Efficiency of the neighbor-joining method in reconstructing deep and shallow evolutionary relationships in large phylogenies

Sudhir Kumar, Sudhindra R. Gadagkar

Research output: Contribution to journalArticlepeer-review

74 Scopus citations

Abstract

The neighbor-joining (NJ) method is widely used in reconstructing large phylogenies because of its computational speed and the high accuracy in phylogenetic inference as revealed in computer simulation studies. However, most computer simulation studies have quantified the overall performance of the NJ method in terms of the percentage of branches inferred correctly or the percentage of replications in which the correct tree is recovered. We have examined other aspects of its performance, such as the relative efficiency in correctly reconstructing shallow (close to the external branches of the tree) and deep branches in large phylogenies; the contribution of zero-length branches to topological errors in the inferred trees; and the influence of increasing the tree size (number of sequences), evolutionary rate, and sequence length on the efficiency of the NJ method. Results show that the correct reconstruction of deep branches is no more difficult than that of shallower branches. The presence of zero-length branches in realized trees contributes significantly to the overall error observed in the NJ tree, especially in large phylogenies or slowly evolving genes. Furthermore, the tree size does not influence the efficiency of NJ in reconstructing shallow and deep branches in our simulation study, in which the evolutionary process is assumed to be homogeneous in all lineages.

Original languageEnglish (US)
Pages (from-to)544-553
Number of pages10
JournalJournal of Molecular Evolution
Volume51
Issue number6
DOIs
StatePublished - Jan 1 2000

Keywords

  • Accuracy
  • Deep versus shallow branches
  • Large phylogenies
  • Neighbor-joining method
  • Phylogenetic inference
  • Zero-length branches

ASJC Scopus subject areas

  • Ecology, Evolution, Behavior and Systematics
  • Molecular Biology
  • Genetics

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