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This article is part of the supplement: Proceedings of the Tenth Annual Research in Computational Molecular Biology (RECOMB) Satellite Workshop on Comparative Genomics

Open Access Proceedings

Inference of reticulate evolutionary histories by maximum likelihood: the performance of information criteria

Hyun Jung Park1 and Luay Nakhleh2

Author Affiliations

1 Department of Molecular and Cellular Biology, Baylor College of Medicine, Houston, TX, USA

2 Department of Computer Science, Rice University, Houston, TX, USA

BMC Bioinformatics 2012, 13(Suppl 19):S12  doi:10.1186/1471-2105-13-S19-S12

Published: 19 December 2012

Abstract

Background

Maximum likelihood has been widely used for over three decades to infer phylogenetic trees from molecular data. When reticulate evolutionary events occur, several genomic regions may have conflicting evolutionary histories, and a phylogenetic network may provide a more adequate model for representing the evolutionary history of the genomes or species. A maximum likelihood (ML) model has been proposed for this case and accounts for both mutation within a genomic region and reticulation across the regions. However, the performance of this model in terms of inferring information about reticulate evolution and properties that affect this performance have not been studied.

Results

In this paper, we study the effect of the evolutionary diameter and height of a reticulation event on its identifiability under ML. We find both of them, particularly the diameter, have a significant effect. Further, we find that the number of genes (which can be generalized to the concept of "non-recombining genomic regions") that are transferred across a reticulation edge affects its detectability. Last but not least, a fundamental challenge with phylogenetic networks is that they allow an arbitrary level of complexity, giving rise to the model selection problem. We investigate the performance of two information criteria, the Akaike Information Criterion (AIC) and the Bayesian Information Criterion (BIC), for addressing this problem. We find that BIC performs well in general for controlling the model complexity and preventing ML from grossly overestimating the number of reticulation events.

Conclusion

Our results demonstrate that BIC provides a good framework for inferring reticulate evolutionary histories. Nevertheless, the results call for caution when interpreting the accuracy of the inference particularly for data sets with particular evolutionary features.