Recursion and dynamic programming Applied dynamic programming: global alignments: Needleman-Wunsch Applied dynamic programming: local alignments Smith -Waterman Substitution matrices PAM. blosUM, Gonnet Gaps- linear and affine Alignment statistics

Outline Distance Matrix Methods Neighbor-Joining Method and Related Neighbor Methods Maximum Likelihood Parsimony Branch and bound Heuristic Seaching Consensus Trees Software(PHYLIP, PAUP)

What is a genome? 123456789101112 13141516171819202122XY A genome is NoT a bag of proteins

Review of markov models DNA Evolution CpG Island HMM The viterbi algorithm Real World HMMs Markov models for dna evolution Ch. 4 of Mount

Outline Brief review of protein structure chou -Fasman- predictions Garnier, Osguthorpe and Robson Helical wheels and hydrophobic moments Neural networks Nearest neighbor methods Consensus prediction approaches

Review -Homology Modeling Identify a protein with similar sequence for which a structure has been solved (the template) Align the target sequence with the template Use the alignment to build an approximate structure for the target

1. Grow crystals-structure determination by X-ray crystallography relies on the repeating structure of a crystalline lattice 2. Collect a diffraction pattern-periodically spaced atoms in the crystal give specific\spots\where x-rays interfere constructively

Algorithms for detecting structure similarity Dynamic Programming works on 1D strings- reduce problem to this cant accommodate topological changes example: Secondary Structure Alignment Program(SSAP) 3D Comparison/Clustering

Why the hype? Microarray platforms CDNA VS oligo technologies Sample applications Analysis of microarray data clustering of co-expressed genes some classic microarray papers

Ab initio =from the beginning in strictest sense uses first principles, not information about other protein structures In practice, all methods rely on empirical observations