# Improved Duplication-Transfer-Loss Reconciliation with
###### Abstract
However, the underlying evolutionary model on which the probabilistic model of [PERSON] et al. [10] is based makes several simplifying assumptions, for example, the assumption of an identical rate of transfer between any two species. The parsimony-based model of ccccTERA [16] addresses some of these limitations. In ccccTERA, transfers from unsampled lineages are modeled by adding a single lineage to the species tree (as an extra branch attached to the root of the species tree) such that this additional lineage represents all unsampled lineages. In particular, transfers can occur to or from this additional lineage, representing transfers to or from unsampled lineages, respectively. Among other limitations, this setup makes it difficult to infer the \"location\" on the species tree of an unsampled donor of a transfer and to customize transfer event costs based on phylogenetic distance. Furthermore, and perhaps most importantly, there has never been any systematic evaluation of the accuracy of transfers inferred as transferred from unsampled lineages or of the impact of accounting for transfers from unsampled lineages on overall reconciliation accuracy. In this work, we introduce an extended DTL reconciliation model, building upon the widely-used parsimony-based DTL reconciliation framework of [PERSON] et al. [7,11], that accounts for unsampled species lineages in a more effective and useful manner than ccccTERA. The new model, which we refer to as the _DTLx reconciliation model_, has several important advantages over the model implemented in ecccTERA (and also the model implemented in [10]): First, our new model explicitly infers the location on the species tree of each unsampled lineage associated with an identified transfer event. Second, our model allows greater flexibility in assigning event costs to different types of transfer events (e.g., those from unsampled lineages), allowing for the costs to be fine tuned to the specific dataset or evolutionary scenario under consideration. Furthermore, third, the new model makes it possible to use variable transfer event costs based on the phylogenetic distance between donor and recipient species even when the donor is an unsampled species; this is important since transfers happen more frequently between more closely related species [23]. As an additional minor contribution, the DTLx reconciliation model also models _transfer-loss_ events, which are currently only modeled in two other parsimonious DTL reconciliation frameworks, Mowgli [3] and ccccTERA [16]. We show how to compute optimal (most parsimonious) reconcilations under the DTLx reconciliation model in \(O(mn)\) time, where \(m\) and \(n\) denote the number of leaves in the gene tree and species tree, respectively, matching the time complexity of the fastest known algorithms for any DTL reconciliation model. We provide an \(O(mn^{2})\)-time algorithm for sampling optimal reconciliations uniformly at random under the DTLx reconciliation model, thus making it possible to efficiently sample from the space of all optimal DTLx reconciliations, and perform the first systematic simulation study to assess the impact of accounting for unsampled lineages on the accuracy of DTL reconciliation. We also compare the accuracies of inferring transfers from unsampled lineages under our new model and the model implemented in ecceTERA (currently the only other parsimony-based model that accounts for unsampled lineages). Importantly, our experimental results, on both simulated and real data, suggest that models that account for unsampled lineages have only a small impact on overall reconciliation accuracy and that there is a clear trade-off between precision and recall of correctly detecting transfers from unsampled lineages. Encouragingly, we also find that DTLx reconciliation is capable of identifying at least some transfers from unsampled lineages with reasonable confidence and that it is able to identify the phylogenetic placement of the unsampled donors of such transfer events with high accuracy.