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DNA | Av. LogLk rank | Delta>5 | P-value<0.05 | Av. RF distance |

PhyML 3.0 NNI | 3.21 | 33 | 6 | 0.2711 |

PhyML 3.0 SPR | 1.36 | 1 | 0 | 0.0692 |

RAxML CAT | 2.19 | 7 | 0 | 0.2058 |

FastTree reoptimized | 3.24 | 35 | 5 | 0.2585 |

PROTEIN | Av. LogLk rank | Delta>5 | P-value<0.05 | Av. RF distance |

PhyML 3.0 NNI | 2.82 | 19 | 1 | 0.2144 |

PhyML 3.0 SPR | 1.8 | 3 | 0 | 0.0641 |

RAxML CAT | 2.06 | 2 | 1 | 0.108 |

FastTree reoptimized | 3.31 | 26 | 2 | 0.2762 |

- PhyML NNI

PhyML 3.0, optimizing the topology with both simultaneous NNIs (as in original PhyML algorithm) and refined NNIs with 5-edge-length optimization, and using a BioNJ starting tree, configured with the GTR model for DNA sequences, with WAG for proteins, and with 4 discrete gamma rate categories (alpha estimated from the data). - PhyML SPR

PhyML 3.0, optimizing the topology with SPR (and NNI) operations, and using a BioNJ starting tree, configured with the GTR model for DNA sequences, with WAG for proteins, and with 4 discrete gamma rate categories (alpha estimated from the data). - RaxML CAT

RAxML, using the GTRMIX model for DNA sequences, and PROTMIXWAG model for protein sequences. This option makes RAxML perform a topology search under CAT, and then evaluate the final tree under full Γ4 (shape parameter estimated from the data) such that it yields stable likelihood values and branch lengths. - FastTree reoptimized

FastTree, using the GTR model for DNA sequences, and WAG model for protein sequences. FastTree outputs a tree with approximate branch lengths and parameter estimates, and no usable tree likelihood value. Thus, we used PhyML 3.0 to optimize these numerical parameters and obtain the likelihood of the FastTree tree topology. The computing time required by PhyML to achieve this task was added to that of FastTree. Otherwise comparisons would be unfair between programs inferring a tree topology only, and those inferring both the topology and reliable numerical parameter values. Moreover, we believe that in most analyzes users are interested in the value of these parameters (e.g. transition/transversion ratio, alpha, etc).

- Computing time ranks

The six methods are ranked for each of the alignments, based on the computing time. First rank contains methods with computing time ranging from the best (B) computing time to 1.25 X B (i.e. nearly best computing time). Remaining methods are ranked in the same way, until all methods are ranked. Ties are accounted for; e.g. if the first and second group contains 2 methods each, the ranks will be 1.5 ( (1+2)/2 ) and 3.5 ( (3+4)/2 ). To summarize these results, we provide the median and average ranks for all DNA and protein alignments. - Topology ranks

The six methods are ranked for each of the alignments using a similar principle, based on the tree likelihood. First rank contains all methods which find the same best topology. And so on. Moreover, we provide the median and average ranks for all DNA and protein alignments. - Robinson and Foulds distances

RF is the Robinson and Foulds (bipartition) distance between the best topology and the given topology. - Delta>5

Another variable of interest is the number of times a method fails to find a phylogeny which log-likelihood is close to the highest log-likelihood found by any of the methods being compared. We thus counted the number of data sets for which the log-likelihoods returned by a given method was smaller than the highest log-likelihood found on the corresponding alignments minus 5.0. While this boundary of 5.0 points of log-likelihood is arbitrary, we believe that it provides a simple and practical way to tell the methods apart at first sight. - SH tests

We used the Shimoidara-Hasegawa (SH) test to assess the statistical significance of the likelihood differences. Every result displays the P-value between its logLk and the logLk of the best result for the same data. As a summary, we provide the number of times each method is significatively worst than the best one.