# Disjoint Tree Mergers for Large-Scale Maximum Likelihood Tree Estimation
## 3 Results
### Experiment 2: Evaluating the Impact of the StartingTree on DTM Pipelines  
We evaluated the different DTM pipelines in comparison to the three ML methods, using datasets with 1000 to 2341 sequences; the trends described below are based on Tables 3-5, with specific examples or exceptions as indicated. * A comparison of the three ML heuristics shows that RAxML generally produces the most accurate trees, followed by IQ-TREE, and then by FastTree; the exception is the RNASim1000 condition where all three methods obtain about the same accuracy and FastTree is slightly more accurate than the other methods (Table 3). In the model conditions without fragmentary sequences, IQ-TREE and RAxML were very close in accuracy (Tables 3 and 4), but RAxML was clearly more accurate than IQ-TREE on the fragmentary model condition (Table 5). FastTree had particularly poor accuracy on fragmentary sequences, but it was also clearly less accurate than the other methods on the model condition with heterotachy (Table 4). RAxML is the slowest and FastTree is the fastest. * For each DTM method, using IQ-TREE instead of FastTree for the starting tree nearly always improved the accuracy. The one case where we did not see this trend was for the Cox1-HET, where, using Constrained-INC with the FastTree starting tree, was slightly better than using the IQ-TREE starting tree (Table 4). * The DTM methods produced trees with similar accuracy, with a small disadvantage to Constrained-INC when using the IQ-TREE starting tree. * When using an IQ-TREE starting tree, most of the time in using a DTM pipeline was spent constructing the starting tree. Furthermore, the DTM methods are very fast when using FastTree starting trees and slower when using IQ-TREE starting trees (and, by design, the DTM methods must be slower than their starting trees). Finally, TreeMerge was slower than the other DTM methods. (These observations are based on comparing running times for DTM pipelines to the starting tree runtimes.) * For every model condition, the TreeMerge and GTM pipelines produced more accurate trees than their starting trees, but the improvement was small for IQ-TREE starting trees (0.7% to 1.8%) and varied for FastTree starting trees (0.2% to 8.5%). * For every model condition, the TreeMerge and GTM pipelines using IQ-TREE starting trees matched or improved both FastTree and IQ-TREE with respect to accuracy, with the improvement in accuracy, depending on the model condition (and ranging from 0.7% to 1.8%). * The differences in accuracy using GTM or TreeMerge pipelines with FastTree or IQ-TREE starting trees were small (at most 0.3% in Tables 3 and 4), with the exception of the 1000M1-HF datasets, where the difference was large (14.1% in Table 5). Hence, the choice of starting tree can matter for some model conditions. * No DTM pipeline was consistently able to match the accuracy of RAxML. That is, for one model condition (RNASim1000), all of the DTM pipelines matched or improved on RAxML, but no DTM pipeline matched RAxML on the other two model conditions (Table 3). The difference in accuracy between RAxML and the GTM or TreeMerge pipelines using IQ-TREE starting trees was 0.5% for the Cox1-HET dataset and 3.5% for the 1000M1-HF datasets (Tables 4 and 5). * RAxML had a higher running time than these DTM pipelines, especially when the GTM pipeline is used. For example, the GTM pipeline completed in 1.2 h on the RNASim1000 datasets, 3.1 h on the Cox1-HET datasets, and 3.1 h on the 1000M1-HF datasets, whereas RAxML did not complete within 24 h on two of the model conditions and only completed in 7.1 h on the Cox1-HET datasets. In summary, the generally most accurate DTM pipelines are based on GTM or TreeMerge, and use IQ-TREE starting trees, decomposition size 500, and IQ-TREE constraint trees. These DTM pipelines provide an advantage over FastTree and IQ-TREE for accuracy, but they do not reliably provide an advantage over RAxML.