Lecture 10sco.h-its.org/exelixis/web/teaching/lectures/lecture10.pdf · 2013. 11. 15. · Lecture...
Transcript of Lecture 10sco.h-its.org/exelixis/web/teaching/lectures/lecture10.pdf · 2013. 11. 15. · Lecture...
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Introduction to Bioinformatics for Computer Scientists
Lecture 10
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Plan for next lectures
● Today (Alexis & Kassian):
● Phylogenetic Search Operators
● The phylogenetic Maximum Likelihood Model
● Lecture 11 (Alexis & Kassian)
● The Phylogenetic Maximum Likelihood Model
● Parallel Computing in Phylogenetics
● Lecture 12: (Andre) Discrete Operations on Trees
● Lecture 13: (Pavlos) Population genetics I
● Lecture 14: (Pavlos) Population genetics II
● Lecture 15: Wrap-up
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Phylogenetic Inference so far
● Distance-based methods
● Heuristics
– NJ
– UPGMA● Optimaility Criteria
– Least squares
– Minimum Evolution● Character-based methods
● The parsimony criterion
– Computing the parsimony score● Building starting trees
– Random trees
– Randomized stepwise addition order● Data structures for unrooted binary trees
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Outline for today
● Alexis
● Tree search operators● The Parsimonator algorithm● Limitations of parsimony
● Kassian
● Maximum Likelihood
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Search Strategies
● Given a comprehensive tree
● Apply topological alteration mechanisms in some order to improve the score e.g. via
● Hill-climbing● Simulated annealing● Some other technique
→ design of heuristics● The three basic moves are:
● NNI: Nearest Neighbor Interchange● SPR: Subtree Pruning and Re-Grafting● TBR: Tree Bisection and Reconnection
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NNI
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NNI
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NNI
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NNI
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SPR
T5
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T6
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T1
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SPR
T5
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+1
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SPR
T5
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SPR
T5
T2T6
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+1
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SPR
T5
T2T6
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+1
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SPR
T5
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SPR
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TBR
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TBR
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TBR
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TBR
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TBR
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TBR
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The Parsimonator Algorithm
● Build a randomized stepwise addition order parsimony tree
● Apply SPR moves to all subtrees of the current (comprehensive) tree with a rearrangement radius of 20
● If the rearrangement of a subtree yields an improved parsimony score, keep it immediately
→ this is somewhat greedy as opposed to a steepest ascent hill climbing algorithm
● Continue applying SPR moves with a radius of 20 to all subtrees until no tree with a better parsimony score can be found
● There are much more sophisticated algorithms available
→ TNT tool by Pablo Goloboff
● Keep in mind that parsimony returns discrete scores, that is, there may be many equally parsimonious trees among which we can not distinguish!
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Parsimony & Long Branch Attraction
● Because parsimony tries to minimize the number of mutations it faces some problems on trees with long branches
A C
B D
A
C
B
D
Correct tree
Wrong tree inferred by parsimony
Long branch attraction
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Parsimony & Long Branch Attraction● Settings under which parsimony recovers the wrong tree are also called “the Felsenstein
Zone” after Joe Felsenstein who has made numerous very important contributions to the field, e.g.
● The Maximum Likelihood model
● The Bootstrapping procedure
● If you are interested in statistics, there are some on-line courses by Joe at http://evolution.gs.washington.edu/courses.html
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Another Famous Computational Evolutionary Biologist
● Ziheng Yang → the guy who has written this nice book
● Many contributions to statistical modeling of evolutionary processes
● Many widely used tools
At LMU Munich during studentprotests