Computational Biology Study Group

The group was organized by me and Dr Louxin Zhang (matzlx@nus.edu.sg, 874-6579) with the objective to learn more about biological sequence analysis and bioinformatics. The plan is to go thru the following books:

Durbin, R. et al. (1998). Biological Sequence Analysis. CUP.

Gibas, C. and Jambeck, P. (2001). Developing Bioinformatics Computer Skills. O'Reilly.

Other books of interest: 

Warren J. Ewens  Gregory Grant (2001).
Statistical Methods in Bioinformatics: An Introduction.
ISBN: 0387952292
Springer-Verlag New York.

Balding, DJ, Bishop, M and Cannings, C (2001).
Handbook of Statistical Genetics. Wiley

Pierre Baldi and Søren Brunak (2001).
Bioinformatics: The Machine Learning Approach (2e)
MIT Press. ISBN 0-262-02506-X
7 x 9, 400 pp.

Hastie, T. and Tibshirani, R. (2001). Statistical Learning ...

Other topics of interest: Machine Learning, Support Vector Machine ...

Biological Sequence Analysis

List of participants:

Chen, Louis
Chen Zehua
Chew Soon Huat, David
Kuk Yung Cheung, Anthony
Choi Kwok Pui
Liang Faming
Truong Young Kinh Nhue
Zhang Louxin

Time table:

08/23: Introduction and organization of the study group
08/30: Chapter 2: Pairwise Alignment
Additional handouts:
  • Speed, T. and Zhao, H. (2001). "Chromosome Maps" in "Handbook of Statistical Genetics".
  • Miller, W. (2001). "Comparison of genomic DNA sequence: solved and unsolved problems". Review 5: 391-397.
  • Waterman, MS and Vingron, M (1994). Sequence comparison significance and Poisson approximation. Statistical Science, 9: 367-381.
09/04: Chapter 2 (cont'd).
  • Von Bing to discuss how the PAM and BLOSUM matrices are derived, and how these methods can possibly be reconciled with classical statistical procedures. This would complement section 2.8 of Chapter 2 of Durbin et al. The reason for having this discussion is that there is interest from some people, and the topics seem a little too specialised to be covered next Thursday.
  • Amino Acid Substitution Matrices from Protein Blocks Steven Henikoff, Jorja G. Henikoff Proceedings of the National Academy of Sciences of the United States of America, Vol. 89, No. 22. (Nov. 15, 1992), pp. 10915-10919.
09/06: Chapter 3: Markov Chains and Hidden Markov Models
Online tutorials:

http://jedlik.phy.bme.hu/~gerjanos/HMM/node2.html http://www.sbc.su.se/~per/strbio2000/hmm.html http://www.cs.berkeley.edu/~murphyk/Bayes/hmm.html

Useful papers:

  • Rabiner, LR. (1989). A tutorial on hidden Markov models and selected applications in speech recognition. Proceedings of IEEE 77:257-286.
  • Simon Cawley Lior Pachter Marina Alexandersson (2001). Hidden Markov models for Gene Recognition. Manuscript.
  • A. P. Dempster, N. M. Laird, D. B. Rubin. Maximum Likelihood from Incomplete Data via the EM Algorithm. Journal of the Royal Statistical Society. Series B (Methodological), Vol. 39, No. 1. (1977), pp. 1-38.
09/13: A Crash Course on Genetics" by A/P Lim Tit Meng of DBS.
09/20: Chapter 3: Markov Chains and Hidden Markov Models
09/27: Chapter 3: Markov Chains and Hidden Markov Models
10/04: Chapter 4: Pairwise alignment using HMMs
Illustration of BLAST ...
10/11: Chapter 4: Pairwise alignment using HMMs (Wang Yougan)

Searls, David B. (2000). Bioinformatics Tools for Whole Genomes. Annu. Rev. Genomics Hum. Genet. 01:251-79.

10/18: Chapter 5: Profile HMMs for sequence families (Liang Faming)
10/25: Chapter 6: Multiple sequence alignment methods (Chen Zehua)
11/01: Chapter 7: Building polygenetic trees (Zhang Louxin)
11/08: Chapter 8: Probabilistic approaches to phylogeny (Zhang Louxin)
11/15: Dr Chew Fook Tim from DBS to lead the discussion
Chapter 9: Transformational grammars
11/21: Chapter 10: RNA structure analysis
Visitors to be invited: Phil Long, Wong Lim Soon, Ed Liu ...
Last update: Thu Oct 18 09:48:39 SGT 2001