# Improved Duplication-Transfer-Loss Reconciliation with
## 4 Results
### Results on Simulated Datasets
The sets \(\Sigma_{T}\), \(\Delta_{T}\), \(\Theta_{T}\), \(\mathcal{M}_{T}\), and \(\tau_{T}\) are defined analogously for the ground truth reconciliation \(\beta\). Overall accuracy is quantified through the following three metrics:  
\[\text{Event accuracy} = \frac{|\Sigma_{\alpha}\cap\Sigma_{\beta}|+|\Delta_{\alpha}\cap \Delta_{\beta}|+|\Theta_{\alpha}\cap\Theta_{\beta}|}{|I(G)|}\] \[\text{Mapping accuracy} = \frac{|\{g\colon\mathcal{M}_{\alpha}(g)=\mathcal{M}_{\beta}(g)\} |}{|I(G)|}\] \[\text{Recipient accuracy} = \frac{|\{g\colon g\in\Theta_{\alpha}\cap\Theta_{\beta}\text{ and }\tau_{\alpha}(g)=\tau_{\beta}(g)\}|}{|\Theta_{\alpha}\cap\Theta_{\beta}|}\]  
To account for multiple optimal reconciliations, we used the standard approach of assigning the most well supported mapping and event type for each of the three methods/software. Specifically, 100 random optimal reconciliations were sampled for each gene tree/species tree pair, for both RANGER-DTLx and RANGER-DTL, and each gene tree node was assigned the most frequently observed mapping and event type among the 100 samples [11]. A similar procedure was followed for eceTERA, but based on the reconciliation graph computed by eceTERA [12], rather than on uniform random sampling. Recall that, unlike eceTERA, our DTLx reconciliation framework allows for independent assignment of \(\mathbb{TL}\) and \(\mathbb{TX}\) event costs. We therefore tried two different costs for \(\mathbb{TX}\) events in all our experiments: 3 and 4. We fixed the cost of a \(\mathbb{TL}\) event at 4. The costs for other events were fixed as follows for all three methods: Loss cost = 1, duplication cost = 2, and transfer cost = 3, which correspond to the defaults used in RANGER-DTL and eceTERA. Based on these costs, eceTERA automatically assigns \(\mathbb{TL}\) events a cost of 4 (equivalent to one transfer plus one loss), and \(\mathbb{TX}\) events a cost of 3 (equivalent to one transfer event). The results of our analysis appear in Table 2. As the table shows, RANGER-DTL, RANGER-DTLx with \(\mathbb{TX}\) cost 4, and eceTERA have nearly indistinguishable reconciliation accuracies across both datasets, while RANGER-DTLx with \(\mathbb{TX}\) cost 3 shows slightly worse event and mapping accuracies but slightly better recipient accuracy. These results suggest that models that account for \(\mathbb{TL}\) and \(\mathbb{TX}\) events may not result in improved overall reconciliation accuracy compared to traditional DTL reconciliation, at least for low to moderate rates of \(\mathbb{TX}\) events as in our datasets. Importantly, these results also show that allowing for \(\mathbb{TL}\) and \(\mathbb{TX}\) events does not worsen reconciliation accuracy, even when the rate of \(\mathbb{TX}\) events is low (Dataset-1). _Accuracy of \(\mathbb{TX}\) event detection_. Next, we assessed how well RANGER-DTLx and ecceTERA can infer ground-truth \(\mathbb{TX}\) events. We labelled transfers from unsampled lineages as ground truth \(\mathbb{TX}\) events if, in the event log generated by ZOMBI, the gene survives in an extant lineage but the original copy goes extinct. While the original name of the internal gene tree node representing the extinct gene is absent from the input gene tree, we can verify if either method correctly detects this transfer by looking for a \(\mathbb{TX}\) event with the same recipient as the ground truth transfer. We first assessed how well \(\mathbb{TX}\) events were detected, in terms of precision and recall, in a single random optimal reconciliation computed by each method. Table 3 shows the results of this analysis, which reveals several interesting insights. First, and perhaps most strikingly, we find that none of the methods shows high precision in detecting \(\mathbb{TX}\) events, with ecceTERA and RANGER-DTLx with \(\mathbb{TX}\) cost 3 both showing 10% precision in the two datasets, and RANGER-DTLx with \(\mathbb{TX}\) cost 4 showing much higher, but still relatively low, precision of 17.24% in Dataset-1 and 29.41% in Dataset-2.