpose.		-	-	-	-	X
3	To be able to design a complex system, process, device or product under realistic constraints and conditions, in such a way as to meet the requirements; to be able to apply modern design methods for this purpose.		-	-	-	-	-
4	To be able to devise, select, and use modern techniques and tools needed for analysis and solution of complex problems in Genetics and Bioengineering applications; to be able to use information technologies effectively.		-	-	X	-	-
5	To be able to design and conduct experiments, gather data, analyze and interpret results for investigating complex engineering problems or Genetics and Bioengineering research topics.		-	-	-	-	-
6	To be able to work efficiently in Genetics and Bioengineering disciplinary and multi-disciplinary teams; to be able to work individually.		-	-	-	-	-
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 global and social impact of Genetics and Bioengineering practices on health, environment, and safety; to have knowledge about contemporary issues as they pertain to engineering; to be aware of the legal ramifications of Genetics and Bioengineering solutions.		-	-	-	-	-
9	To be aware of ethical behavior, professional and ethical responsibility; to have knowledge about standards utilized in Genetics and Bioengineering applications.		-	-	X	-	-
10	To have knowledge about industrial practices such as project management, risk management, and change management; to have awareness of entrepreneurship and innovation; to have knowledge about sustainable development.		-	-	-	-	-
11	To be able to collect data in the area of Genetics and Bioengineering, and to be able to communicate with colleagues in a foreign language.		-	-	-	-	-
12	To be able to speak a second foreign language at a medium level of fluency efficiently.		-	-	X	-	-
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 Genetics and Bioengineering.		-	-	-	-	-