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source: UC Davis Academics 2015年1月9日
This course covers fundamental algorithms for efficient analysis of biological sequences and for building evolutionary trees. This is an undergraduate course taught by UC Davis computer science professor Dan Gusfield focusing on the ideas and concepts behind the most central algorithms in biological sequence analysis. Dynamic Programming, Alignment, Hidden Markov Models, Statistical Analysis are emphasized.
Lecture 1: Introduction to bioinformatics and the course 47:30
Lecture 2: Further introduction 48:43
Lecture 3: Defining sequence similarity 51:07
Lecture 4: Extending the model of sequence similarity 48:01
Lecture 5: Computing sequence similarity 47:44
Lecture 6: Computing similarity using an alignment graph 48:00
Lecture 7: From alignment graphs to formal dynamic programming 1:09:00
Lecture 8: Sequence alignment using dynamic programming - continued 49:33
Lecture 9: Local sequence alignment 46:18
Lecture 10: End-gap-free alignment and whole-genome shotgun sequencing 51:06
Lecture 11a: Expected Length of the Longest Common Subsequence 12:38
Lecture 11b: Expected Length of the Longest Common Substring 29:31
Lecture 12: Expected longest common substring II 16:02
Lecture 13: Probability of a complete query match in a database 40:27
Lecture 14: BLAST I 50:40
Lecture 15: BLAST II 46:28
Lecture 16: BLAST statistics 9:59
Lecture 17: Probability and database search 44:55
Lecture 18: Multiple sequence alignment I 50:39
Lecture 19: Multiple sequence alignment II 50:33
Lecture 20: Multiple sequence alignment III 49:52
Lecture 21: Uses of multiple sequence alignment 41:15
Lecture 22: From profiles to Markov models 48:30
Lecture 23: Hidden Markov models 49:25
Lecture 24: Hidden Markov models and the Vitterbi algorithm 49:55
Lecture 25: From the Vitterbi algorithm to the forward algorithm 45:46
Lecture 26: Hidden Markov models - The Backwards algorithm 28:22
Lecture 27: Introduction to evolutionary trees - Ultrametric trees 20:51
Lecture 28: Algorithms for Ultrametric trees - molecular clocks 37:49
Lecture 29; Additive trees and the Neighbor-Joining algorithm 47:18
Lecture 30: Maximum Parsimony and minimum mutation methods 39:04
Postscript: Where to go next 4:50
