Department of Biomedical Engineering

BME 311 | Course Introduction and Application Information

Course Name

Computational Bioinformatics

Code	Semester	Theory (hour/week)	Application/Lab (hour/week)	Local Credits	ECTS
BME 311	FALL	2	2	3	6

Prerequisites	SE 113 To succeed (To get a grade of at least DD) or SE 115 To succeed (To get a grade of at least DD)
Course Language	English
Course Type	ELECTIVE_COURSE
Course Level	First Cycle
Mode of Delivery	Face-To-Face
Teaching Methods and Techniques of the Course	-
National Occupational Classification Code	-
Course Coordinator	Doç. Dr. Osman Doluca
Course Lecturer(s)	Doç. Dr. Osman Doluca
Assistant(s)	Araş. Gör. Duygu Geçkin

Course Objectives

This course aims to teach the algorithms used for analysis of biological data and file formats that these biological data are stored in online databases.

Learning Outcomes

The students who succeeded in this course;

Name	Description	PC Sub	* Contribution Level
Name	Description	PC Sub	1	2	3	4	5
LO1	Will be able to use simple programming techniques	1.4		X
LO2	Will be able to explain biological data formats and online databases	1.5		X
LO3	Will be able to collect biological data over the internet using coded methods	5.3		X
LO4	Will be able to solve simple bioformatics problems using coding	3.1				X
LO5	Will be able to apply a given algorithm to solve a given bioinformatics problem	1.6					X

Course Description

The course covers algorithms used for sequence alignment, motif discovery in DNA and protein sequences, clustering protein networks, protein complex discovery using interaction networks, phylogenetic tree construction and next generation sequencing.

Related Sustainable Development Goals

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

WEEKLY SUBJECTS AND RELATED PREPARATION STUDIES

Week	Subjects	Required Materials	Learning Outcome
1	Introduction to Python	Chapter 2, Choudhuri, S. (2014). Bioinformatics for Beginners: Genes, Genomes, Molecular Evolution, Databases and Analytical Tools. Elsevier. ISBN: 9780124105102	LO1
2	Variables, if/else blocks, importing modules	Chapter 5, Choudhuri, S. (2014). Bioinformatics for Beginners: Genes, Genomes, Molecular Evolution, Databases and Analytical Tools. Elsevier. ISBN: 9780124105102	LO1
3	Functions and modules	Chapter 5, Choudhuri, S. (2014). Bioinformatics for Beginners: Genes, Genomes, Molecular Evolution, Databases and Analytical Tools. Elsevier. ISBN: 9780124105102	LO1
4	Collections and comprehensions	Chapter 5, Jones, N. C., & Pevzner, P. A. (2004). An Introduction to Bioinformatics Algorithms. MIT Press. ISBN: 9780262101066	LO2
5	Reading from FASTA and Genbank files	Chapter 5, Choudhuri, S. (2014). Bioinformatics for Beginners: Genes, Genomes, Molecular Evolution, Databases and Analytical Tools. Elsevier. ISBN: 9780124105102	LO2
6	Retrieving data from NCBI using API I	Lecture notes	LO3
7	Retrieving data from NCBI using API II	Lecture notes	LO3
8	Midterm		-
9	Using RegEx to find motifs	Chapter 5, Jones, N. C., & Pevzner, P. A. (2004). An Introduction to Bioinformatics Algorithms. MIT Press. ISBN: 9780262101066	LO4
10	Sequence alignment I	Chapter 6, Choudhuri, S. (2014). Bioinformatics for Beginners: Genes, Genomes, Molecular Evolution, Databases and Analytical Tools. Elsevier. ISBN: 9780124105102	LO4
11	Sequence alignment II	Chapter 6, Choudhuri, S. (2014). Bioinformatics for Beginners: Genes, Genomes, Molecular Evolution, Databases and Analytical Tools. Elsevier. ISBN: 9780124105102	LO4
12	Using K-mer to find repeats in DNA sequences	Chapter 7, Choudhuri, S. (2014). Bioinformatics for Beginners: Genes, Genomes, Molecular Evolution, Databases and Analytical Tools. Elsevier. ISBN: 9780124105102	LO2
13	Clustering biological data using K-means	Chapter 10, Jones, N. C., & Pevzner, P. A. (2004). An Introduction to Bioinformatics Algorithms. MIT Press. ISBN: 9780262101066	LO2
14	UPMGA and Neighbor-joining tree construction algorithms	Chapter 10, Jones, N. C., & Pevzner, P. A. (2004). An Introduction to Bioinformatics Algorithms. MIT Press. ISBN: 9780262101066	LO5
15	Review of the Semester	Lecture Notes	LO5
16	Final Exam		-

