RASCAL: A randomized approach for coevolutionary analysis

A popular method for coevolutionary inference is cophylogenetic reconstruction where the branch length of the phylogenies have been previously derived. This approach, unlike the more generalized reconstruction techniques that are NP-Hard, can reconcile the shared evolutionary history of a pair of phylogenetic trees in polynomial time. This approach, while proven to be highly successful, requires a high polynomial running time. This is quickly becoming a limiting factor of this approach due to the continual increase in size of coevolutionary data sets. One existing method that combats this issue proposes a trade-off of accuracy for an asymptotic time complexity reduction. This technique in almost 70% of cases converges on Pareto optimal solutions in linear time. We build on this prior work by proposing an alternate linear time algorithm (RASCAL) that offers a significant accuracy increase, with RASCAL converging on Pareto optimal solutions in 85% of cases and unlike prior methods can ensure, with high probability, that all optimal solutions can be recovered, provided sufficient replicates are performed.