Course Objectives

• Familiarity with Computational Problems in Biology
• Applying algorithmic ideas
• See real-life computational challenges
• Improve understanding of algorithms

News:

Administrivia

• Instructor: Suprakash Datta ( email: [lastname in lowercase] @ cse.yorku.ca , web: http://www.cse.yorku.ca/~[lastname in lowercase])
• Textbook: [available at the University bookstore] An Introduction to Bioinformatics Algorithms, by Neil C. Jones and Pavel A. Pevzner
  August 2004, ISBN-10: 0-262-10106-8, ISBN-13: 978-0-262-10106-6
  publisher site. and book site maintained by the authors.
• Class hours: Th 19:00-22:00 at CB 122.
• Office hours: By appointment, at CSEB 3043.
  

1. Gusfield D (1997) Algorithms on Strings, Trees and Sequences
2. A. Tozeren, S. Byers (2004): New Biology for Engineers and Computer Scientists. Pearson Prentice Hall.

• Midterm: 15%: October 27, in class
• Final : 30%: Scheduled by the registrar's office.
• HW: 30%
• Project: 25%

• Choose a topic
• Read a few (typically 2-3) papers
• Critique them
• Implement with modifications/combination of ideas (the instructor will offer suggestions)
• Present an overview as well as your results.

• Probe design
• Protein-protein interactions
• Phylogenetic trees
• Clustering
• Exon prediction
• RNA secondary structure
• Retroviruses
• Other topics can be agreed to by the student and instructor.

List of topics and Lecture schedule (tentative):

1. Introduction:
Lecture 1 (Sep 08): Administrivia: Objectives, course overview. My slides are here.
Please consider participating in the York Programming contests. Details are here.
Some notes on installing and using R are here.
2. Lecture 2 (Sep 15). Finish introduction, Genome assembly. My slides are here.
3. Lecture 3 (Sep 28): Exhaustive search algorithms. Same slides as the last lecture. Fourier methods for genome analysis.
4. Lecture 4 (Oct 4): Greedy algorithms (Ch 5). Intro to dynamic programming (Ch 6). My slides are here.
5. Lecture 5 (Oct 18): Sequence alignment using dynamic programming (Ch 6). My slides are here.
6. Lecture 6 (Oct 25): Sequence assembly using graph algorithms (Ch 7). My slides are here.
7. Lecture 7 (Nov 2): midterm. Suffix trees and repeat finding. My slides are here.
8. Lecture 8 (Nov 9): Clustering, phyllogenetic trees. My slides are here. A r-file that demonstrates some problems with the k-means algorithm is here.
9. Lecture 9 (Nov 16): phyllogenetic trees contd. Markov chains and hidden markov models. My slides are here.
10. Lecture 10 (Nov 23): Hidden markov models - contd. Randomized algorithms. My slides are here.
11. Lecture 11 (Nov 30): Extra topics. My slides are here.

• HW 1. R Basics: pdf.
• HW 2. Ab initio exon prediction: pdf. If you wish you can use this example of the use of the sapply() function.

• The starting point for R is its homepage.
• Read the introduction here. Then check out some of the documentation available here. If you are looking for a smaller document, here is one I like.
• To search for R functionality on a certain topic, use google or this search engine.
• Some tips on R are here.
• Another resource on R is here.

While it is not required for the course, this is a good opportunity to start using LaTeX. A good starting point is this page.