# Predicting the Evolution of Syntenies--An Algorithmic Review
## 6 Conclusions  
Despite the large effort dedicated to the development of methods for inferring the evolution of synteny blocks, a lot remains to be done towards a unifying model allowing consideration of the variety of evolutionary events shaping the genomes. As reviewed in this paper, most of the algorithmic effort has been invested in the genome rearrangement field on one side, and in the reconciliation field for inferring individual gene gain and loss on the other side. Combining order and content information in the purpose of inferring a co-evolutionary history of genes remains a largely under-explored field. In most studies on gene gain and loss, segmental movements are indirectly inferred from single-gene movements, by combining co-occurring events or concatenating individual adjacencies. For example, although allowing for a wide variety of events (rearrangements, tandem duplications, losses, HGTs, etc. ), the DeCo collection of algorithms stand on minimizing single adjacencies' gain or breakage with no direct link to segmental events. The same holds for algorithms dedicated to minimizing episodes of events, only indirectly referring to multiple duplications and ignoring order information. As for the reviewed algorithms for tandemly arrayed gene clusters, although accounting for both rearrangements and segmental duplications and losses, duplications are limited to tandem duplications, and they do not allow for the study of paralogous syntenies, and do not account for the gain or loss of syntenies. Conversely, the Super-Reconciliation approach only accounts for transposed duplications, ignoring tandem duplications. This latter approach, which is a direct generalization of the reconciliation approach to segmental events, opens the door to the same algorithmic perspectives as the classical reconciliation approach, such as generalization to HGT events. However, its main limitation is the difficulty of generating an accurate synteny tree, though a solution may actually be to choose a tree optimizing such a Super-Reconciliation cost. In any case, a better use of individual gene trees, including the inconsistency among those trees, should be considered, ways of including tandem duplications should be explored, as well as better solutions to integrate rearrangement inference algorithms. To summarize, accounting at the same time for sequence divergence (gene trees), gene order and gene content, evolving through punctual mutations (substitutions and indels), rearrangements and segmental gain and loss events, remain a challenge. Beyond the difficulty of designing an appropriate algorithmic method outpassing the limitations of existing methods, the problem is to design appropriate scores accounting for the variety of evolutionary events. Designing such a general score has been undertaken in [93], but a lot remains to do in this field. Of course, all the algorithmic methods presented in this paper suffer from the standard limitations of parsimony methods [138; 139], such as impossibility of accounting for multiple state changes along a branch of a phylogeny, or uncertainty in phylogenetic reconstructions. An overall statistical framework for evaluating evolutionary hypothesis [140] is surely better than a heuristic outputting a single solution with no measure of reliability. However, the largest the list of considered evolutionary events, the more difficult is the problem of an appropriate sampling for such a statistical study. Being able to generate the whole set of most parsimonious scenarios remains an interesting approach for a probabilistic evaluation of the solutions. Another related problem lies on the possibility of performing the appropriate simulations for testing the accuracy of the developed algorithms. This goal is also complicated with the increase of the considered data types and evolutionary events.