# Disjoint Tree Mergers for Large-Scale Maximum Likelihood Tree Estimation
## 5 Conclusions  
This study provides new insight into maximum likelihood (ML) tree estimation on large gene sequence datasets. We compared three leading ML heuristics--RAxML-NG, IQ-TREE 2, and FastTree 2--on five different model conditions with 1000 or more sequences with respect to the topological accuracy and running time. We found that RAxML-NG and IQ-TREE 2 were more robust to challenging conditions that were characterized by sequence length heterogeneity and substantial heterotachy than FastTree 2. We also observed that RAxML-NG was the most computationally intensive of the three ML methods and FastTree 2 was the fastest. The comparison between RAxML-NG and IQ-TREE 2 showed that they had similar accuracy under some conditions, but RAxML-NG was more accurate on the datasets with fragmentary conditions (thus displaying greater robustness to this challenging condition than IQ-TREE 2), but less accurate on the RNASim10K datasets. Thus, the choice between RAxML-NG and IQ-TREE 2 may depend on the dataset size (with IQ-TREE 2 being preferred for the largest datasets), as well as the available computational resources. Moreover, our study suggests that gene tree maximum likelihood estimation using RAxML-NG is not likely to be feasible for the very large datasets. Our study then explored the design space for divide-and-conquer pipelines using three DTM methods. In contrast to an earlier study [24], which found that a divide-and-conquer pipeline using Constrained-INC did not provide an advantage over its starting tree for gene tree estimation, our new pipeline reliably produces trees more accurately than both IQ-TREE 2 and FastTree 2 starting trees. However, we also saw that, to consistently match or improve on of RAxML-NG while being substantially faster, will require changes to the current divide-and-conquer strategy and most likely will require a new approach to decomposing the input set of sequences into disjoint sets that is fast and suitable for subsequent use in a DTM pipeline. Although this study was only based on a few model conditions and much work is still needed to understand the relative performance of different methods and the conditions under which each method is generally reliable, this study suggests the following guidelines for the selection of ML heuristic:* When the dataset sizes and computational resources make it feasible to use RAxML-NG or IQ-TREE 2, these methods should be used rather than FastTree 2, due to their (generally) higher level of topological accuracy when analyzing datasets that evolve under non-_i.i.d._ models (which may be typical of biological datasets). The choice between IQ-TREE 2 and RAxML-NG is less clear, as the relative accuracy of these two methods depended on the model condition. However, when datasets have substantial numbers of fragmentary sequences, RAxML-NG may be preferable to IQ-TREE 2. * For very large datasets where RAxML-NG and IQ-TREE 2 are not feasible to run, then FastTree 2 or one of the DTM pipelines that we presented could provide reasonable accuracy. In summary, this study is very positive with respect to the potential for DTM pipelines to improve the scalability of gene tree estimation. First, the current best DTM pipeline improves on IQ-TREE 2 and FastTree 2, which had not been seen before. Second, we have identified an algorithmic challenge--the fast and accurate clustering of sequence datasets--that may provide the key to improving the DTM pipeline accuracy and scalability to large and more challenging datasets. Advances on this basic challenge could lead to substantial breakthroughs for large-scale phylogeny estimation. This study also showed that the phylogenetic estimation error rates can decrease with increased taxonomic sampling (a trend that has been observed in other studies [13]), provided that good tree estimation methods are used, showing the value of developing methods that can scale well to large datasets. Overall, therefore, this study suggests new designs for large-scale phylogeny estimation that have both high accuracy and speed.