Course Notes/Textbooks	"Choudhuri S. (2014). Bioinformatics for Beginners: Genes Genomes Molecular Evolution Databases and Analytical Tools. Elsevier. ISBN: 9780124105102 Jones N. C. & Pevzner P. A. (2004). An Introduction to Bioinformatics Algorithms. MIT Press. ISBN: 9780262101066"
Suggested Readings/Materials	Bassi S. (2017). Python for bioinformatics. CRC Press. ISBN: 9781351976954

EVALUATION SYSTEM

Semester Activities	Number	Weighting	LO1	LO2	LO3	LO4	LO5
Homework / Assignments	1	10			X	X
Project	1	20	X			X	X
Midterm	1	30	X	X	X
Final Exam	1	40	X	X	X	X	X
Total	4	100

ECTS / WORKLOAD TABLE

Semester Activities	Number	Duration (Hours)	Workload
Participation	-	-	-
Theoretical Course Hours	16	2	32
Laboratory / Application Hours	16	2	32
Study Hours Out of Class	14	3	42
Field Work	-	-	-
Quizzes / Studio Critiques	-	-	-
Portfolio	-	-	-
Homework / Assignments	1	14	14
Presentation / Jury	-	-	-
Project	1	16	16
Seminar / Workshop	-	-	-
Oral Exams	-	-	-
Midterms	1	20	20
Final Exam	1	24	24
		Total	180

COURSE LEARNING OUTCOMES AND PROGRAM QUALIFICATIONS RELATIONSHIP

#	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, computation, and related engineering discipline-specific topics; the ability to apply this knowledge to solve complex engineering problems.
	1	Mathematics
	2	Science
	3	Basic Engineering
	4	Computation		LO1
	5	Related engineering discipline-specific topics		LO2
	6	The ability to apply this knowledge to solve complex engineering problems					LO5
2	Problem Analysis: Ability to identify, formulate and analyze complex engineering problems using basic knowledge of science, mathematics and engineering, and considering the UN Sustainable Development Goals relevant to the problem being addressed.
3	Engineering Design: The ability to devise creative solutions to complex engineering problems; the ability to design complex systems, processes, devices or products to meet current and future needs, considering realistic constraints and conditions.
	1	Ability to design creative solutions to complex engineering problems				LO4
	2	Ability to design complex systems, processes, devices or products to meet current and future needs, considering realistic constraints and conditions
4	Use of Techniques and Tools: Ability to select and use appropriate techniques, resources, and modern engineering and computing tools, including estimation and modeling, for the analysis and solution of complex engineering problems, while recognizing their limitations.
5	Research and Investigation: Ability to use research methods to investigate complex engineering problems, including literature research, designing and conducting experiments, collecting data, and analyzing and interpreting results.
	1	Literature research for the study of complex engineering problems
	2	Designing experiments
	3	Ability to use research methods, including conducting experiments, collecting data. analyzing and interpreting results		LO3
6	Global Impact of Engineering Practices: Knowledge of the impacts of engineering practices on society, health and safety, economy, sustainability, and the environment, within the context of the UN Sustainable Development Goals; awareness of the legal implications of engineering solutions.
	1	Knowledge of the impacts of engineering practices on society, health and safety, economy, sustainability, and the environment, within the context of the UN Sustainable Development Goals
	2	Awareness of the legal implications of engineering solutions
7	Ethical Behavior: Acting in accordance with the principles of the engineering profession, knowledge about ethical responsibility; awareness of being impartial, without discrimination, and being inclusive of diversity.
	1	Acting in accordance with the principles of the engineering profession, knowledge about ethical responsibility ethical responsibility
	2	Awareness of being impartial and inclusive of diversity, without discriminating on any subject
8	Individual and Teamwork: Ability to work effectively, individually and as a team member or leader on interdisciplinary and multidisciplinary teams (face-to-face, remote or hybrid).
	1	Ability to work individually and within the discipline
	2	Ability to work effectively as a team member or leader in multidisciplinary teams (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) on technical issues.
	1	Ability to communicate verbally
	2	Ability to communicate effectively in writing
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
	2	Awareness of entrepreneurship and innovation
11	Lifelong Learning: Lifelong learning skills that include being able to learn independently and continuously, adapting to new and developing technologies, and thinking questioningly about technological changes.