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.
Introduction to Bioinformatics (BIO 310)
Programs\Type | Required | Core Elective | Area Elective |
BA- Political Science | |||
BA-Cultural Studies | |||
BA-Cultural Studies | |||
BA-Economics | |||
BA-Economics | |||
BA-International Studies | |||
BA-International Studies | |||
BA-Management | |||
BA-Management | |||
BA-Political Sci.&Inter.Relat. | |||
BA-Political Sci.&Inter.Relat. | |||
BA-Social & Political Sciences | |||
BA-Visual Arts&Visual Com.Des. | |||
BA-Visual Arts&Visual Com.Des. | |||
BS-Biological Sci.&Bioeng. | * | ||
BS-Computer Science & Eng. | * | ||
BS-Computer Science & Eng. | * | ||
BS-Electronics Engineering | * | ||
BS-Electronics Engineering | * | ||
BS-Industrial Engineering | * | ||
BS-Manufacturing Systems Eng. | * | ||
BS-Materials Sci. & Nano Eng. | * | ||
BS-Materials Science & Eng. | * | ||
BS-Mechatronics | * | ||
BS-Mechatronics | * | ||
BS-Microelectronics | |||
BS-Molecular Bio.Gen.&Bioeng | * | ||
BS-Telecommunications | * |
CONTENT
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
Update Date:
ASSESSMENT METHODS and CRITERIA
Percentage (%) | |
Final | 30 |
Exam | 30 |
Assignment | 10 |
Presentation | 5 |
Homework | 25 |
RECOMENDED or REQUIRED READINGS
Textbook |
There is no required text book. Recommended textbooks: P. Compeau, P. Pevzner. Bioinformatics Algorithms: An Active Learning Approach. Active |
Readings |
Published papers. |