Fast Tree: Computing Large Minimum-Evolution Trees with Profiles instead of a Distance Matrix

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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 ... continued below

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N. Price, Morgan; S. Dehal, Paramvir & P. Arkin, Adam July 31, 2009.

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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 sqrt(N) ) memory and O( N sqrt(N) log(N) L a ) 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 hours and 2.4 gigabytes of memory. Just computing pairwise Jukes-Cantor distances and storing them, without inferring a tree or bootstrapping, would require 17 hours and 50 gigabytes 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 http://microbesonline.org/fasttree.

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  • Journal Name: Molecular Biology and Evolution

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  • Report No.: LBNL-1861E
  • Grant Number: DE-AC02-05CH11231
  • Office of Scientific & Technical Information Report Number: 957328
  • Archival Resource Key: ark:/67531/metadc925702

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  • July 31, 2009

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  • Nov. 13, 2016, 7:26 p.m.

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  • Nov. 18, 2016, 2:37 p.m.

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N. Price, Morgan; S. Dehal, Paramvir & P. Arkin, Adam. Fast Tree: Computing Large Minimum-Evolution Trees with Profiles instead of a Distance Matrix, article, July 31, 2009; Berkeley, California. (digital.library.unt.edu/ark:/67531/metadc925702/: accessed November 14, 2018), University of North Texas Libraries, Digital Library, digital.library.unt.edu; crediting UNT Libraries Government Documents Department.