1 an introduction to bioinformatics algorithms
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An introduction to
Bioinformatics Algorithms
Qi Liu
email: [email protected]
Presented by Liu Qi
Description of the Course
introduce the basic computational issues and
methods used in molecular biology
Topics will include basic algorithms for
alignment of biological sequences and
structures. These include, for example,
dynamic programming algorithms for
alignment, motif definition and computation,
Hidden Markov Models, neural networks etc.
Presented By Liu Qi
Related Courses
University of Washington (Computational Biology)
Tel Aviv University School of Computer Science
(Algorithms in Molecular Biology )
http://www-helix.stanford.edu/courses/bmi214/
MIT(Foundations of Computational and Systems Biology)
http://www.cs.tau.ac.il/~rshamir/algmb/algmb-archive.htm
Stanford(Representations and Algorithms for
Computational Molecular Biology )
http://www.cs.washington.edu/education/courses/527/09au/
http://ocw.mit.edu/courses/biology/7-91j-foundations-ofcomputational-and-systems-biology-spring-2004/
Imperial College (Introduction to Bioinformatics)
http://www.doc.ic.ac.uk/~sgc/teaching/341/
Presented By Liu Qi
Reference Books
An Introduction to Bioinformatics Algorithms
Neil C. Jones and Pavel A. Pevzner
Bioinformatics: The Machine Learning Approach
by Baldi, Pierre. Brunak, Søren.
Bioinformatics: Sequence and genome analysis
(cold spring harbor laboratory press) Mount,
David W.
Biological sequence analysis: Probabilistic
models of proteins and nucleic acids
(Cambridge university press) R. Durbin et al.
Presented By Liu Qi
Content
Pairwise Sequence Alignment
Multiple sequence alignment
Motif discovery
Protein secondary structure prediction
Microarrays, Clustering and Classification
Topics for Discussion
Presented By Liu Qi
Pairwise Sequence alignment
Dot matrix (intuitive)
Dynamic programming (exact)
Global Needleman-Wunsch
Local Smith-Waterman
Word or k-tuple (heuristic)
FASTA
BLAST
Presented By Liu Qi
Multiple sequence alignment
Dynamic Programming
Heuristic Alignment Methods
Progressive alignment
clustalw
Iterative refinement
Hidden Markov Model
Presented By Liu Qi
Motif discovery
Greedy Search
Expectation Maximization
Gibbs sampler
…
Presented By Liu Qi
Protein secondary structure
prediction
Chou-Fasman predictions
Garnier, Osguthorpe and Robson
Neural networks
Nearest neighbor methods
Consensus prediction approaches
Presented By Liu Qi
Microarrays, Clustering and
Classification
Normalization
Differential Expression Genes Detection
Clustering
– Hierarchical
– K-means
– SOM
Class Prediction
Integrating other Biological Knowledge
Presented By Liu Qi
Topics for Discussion
Proteomics data analysis
NGS Data Analysis
Integrative analysis of various omics data
…..
Presented By Liu Qi