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5.1 Parsimony
Mutations are exceedingly rare events. The most unlikely events a model
invokes, the less likely the model is to be correct.
The fewest number of mutations to explain a state is the most likely to be correct.
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5.1.1 Informative and Uninformative Sites informative sites
have information to construct a tree uninformative sites
have no information
in the sense of parsimony principle.
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A position to be informative must have at least two different nucleotides each of these nucleotides to present at
least twice.
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informative sites synapomorphy: support the internal branches
(true) homoplasy: acquired as a result of parallel evol
ution of convergence (false) 眼睛: humans, flies, mollusks ( 軟體動物 )
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5.1.2 Unweighted Parsimony
Every possible tree is considered individually for each informative site.
The tree with the minimum overall costs are reported.
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There are several problems: The number of alternative unrooted trees incre
ases dramatically. Calculating the number of substitutions invoke
d by each alternative tree is difficult.
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The second problem can be solved by intersection: if the intersection of the two
sets of its children is not empty union: if it is empty.
The number of unions is the minimum number of substitutions.
For uninformative site, it is the number of different nucleotides minus one.
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5.1.4 Weighted Parsimony
Not all mutations are equivalent Some sequences (e.g., non-coding seq.) are mo
re prone to indel than others. Functional importance differs from gene to gen
e. Subtle substitution biases usually vary between
genes and between species. Weights (scoring matrices) can be added t
o reflect these differences.
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5.2 Inferred Ancestral Sequences Can be derived while constructing the tree.
No missing link! 如何取樣本 ? It may be bias.
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5.3 Strategies for Faster Searches The number of different phylogenetic tree
grows enormously. 10 sequences 2M for exhaustive search
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參考資料及圖片出處
1. Fundamental Concepts of BioinformaticsDan E. Krane and Michael L. Raymer, Benjamin/Cummings, 2003.
2. Biological Sequence Analysis – Probabilistic models of proteins and nucleic acidsR. Durbin, S. Eddy, A. Krogh, G. Mitchison, Cambridge University Press, 1998.
3. Biology, by Sylvia S. Mader, 8th edition, McGraw-Hill, 2003.