Syllabus Application
BIO 310
Introduction to Bioinformatics
Faculty
Faculty of Engineering and Natural Sciences
Semester
Spring 2025-2026
Course
BIO 310 -
Introduction to Bioinformatics
Time/Place
Time
Week Day
Place
Date
11:40-12:30
Mon
FASS-G018
Feb 16-May 22, 2026
09:40-11:30
Thu
FASS-G018
Feb 16-May 22, 2026
Level of course
Undergraduate
Course Credits
SU Credit:3, ECTS:6, Basic:6
Prerequisites
MATH 203 and IF 100
Corequisites
BIO 310L
Course Type
Lecture
Instructor(s) Information
Öznur Taştan Okan
- Email: otastan@sabanciuniv.edu
Course Information
Catalog Course Description
Analysis of genes and proteins. Gene finding methods; sequence patterns, Hidden Markov Models. Bioinformatics software on the net. Protein folding problem; Homology modelling and threading algolrithms. Gibbs free energy and contact potentials. Clustering of structures; Structural databases. Structural genomics.
Course Learning Outcomes:
| 1. | Understand and appreciate the role of bioinformatics in solving biological problems. |
|---|---|
| 2. | Use established bioinformatics databases and web servers |
| 3. | Demonstrate working proficiency with sequence search and alignment (local, global, pairwise multiple sequence alignment algorithms.) algorithms. |
| 4. | Acquire an elementary understanding of Hidden Markov Models and their applications to problems which involve sequence learning. |
| 5. | Gain a solid perspective of sequence, structure and function relationships in proteins. |
| 6. | Acquire a necessary foundation in machine learning methods for classification and their use to address biological questions. |
| 7. | Gain hands-on experience in the implementation of major clustering algorithms (k-means, hierarchical clustering) and their use in the analysis of biological datasets (e.g., gene expression) and be able to perform clustering analysis. |
| 8. | Have a grasp of gene expression analysis and perform basic expression analysis on gene expression data |
| 9. | Obtain a conceptual knowledge of gene set enrichment analysis and be able to analyze and interpret the results coming from omics data. |
| 10. | Recognize the increasing role of biological networks in analyzing biological systems |
Course Objective
To supply the students with the foundations in bioinformatics.
Sustainable Development Goals (SDGs) Related to This Course:
| Good Health and Well-being | |
| Affordable and Clean Energy |
Course Materials
Resources:
There are no required textbooks for this course. Required readings will be posted on SuCourse. Students are expected to check the weekly resources regularly.
Optional textbooks:
P. Compeau and P. Pevzner. Bioinformatics Algorithms: An Active Learning Approach. Active Learning Publishers, 2nd Edition, Volumes 1 and 2, 2015.
Supplementary website: http://bioinformaticsalgorithms.com
J. Pevsner. Bioinformatics and Functional Genomics, 3rd Edition, 2015.
A. Lesk. Introduction to Bioinformatics, 4th Edition, Oxford University Press, 2014.
(The 3rd edition is also acceptable.) ISBN: 978-0199651566.
N. Jones and P. Pevzner. An Introduction to Bioinformatics Algorithms (Computational Molecular Biology). MIT Press, 2004.
P. Baldi and S. Brunak. Bioinformatics: The Machine Learning Approach, 2nd Edition. MIT Press.
R. Durbin, S. R. Eddy, A. Krogh, and G. Mitchison. Biological Sequence Analysis: Probabilistic Models of Proteins and Nucleic Acids. Cambridge University Press.
Optional textbooks:
P. Compeau and P. Pevzner. Bioinformatics Algorithms: An Active Learning Approach. Active Learning Publishers, 2nd Edition, Volumes 1 and 2, 2015.
Supplementary website: http://bioinformaticsalgorithms.com
J. Pevsner. Bioinformatics and Functional Genomics, 3rd Edition, 2015.
A. Lesk. Introduction to Bioinformatics, 4th Edition, Oxford University Press, 2014.
(The 3rd edition is also acceptable.) ISBN: 978-0199651566.
N. Jones and P. Pevzner. An Introduction to Bioinformatics Algorithms (Computational Molecular Biology). MIT Press, 2004.
P. Baldi and S. Brunak. Bioinformatics: The Machine Learning Approach, 2nd Edition. MIT Press.
R. Durbin, S. R. Eddy, A. Krogh, and G. Mitchison. Biological Sequence Analysis: Probabilistic Models of Proteins and Nucleic Acids. Cambridge University Press.
Technology Requirements:
Laptops will be used in the computational lab.