ELEC5810  Introduction to Bioinformatics Algorithms

 

                                           Time:        Monday 6:30-8:50PM, Spring 2017

                                           Venue:      Room 4582

                                           Instructor: Weichuan Yu (eeyu AT ust DOT HK)

                                          

 

 

This is an introductory course on bioinformatics.  It will cover basic biological knowledge, common biological data acquisition techniques, popular data analysis algorithms and their applications. The major content of this course is computation-oriented.  

 

This course is designed for graduate students and senior undergraduate students who are interested in the emerging area of bioinformatics and computational biology. Basic knowledge in applied mathematics, probability, algorithms and programming is required. Knowledge in biology is a plus but not a must.

 

Starting from 2016, we have adopted a new blended-learning style, which means that you will have more flexibility in terms of managing your learning time and controlling your own learning pace. Concretely, the course consists of in-classroom discussions and lectures, online learning and quizzes, homework,  mid-term exam, a final project report, and a project presentation.  Grade is based on homework (10%), online quizzes (10%), class participation (10%), mid-term exam (20%),  and final project (50%, including the course presentation).  The final project is a literature review study in which each student focuses on one specific bioinformatics problem. He/she will complete a comprehensive literature survey with his/her own comments on pros and cons of the methods and/or suggestions on how to improve the existing methods. The project consists of a written report (20-25 pages) and a class presentation (25-30 minutes duration). Topic of the project will be assigned or chosen by himself/herself (with the instructor’s approval).

 

A tentative schedule is described as follows. More materials (lecture notes, homework assignment, etc.) can be found at http://canvas.ust.hk

 

Week 1            Overview and Fundamentals of Molecular Biology

Week 2            Genome Sequencing I: different sequencing techniques

Week 3            Genome Sequencing II: genome mapping theoretical analysis

Week 4            Sequence Analysis I: Exact string matching (Gusfield Chapter 1)

Week 5            Sequence Analysis II: Suffix trees (Gusfield Chapter 5 and 6)

Week 6            Sequence Analysis III: Sequence alignment (global alignment,

                         local alignment, multiple sequence alignment)

Week 7            Genome-Wide SNP Data Analysis I

Week 8            Genome-Wide SNP Data Analysis II

Week 9            Midterm (April 3)

Week 10          FDR

Week 11          Course Presentation

Week 12          Course Presentation

 

Note: If feasible, we may schedule course presentations into the same week. 

  

Textbook
An Introduction to Bioinformatics Algorithms.
Neil C. Jones and Pavel A. Pevzner, The MIT Press, 2004.

 

References
1. Algorithms on Strings, Trees, and Sequences.
   Dan Gusfield.  Cambridge University Press, 1997
2. Bioinformatics: Genes, Proteins and Computers
   C.A. Orengo, D.T. Jones & J.M. Thornton, BIOS Scientific Publishers, 2003
3. Bioinformatics Basics: Applications in Biological Sciences and Medicine
   H. H. Rashidi and L. K. Buehler, CRC Press, 2000
4. Introduction to Proteomics: Tools for the New Biology
   Daniel C. Liebler, Humana Press, 2002
5. L. Hunter, Artificial Intelligence and Molecular Biology
   http://www.aaai.org/AITopics/pmwiki/pmwiki.php/AITopics/ArtificialIntelligenceAndMolecularBiology