Result: Optimal phylogenetic reconstruction

Title:
Optimal phylogenetic reconstruction
Authors:
DASKALAKIS, Constantinos, MOSSEL, Elchanan, ROCH, Sébastien
Source:
STOC'06 (Proceedings of the 38th annual ACM symposium on theory of computing). :159-168
Publisher Information:
New York NY: ACM Press, 2006.
Publication Year:
2006
Physical Description:
print, 26 ref 1
Original Material:
INIST-CNRS
Subject Terms:
Computer science, Informatique, Sciences exactes et technologie, Exact sciences and technology, Sciences appliquees, Applied sciences, Informatique; automatique theorique; systemes, Computer science; control theory; systems, Informatique théorique, Theoretical computing, Acier, Steel, Acero, Algorithme optimal, Optimal algorithm, Algoritmo óptimo, Algorithme évolutionniste, Evolutionary algorithm, Algoritmo evoluciónista, Analyse algorithme, Algorithm analysis, Análisis algoritmo, Application médicale, Medical application, Aplicación medical, Approche probabiliste, Probabilistic approach, Enfoque probabilista, Arbre phylogénétique, Phylogenetic tree, Arbol filogenético, Biologie, Biology, Biología, Chaîne Markov, Markov chain, Cadena Markov, Complexité algorithme, Algorithm complexity, Complejidad algoritmo, Modélisation, Modeling, Modelización, Méthode polynomiale, Polynomial method, Método polinomial, Phylogenèse, Phylogeny, Filogénesis, Physique statistique, Statistical physics, Física estadística, Reconstruction phase, Phase reconstruction, Reconstrucción fase, Science médicale, Medical science, Ciencia Medica, Temps polynomial, Polynomial time, Tiempo polinomial, Transition phase, Phase transitions, Transición fase
Document Type:
Conference Conference Paper
File Description:
text
Language:
English
Author Affiliations:
EECS Department UC Berkeley, Berkeley, CA 94720, United States
Department of Statistics UC Berkeley, Berkeley, CA 94720, United States
Access URL:
http://pascal-francis.inist.fr/vibad/index.php?action=search&terms=18297907
Rights:
Copyright 2006 INIST-CNRS
CC BY 4.0
Sauf mention contraire ci-dessus, le contenu de cette notice bibliographique peut être utilisé dans le cadre d’une licence CC BY 4.0 Inist-CNRS / Unless otherwise stated above, the content of this bibliographic record may be used under a CC BY 4.0 licence by Inist-CNRS / A menos que se haya señalado antes, el contenido de este registro bibliográfico puede ser utilizado al amparo de una licencia CC BY 4.0 Inist-CNRS
Notes:
Computer science; theoretical automation; systems
Accession Number:
edscal.18297907
Database:
PASCAL Archive

Further Information

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. Categories and Subject Descriptors: F.2 [Theory of Computation]: Analysis of Algorithms and Problem Complexity; J.3 [Computer Applications]: Life and Medical Sciences-Biology and genetics.