accessibility: null
additionDate: '2017-01-17T15:07:50Z'
biotoolsCURIE: biotools:fasttree
biotoolsID: fasttree
collectionID:
- EBI Training Tools
confidence_flag: null
cost: null
credit:
- email: fasttree@microbesonline.org
  fundrefid: null
  gridid: null
  name: null
  note: null
  orcidid: null
  rorid: null
  typeEntity: Person
  typeRole:
  - Primary contact
  url: null
description: Infers approximately-maximum-likelihood phylogenetic trees from alignments
  of nucleotide or protein sequences.
documentation:
- note: null
  type:
  - General
  url: https://www.microbesonline.org/fasttree/#How
download: []
editPermission:
  authors:
  - ardeleanadriandvm
  - ELIXIR-CZ
  type: group
elixirCommunity: []
elixirNode: []
elixirPlatform: []
elixir_badge: 0
function:
- cmd: null
  input: []
  note: null
  operation:
  - term: Phylogenetic tree generation (from molecular sequences)
    uri: http://edamontology.org/operation_0540
  - term: Phylogenetic tree generation (maximum likelihood and Bayesian methods)
    uri: http://edamontology.org/operation_0547
  output: []
homepage: https://www.microbesonline.org/fasttree/
homepage_status: 0
language:
- C
lastUpdate: '2024-11-24T14:34:57.624784Z'
license: null
link: []
maturity: null
name: FastTree
operatingSystem:
- Linux
- Windows
otherID: []
owner: ebi_training_import
publication:
- doi: 10.1093/molbev/msp077
  metadata:
    abstract: 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 2L) time, but FastTree requires just O(NLa
      + N) memory and O(Nlog (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 http://microbesonline.org/fasttree.
      2009 The Authors2009This is an Open Access article distributed under the terms
      of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/2.0/uk/)
      which permits unrestricted non-commercial use, distribution, and reproduction
      in any medium, provided the original work is properly cited. © 2009 The Authors.
    authors:
    - name: Price M.N.
    - name: Dehal P.S.
    - name: Arkin A.P.
    citationCount: 3611
    date: '2009-07-01T00:00:00Z'
    journal: Molecular Biology and Evolution
    title: 'Fasttree: Computing large minimum evolution trees with profiles instead
      of a distance matrix'
  note: null
  pmcid: PMC2693737
  pmid: '19377059'
  type: []
  version: null
- doi: 10.1371/journal.pone.0009490
  metadata:
    abstract: 'Background: We recently described FastTree, a tool for inferring phylogenies
      for alignments with up to hundreds of thousands of sequences. Here, we describe
      improvements to FastTree that improve its accuracy without sacrificing scalability.
      Methodology/Principal Findings: Where FastTree 1 used nearest-neighbor interchanges
      (NNIs) and the minimum-evolution criterion to improve the tree, FastTree 2 adds
      minimum-evolution subtree-pruning-regrafting (SPRs) and maximum-likelihood NNIs.
      FastTree 2 uses heuristics to restrict the search for better trees and estimates
      a rate of evolution for each site (the "CAT" approximation). Nevertheless, for
      both simulated and genuine alignments, FastTree 2 is slightly more accurate
      than a standard implementation of maximum-likelihood NNIs (PhyML 3 with default
      settings). Although FastTree 2 is not quite as accurate as methods that use
      maximum-likelihood SPRs, most of the splits that disagree are poorly supported,
      and for large alignments, FastTree 2 is 100-1,000 times faster. FastTree 2 inferred
      a topology and likelihood-based local support values for 237,882 distinct 16S
      ribosomal RNAs on a desktop computer in 22 hours and 5.8 gigabytes of memory.
      Conclusions/Significance: FastTree 2 allows the inference of maximum-likelihood
      phylogenies for huge alignments. FastTree 2 is freely available at http://www.microbesonline.org/fasttree.
      © 2010 Price et al.'
    authors:
    - name: Price M.N.
    - name: Dehal P.S.
    - name: Arkin A.P.
    citationCount: 9314
    date: '2010-03-10T00:00:00Z'
    journal: PLoS ONE
    title: FastTree 2 - Approximately maximum-likelihood trees for large alignments
  note: null
  pmcid: PMC2835736
  pmid: '20224823'
  type: []
  version: null
relation: []
toolType:
- Command-line tool
topic:
- term: Phylogenetics
  uri: http://edamontology.org/topic_3293
- term: Sequence analysis
  uri: http://edamontology.org/topic_0080
validated: 1
version: []
