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Optimal phylogenetic reconstruction
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Source Annual ACM Symposium on Theory of Computing archive
Proceedings of the thirty-eighth annual ACM symposium on Theory of computing table of contents
Seattle, WA, USA
SESSION: Session 4A table of contents
Pages: 159 - 168  
Year of Publication: 2006
ISBN:1-59593-134-1
Authors
Constantinos Daskalakis  UC Berkeley, Berkeley, CA
Elchanan Mossel  UC Berkeley, Berkeley, CA
Sébastien Roch  UC Berkeley, Berkeley, CA
Sponsors
SIGACT: ACM Special Interest Group on Algorithms and Computation Theory
ACM: Association for Computing Machinery
Publisher
ACM  New York, NY, USA
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ABSTRACT

One of the major tasks of evolutionary biology is the reconstruction of phylogenetic trees from molecular data. The evolutionary model is given by a Markov chain on the true evolutionary tree. Given samples from this Markov chain at the leaves of the tree, the goal is to reconstruct the evolutionary tree.It is well known that in order to reconstruct a tree on n leaves, sequences of length Ω(log n) are needed. It was conjectured by M. Steel that for the CFN evolutionary model, if the mutation probability on all edges of the tree is less than p* = (√2-1)/23/2, then the tree can be recovered from sequences of length O(log n). This was proven by the second author in the special case where the tree is "balanced". The second author also proved that if all edges have mutation probability larger than p* then the length needed is nΩ(1). This "phase-transition" in the number of samples needed is closely related to the phase transition for the reconstruction problem (or extremality of free measure) studied extensively in statistical physics, probability and computer science.Here we complete the proof of Steel's conjecture and give a reconstruction algorithm using optimal (up to a multiplicative constant) sequence length. Our results further extend to obtain an optimal reconstruction algorithm for the Jukes-Cantor model with short edges. All reconstruction algorithms run in polynomial time.


REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

 
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P. M. Bleher, J. Ruiz, and V. A. Zagrebnov. On the purity of the limiting Gibbs state for the Ising model on the Bethe lattice. J. Statist. Phys., 79(1-2):473--482, 1995.
 
3
J. A. Cavender. Taxonomy with confidence. Math. Biosci., 40(3-4), 1978.
 
4
J. Chang. Full reconstruction of markov models on evolutionary trees: identifiability and consistency. Math. Biosci., 137(51--73), 1996.
 
5
 
6
W. S. Evans, C. Kenyon, Y. Y. Peres, and L. J. Schulman. Broadcasting on trees and the Ising model. Ann. Appl. Probab., 10(2):410--433, 2000.
7
 
8
J. S. Farris. A probability model for inferring evolutionary trees. Syst. Zool., 22(4):250--256, 1973.
 
9
J. Felsenstein. Inferring Phylogenies. Sinauer, New York, New York, 2004.
 
10
H. O. Georgii. Gibbs measures and phase transitions, volume 9 of de Gruyter Studies in Mathematics. Walter de Gruyter & Co., Berlin, 1988.
 
11
Y. Higuchi. Remarks on the limiting Gibbs states on a (d+1)-tree. Publ. Res. Inst. Math. Sci., 13(2):335--348, 1977.
 
12
D. Ioffe. On the extremality of the disordered state for the Ising model on the Bethe lattice. Lett. Math. Phys., 37(2):137--143, 1996.
 
13
S. Janson and E. Mossel. Robust reconstruction on trees is determined by the second eigenvalue. Ann. Probab., 32:2630--2649, 2004.
 
14
T. H. Jukes and C. Cantor. Mammalian protein metabolism. In H. N. Munro, editor, Evolution of protein molecules, pages 21--132. Academic Press, 1969.
 
15
F. Martinelli, A. A. Sinclair, and D. Weitz. Glauber dynamics on trees: boundary conditions and mixing time. Comm. Math. Phys., 250(2):301--334, 2004.
 
16
 
17
 
18
E. Mossel. Reconstruction on trees: beating the second eigenvalue. Ann. Appl. Probab., 11(1):285--300, 2001.
 
19
E. Mossel. Distorted metrics on trees and phylogenetic forests. IEEE Comp. Biol. and Bioinformatics, 2004.
 
20
E. Mossel. Phase transitions in phylogeny. Trans. Amer. Math. Soc., 356(6):2379--2404, 2004.
 
21
E. Mossel and Y. Peres. Information flow on trees. Ann. Appl. Probab., 13(3):817--844, 2003.
 
22
E. Mossel and M. Steel. A phase transition for a random cluster model on phylogenetic trees. Math. Biosci., 187(2):189--203, 2004.
 
23
J. Neyman. Molecular studies of evolution: a source of novel statistical problems. In S. S. Gupta and J. Yackel, editors, Statistical desicion theory and related topics, pages 1--27. 1971.
 
24
C. Semple and M. Steel. Phylogenetics, volume 22 of Mathematics and its Applications series. Oxford University Press, 2003.
 
25
F. Spitzer. Markov random fields on an infinite tree. Ann. Probability, 3(3):387--398, 1975.
 
26
M. Steel. My Favourite Conjecture. http://www.math.canterbury.ac.nz/~mathmas/conjecture.pdf, 2001.


Collaborative Colleagues:
Constantinos Daskalakis: colleagues
Elchanan Mossel: colleagues
Sébastien Roch: colleagues