1. Presented by Liu Qi An introduction to Bioinformatics Algorithms Qi Liu email: qi.liu@vanderbilt.edu

2. Presented By Liu Qi Description of the Course introduce the basic computational issues and methods used in molecular biology 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. 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.

3. Presented By Liu Qi Related Courses University of Washington （ Computational Biology) University of Washington （ Computational Biology) http://www.cs.washington.edu/education/courses/527/09au/ http://www.cs.washington.edu/education/courses/527/09au/ Tel Aviv University School of Computer Science (Algorithms in Molecular Biology ) Tel Aviv University School of Computer Science (Algorithms in Molecular Biology ) http://www.cs.tau.ac.il/~rshamir/algmb/algmb-archive.htm http://www.cs.tau.ac.il/~rshamir/algmb/algmb-archive.htm http://www.cs.tau.ac.il/~rshamir/algmb/algmb-archive.htm Stanford(Representations and Algorithms for Computational Molecular Biology ) Stanford(Representations and Algorithms for Computational Molecular Biology ) http://www-helix.stanford.edu/courses/bmi214/ http://www-helix.stanford.edu/courses/bmi214/ http://www-helix.stanford.edu/courses/bmi214/ MIT(Foundations of Computational and Systems Biology) MIT(Foundations of Computational and Systems Biology) http://ocw.mit.edu/courses/biology/7-91j-foundations-of- computational-and-systems-biology-spring-2004/ http://ocw.mit.edu/courses/biology/7-91j-foundations-of- computational-and-systems-biology-spring-2004/ Imperial College (Introduction to Bioinformatics) Imperial College (Introduction to Bioinformatics) http://www.doc.ic.ac.uk/~sgc/teaching/341/ http://www.doc.ic.ac.uk/~sgc/teaching/341/ http://www.doc.ic.ac.uk/~sgc/teaching/341/

4. Presented By Liu Qi Reference Books An Introduction to Bioinformatics Algorithms Neil C. Jones and Pavel A. Pevzner An Introduction to Bioinformatics Algorithms Neil C. Jones and Pavel A. Pevzner Bioinformatics: The Machine Learning Approach by Baldi, Pierre. Brunak, Søren. Bioinformatics: The Machine Learning Approach by Baldi, Pierre. Brunak, Søren.. Bioinformatics: Sequence and genome analysis (cold spring harbor laboratory press) Mount, David W. 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. Biological sequence analysis: Probabilistic models of proteins and nucleic acids (Cambridge university press) R. Durbin et al.

5. Presented By Liu Qi Content Pairwise Sequence Alignment Pairwise Sequence Alignment Multiple sequence alignment Multiple sequence alignment Motif discovery Motif discovery Protein secondary structure prediction Protein secondary structure prediction Microarrays, Clustering and Classification Microarrays, Clustering and Classification Topics for Discussion Topics for Discussion

6. Presented By Liu Qi Pairwise Sequence alignment Dot matrix (intuitive) Dot matrix (intuitive) Dynamic programming (exact) Dynamic programming (exact) Global Global Needleman-Wunsch Local Local Smith-Waterman Word or k-tuple (heuristic) Word or k-tuple (heuristic) FASTA FASTA BLAST BLAST

7. Presented By Liu Qi Multiple sequence alignment Dynamic Programming Dynamic Programming Heuristic Alignment Methods Heuristic Alignment Methods Progressive alignment Progressive alignment clustalw clustalw Iterative refinement Iterative refinement Hidden Markov Model Hidden Markov Model

8. Presented By Liu Qi Motif discovery Greedy Search Greedy Search Expectation Maximization Expectation Maximization Gibbs sampler Gibbs sampler …

9. Presented By Liu Qi Protein secondary structure prediction Chou-Fasman predictions Garnier, Osguthorpe and Robson Neural networks Nearest neighbor methods Consensus prediction approaches

10. Presented By Liu Qi Microarrays, Clustering and Classification Microarrays, Clustering and Classification Normalization Differential Expression Genes Detection Clustering – Hierarchical – K-means – SOM Class Prediction Integrating other Biological Knowledge

11. Topics for Discussion Proteomics data analysis Proteomics data analysis NGS Data Analysis NGS Data Analysis Integrative analysis of various omics data Integrative analysis of various omics data ….. ….. Presented By Liu Qi