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, 26 (7), 1641-50

FastTree: Computing Large Minimum Evolution Trees With Profiles Instead of a Distance Matrix


FastTree: Computing Large Minimum Evolution Trees With Profiles Instead of a Distance Matrix

Morgan N Price et al. Mol Biol Evol.


Gene families are growing rapidly, but standard methods for inferring phylogenies do not scale to alignments with over 10,000 sequences. We present FastTree, a method for constructing large phylogenies and for estimating their reliability. Instead of storing a distance matrix, FastTree stores sequence profiles of internal nodes in the tree. FastTree uses these profiles to implement Neighbor-Joining and uses heuristics to quickly identify candidate joins. FastTree then uses nearest neighbor interchanges to reduce the length of the tree. For an alignment with N sequences, L sites, and a different characters, a distance matrix requires O(N(2)) space and O(N(2)L) time, but FastTree requires just O(NLa + N ) memory and O(N log (N)La) time. To estimate the tree's reliability, FastTree uses local bootstrapping, which gives another 100-fold speedup over a distance matrix. For example, FastTree computed a tree and support values for 158,022 distinct 16S ribosomal RNAs in 17 h and 2.4 GB of memory. Just computing pairwise Jukes-Cantor distances and storing them, without inferring a tree or bootstrapping, would require 17 h and 50 GB of memory. In simulations, FastTree was slightly more accurate than Neighbor-Joining, BIONJ, or FastME; on genuine alignments, FastTree's topologies had higher likelihoods. FastTree is available at


F<sc>IG</sc>. 1.—
FIG. 1.—
Overview of FastTree.
F<sc>IG</sc>. 2.—
FIG. 2.—
Distribution of support values for simulated alignments of 250 protein sequences with gaps. We compare the distribution of FastTree's local bootstrap and the traditional (global) bootstrap for correctly and incorrectly inferred splits. The right-most bin contains the strongly supported splits (0.95–1.0)

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