# Improved Duplication-Transfer-Loss Reconciliation with
## 5 Discussion and Conclusions  
In this work, we introduced an extension of the classical DTL reconciliation model, called the DTLx reconciliation model, that accounts for extinct or unsampled species lineages and allows for \(\mathbb{TL}\) and \(\mathbb{TX}\) events. The resulting method, RANGER-DTLx, handles these additional evolutionary events in a more functional manner compared to ecceERA, the only other parsimony-based model that also handles these events, while matching the time complexity of the fastest known algorithms for DTL reconciliation. Perhaps the most important contribution of this work is our systematic evaluation of the impact of accounting for \(\mathbb{TL}\) and \(\mathbb{TX}\) events and unsampled lineages on the accuracy of DTL reconciliation and of our ability to correctly detect \(\mathbb{TX}\) events. This evaluation, using both simulated and biological data, reveals many new insights that are important for understanding and interpreting microbial gene family evolution and that can inform the development of more sophisticated reconciliation models: First, we find that DTLx reconciliation (i.e., both RANGER-DTLx and ecceTERA) does not noticeably result in improved overall reconciliation accuracy compared to the simpler DTL reconciliation model. Second, we find that there is a very clear tradeoff between the precision and recall of \(\mathbb{TX}\) event detection, with precision remaining below 50% for even the most restrictive selection of \(\mathbb{TX}\) events and generally staying below 20% for non-negligible values (say, \(>\)0.3) of recall. Furthermore, third, our results on the biological dataset indicate that while allowing for \(\mathbb{TL}\) and \(\mathbb{TX}\) events leads to more parsimonious reconciliations in some cases, for the majority of gene trees \(\mathbb{TL}\) and \(\mathbb{TX}\) events lead to co-optimal reconciliations that can make it difficult to confidently infer if \(\mathbb{TL}\) and \(\mathbb{TX}\) events have, in fact, occurred. Nonetheless, we do find that DTLx reconciliation is indeed capable of identifying at least some \(\mathbb{TX}\) events with reasonable confidence and that RANGER-DTLx is actually able to find the phylogenetic placement of the unsampled donor of a \(\mathbb{TX}\) event with fairly high accuracy. Our experimental results suggest that it may be possible to use RANGER-DTLx to identify and locate major unsampled lineages on the tree of life and it would be interesting to further explore this application of the new DTLx reconciliation model. Several aspects of the proposed model could also be improved or evaluated further. In particular, though the proposed DTLx reconciliation model uses an undated species tree, the model can be easily extended to work with fully dated species trees. It is possible that the use of dated species trees may help to improve the precision and recall of \(\mathbb{TX}\) event inference and this possibility should be explored further. In particular, the use of a dated species tree can help identify transfers that appear to be going forward in time (i.e., where the recipient of a transfer event appears to be more recent than its donor), suggesting the presence of \(\mathbb{TX}\) events. Likewise, though RANGER-DTLx allows for the use of distance-dependant transfer costs, we did not evaluate the impact of using this feature in our current study. Using distance-dependent transfer costs (including \(\mathbb{TL}\) and \(\mathbb{TX}\) costs) may help to improve \(\mathbb{TX}\) inference accuracy. Finally, our experimental study suggests that precise detection of \(\mathbb{TX}\) events may be difficult to achieve using phylogenetic reconciliation alone, and that \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Dataset Size** & **RANGER-DTL** & **RANGER-DTLx** & **ecceTERA** \\ \hline \(<\)50 leaves & \(<\)1 s & 2.5 s & \(<\)1 s \\
50–100 leaves & \(<\)1 s & 12.4 s & \(<\)1 s \\ \(>\)100 leaves & \(<\)1 s & 24.5 s & \(<\)1 s \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Runtime comparison on the biological dataset.