CE 405 | Course Introduction and Application Information - Department of Software Engineering

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	Weigthing	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	Engineering Knowledge: Knowledge of mathematics, science, basic engineering, computer computation, and topics specific to related engineering disciplines; the ability to use this knowledge in solving complex engineering problems		-	X	-	-	-
	1	Mathematics	-	-	-	-	-
	2	Science	-	-	-	-	-
	3	Basic engineering	-	-	-	-	-
	4	Computer computation	-	-	-	-	-
	5	Topics specific to related engineering disciplines	-	-	-	-	-
	6	The ability to use this knowledge in solving complex engineering problems	-	-	-	-	-
2	Problem Analysis: The ability to define, formulate, and analyze complex engineering problems by using fundamental science, mathematics, and engineering knowledge, while considering the relevant UN Sustainable Development Goals (SDGs) related to the problem.		-	-	X	-	-
3	Engineering Design: The ability to design creative solutions to complex engineering problems; the ability to design complex systems, processes, devices, or products that meet present and future requirements, considering realistic constraints and conditions.		-	-	-	-	-
	1	The ability to design creative solutions to complex engineering problems	-	-	-	-	-
	2	Considering realistic constraints and conditions in designing complex systems, processes, devices, or products	-	-	-	-	-
	3	The ability to design in a way that meets current and future requirements	-	-	-	-	-
4	Use of Techniques and Tools: The ability to select and use appropriate techniques, resources, and modern engineering and information technology tools, including prediction and modeling, for the analysis and solution of complex engineering problems, while being aware of their limitations		-	-	-	X	-
5	Research and Investigation: The ability to use research methods, including literature review, designing experiments, conducting experiments, collecting data, analyzing and interpreting results, for the investigation of complex engineering problems.		-	-	-	-	-
	1	The ability to use research methods, including literature review	-	-	-	-	-
	2	Designing experiments	-	-	-	-	-
	3	Conducting experiments, collecting data, analyzing and interpreting results, for the investigation of complex engineering problems	-	-	-	-	-
6	Global Impact of Engineering Practices: Knowledge of the impacts of engineering practices on society, health and safety, the economy, sustainability, and the environment within the scope of the UN Sustainable Development Goals (SDGs); awareness of the legal consequences of engineering solutions		-	-	-	-	-
	1	Global Impact of Engineering Practices: Knowledge of the impacts of engineering practices on society, health and safety, the economy, sustainability, and the environment within the scope of the UN Sustainable Development Goals (SDGs)	-	-	-	-	-
	2	Awareness of the legal consequences of engineering solutions	-	-	-	-	-
7	Ethical Behavior: Acting in accordance with the principles of the engineering profession; knowledge of ethical responsibility; awareness of acting impartially and inclusively, without discrimination in any matter. (FENG101)		-	-	-	-	-
	1	Acting in accordance with the principles of the engineering profession; knowledge of ethical responsibility	-	-	-	-	-
	2	Awareness of acting impartially and inclusively, without discrimination in any matter.	-	-	-	-	-
8	Individual and Team Work: The ability to work effectively as an individual and as a member or leader of both intra-disciplinary and interdisciplinary teams (whether face-to-face, remote, or hybrid).		-	-	-	-	-
9	Verbal and Written Communication: Taking into account the various differences of the target audience (such as education, language, profession), particularly in technical matters.		-	-	-	-	-
	1	Verbal (ENGxxx)	-	-	-	-	-
	2	Written effective communication skills. (ENGxxx)	-	-	-	-	-
10	Project Management: Knowledge of business practices such as project management and economic feasibility analysis; awareness of entrepreneurship and innovation.		-	-	-	-	-
	1	Knowledge of business practices such as project management and economic feasibility analysis; (FENG497-FENG498)	-	-	-	-	-
	2	Awareness of entrepreneurship and innovation. (FENG101)	-	-	-	-	-
11	Lifelong Learning: The ability to learn independently and continuously, adapt to new and emerging technologies, and think critically about technological changes.		-	-	-	-	-

CE 405 | Course Introduction and Application Information

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