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Báo cáo sinh học: "On the optimality of the neighbor-joining algorithm"

Tuyển tập các báo cáo nghiên cứu về sinh học được đăng trên tạp chí y học Molecular Biology cung cấp cho các bạn kiến thức về ngành sinh học đề tài: On the optimality of the neighbor-joining algorithm. | Algorithms for Molecular Biology BioMed Central Open Access On the optimality of the neighbor-joining algorithm Kord Eickmeyer1 Peter Huggins2 Lior Pachter 2 and Ruriko Yoshida3 Address Department of Computer Science Humboldt University Unter den Linden 6 10099 Berlin Germany 2Department of Mathematics University of California at Berkeley Berkeley CA 94720-3840 USA and 3Department of Statistics University of Kentucky Lexington KY 40506 USA Email Kord Eickmeyer-eickmeye@informatik.hu-berlin.de Peter Huggins - phuggins@math.berkeley.edu Lior Pachter - lpachter@math.berkeley.edu Ruriko Yoshida - ruriko.yoshida@uky.edu Corresponding author Published 30 April 2008 Received 13 November 2007 Algorithms for Molecular Biology 2008 3 5 doi 10.1186 1748-7188-3-5 Accepted 30 April 2008 This article is available from http www.almob.Org content 3 1 5 2008 Eickmeyer et al licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License http creativecommons.org licenses by 2.0 which permits unrestricted use distribution and reproduction in any medium provided the original work is properly cited. Abstract The popular neighbor-joining NJ algorithm used in phylogenetics is a greedy algorithm for finding the balanced minimum evolution BME tree associated to a dissimilarity map. From this point of view NJ is optimal when the algorithm outputs the tree which minimizes the balanced minimum evolution criterion. We use the fact that the NJ tree topology and the BME tree topology are 7 n determined by polyhedral subdivisions of the spaces of dissimilarity maps 27 to study the optimality of the neighbor-joining algorithm. In particular we investigate and compare the polyhedral subdivisions for n 8. This requires the measurement of volumes of spherical polytopes in high dimension which we obtain using a combination of Monte Carlo methods and polyhedral algorithms. Our results include a demonstration that highly unrelated trees