Introduction to Bioinformatics (BIO 310)

2019 Spring
Faculty of Engineering and Natural Sciences
Mol.Bio.Genetic&Bioengin.(BIO)
3
6.00 / 6.00 ECTS (for students admitted in the 2013-14 Academic Year or following years)
Öznur Taştan Okan -otastan@sabanciuniv.edu,
English
Undergraduate
MATH203 IF100
Formal lecture,Interactive lecture,Recitation
Interactive,Learner centered,Communicative,Discussion based learning,Task based learning
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CONTENT

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.

OBJECTIVE

To supply the students with the foundations in bioinformatics.

LEARNING OUTCOME

Understand and appreciate the role of bioinformatics in solving biological problems.
Use established bioinformatics databases and web servers
Demonstrate working proficiency with sequence search and alignment (local, global, pairwise multiple sequence alignment algorithms.) algorithms.
Acquire an elementary understanding of Hidden Markov Models and their applications to problems which involve sequence learning.
Gain a solid perspective of sequence, structure and function relationships in proteins.
Acquire a necessary foundation in machine learning methods for classification and their use to address biological questions.
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.

Have a grasp of gene expression analysis and perform basic expression analysis on gene expression data
Obtain a conceptual knowledge of gene set enrichment analysis and be able to analyze and interpret the results coming from omics data.
Recognize the increasing role of biological networks in analyzing biological systems

ASSESSMENT METHODS and CRITERIA

  Percentage (%)
Final 30
Exam 30
Participation 5
Presentation 5
Homework 20
Other 10

RECOMENDED or REQUIRED READINGS

Textbook

There is no required text book.

Recommended textbooks:

P. Compeau, P. Pevzner. Bioinformatics Algorithms: An Active Learning Approach. Active
Learning Publishers, 2nd Ed. Vol. 1 and Vol.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
(3rd edition also OK). ISBN - 978-0199651566.
- N. Jones and P. Pevzner. An Introduction to Bioinformatics Algorithms (Computational Molecular Biology), MIT Press, 2004.

Readings

Published papers.