### Abstract

A stepwise algorithm for reconstructing minimum evolution (ME) trees from evolutionary distance data is proposed. In each step, a taxon that potentially has a neighbor (another taxon connected to it with a single interior node) is first chosen and then its true neighbor searched iteratively. For m taxa, at most (m - 1)1/2 trees are examined and the tree with the minimum sum of branch lengths (S) is chosen as the final tree. This algorithm provides simple strategies for restricting the tree space searched and allows us to implement efficient ways of dynamically computing the ordinary least squares estimates of S for the topologies examined. Using computer simulation, we found that the efficiency of the ME method in recovering the correct tree is similar to that of the neighbor-joining method (Saitou and Nei 1987). A more exhaustive search is unlikely to improve the efficiency of the ME method in finding the correct tree because the correct tree is almost always included in the tree space searched with this stepwise algorithm. The new algorithm finds trees for which S values may not be significantly different from that of the ME tree if the correct tree contains very small interior branches or if the pairwise distance estimates have large sampling errors. These topologies form a set of plausible alternatives to the ME tree and can be compared with each other using statistical tests based on the minimum evolution principle. The new algorithm makes it possible to use the ME method for large data sets.

Original language | English (US) |
---|---|

Pages (from-to) | 584-593 |

Number of pages | 10 |

Journal | Molecular Biology and Evolution |

Volume | 13 |

Issue number | 4 |

State | Published - Apr 1996 |

Externally published | Yes |

### Fingerprint

### Keywords

- distance method
- minimum evolution trees
- neighbor-joining
- phylogeny

### ASJC Scopus subject areas

- Genetics
- Biochemistry
- Genetics(clinical)
- Biochemistry, Genetics and Molecular Biology(all)
- Ecology, Evolution, Behavior and Systematics
- Agricultural and Biological Sciences (miscellaneous)
- Molecular Biology

### Cite this

*Molecular Biology and Evolution*,

*13*(4), 584-593.

**A stepwise algorithm for finding minimum evolution trees.** / Kumar, Sudhir.

Research output: Contribution to journal › Article

*Molecular Biology and Evolution*, vol. 13, no. 4, pp. 584-593.

}

TY - JOUR

T1 - A stepwise algorithm for finding minimum evolution trees

AU - Kumar, Sudhir

PY - 1996/4

Y1 - 1996/4

N2 - A stepwise algorithm for reconstructing minimum evolution (ME) trees from evolutionary distance data is proposed. In each step, a taxon that potentially has a neighbor (another taxon connected to it with a single interior node) is first chosen and then its true neighbor searched iteratively. For m taxa, at most (m - 1)1/2 trees are examined and the tree with the minimum sum of branch lengths (S) is chosen as the final tree. This algorithm provides simple strategies for restricting the tree space searched and allows us to implement efficient ways of dynamically computing the ordinary least squares estimates of S for the topologies examined. Using computer simulation, we found that the efficiency of the ME method in recovering the correct tree is similar to that of the neighbor-joining method (Saitou and Nei 1987). A more exhaustive search is unlikely to improve the efficiency of the ME method in finding the correct tree because the correct tree is almost always included in the tree space searched with this stepwise algorithm. The new algorithm finds trees for which S values may not be significantly different from that of the ME tree if the correct tree contains very small interior branches or if the pairwise distance estimates have large sampling errors. These topologies form a set of plausible alternatives to the ME tree and can be compared with each other using statistical tests based on the minimum evolution principle. The new algorithm makes it possible to use the ME method for large data sets.

AB - A stepwise algorithm for reconstructing minimum evolution (ME) trees from evolutionary distance data is proposed. In each step, a taxon that potentially has a neighbor (another taxon connected to it with a single interior node) is first chosen and then its true neighbor searched iteratively. For m taxa, at most (m - 1)1/2 trees are examined and the tree with the minimum sum of branch lengths (S) is chosen as the final tree. This algorithm provides simple strategies for restricting the tree space searched and allows us to implement efficient ways of dynamically computing the ordinary least squares estimates of S for the topologies examined. Using computer simulation, we found that the efficiency of the ME method in recovering the correct tree is similar to that of the neighbor-joining method (Saitou and Nei 1987). A more exhaustive search is unlikely to improve the efficiency of the ME method in finding the correct tree because the correct tree is almost always included in the tree space searched with this stepwise algorithm. The new algorithm finds trees for which S values may not be significantly different from that of the ME tree if the correct tree contains very small interior branches or if the pairwise distance estimates have large sampling errors. These topologies form a set of plausible alternatives to the ME tree and can be compared with each other using statistical tests based on the minimum evolution principle. The new algorithm makes it possible to use the ME method for large data sets.

KW - distance method

KW - minimum evolution trees

KW - neighbor-joining

KW - phylogeny

UR - http://www.scopus.com/inward/record.url?scp=0029917260&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=0029917260&partnerID=8YFLogxK

M3 - Article

VL - 13

SP - 584

EP - 593

JO - Molecular Biology and Evolution

JF - Molecular Biology and Evolution

SN - 0737-4038

IS - 4

ER -