Directed Research Project Details

On Phylogenetic Uncertainty and Ancestral Sequence Reconstruction

Abstract

Life has been evolving on Earth for over 3.8 billion years. However, given the extreme paucity of molecular fossils and the relative brevity of a human lifetime, the evolutionary history of genes can be difficult to study. Fortunately, the computational methods of ancestral sequence reconstruction (ASR) can be used to statistically infer the sequences of extinct genes. In this project, I investigate one aspect of ASR algorithms which may impact accuracy: phylogenetic uncertainty. Most ASR algorithms assume the phylogeny is known with certainty; in practice, this assumption is rarely valid. Does ignoring phylogenetic uncertainty affect ASR accuracy?

To answer this question, I proposed an empirical Bayesian algorithm for integrating phylogenetic uncertainty in ASR. I examined this method in simulated and real conditions. My results are surprising and nonintuitive: phylogenetic uncertainty is not correlated with the accuracy of reconstructed ancestral states. The conditions which produce phylogenetic uncertainty result in ancestral states on alternate trees which are similar, if not identical, to the ancestral states on the maximum likelihood tree. Ultimately, integrating phylogenetic uncertainty does not significantly affect the accuracy of reconstructed ancestral sequences.