Gaurav Bhardwaj 1 , 2 , 6 , 8 , Kyung Dae Ko 1 , 2 , Yoojin Hong 1 , 2 , 3 , Zhenhai Zhang 1 , 4 , Ngai Lam Ho 1 , 3 , Sree V. Chintapalli 7 , 8 , Lindsay A. Kline 1 , 2 , Matthew Gotlin 1 , 2 , David Nicholas Hartranft 1 , 2 , Morgen E. Patterson 1 , Foram Dave 1 , Evan J. Smith 1 , 2 , Edward C. Holmes 2 , 5 , Randen L. Patterson 1 , 6 , 7 , 8 , * , Damian B. van Rossum 1 , 2 , 8 , *
13 April 2012
Both multiple sequence alignment and phylogenetic analysis are problematic in the “twilight zone” of sequence similarity (≤25% amino acid identity). Herein we explore the accuracy of phylogenetic inference at extreme sequence divergence using a variety of simulated data sets. We evaluate four leading multiple sequence alignment (MSA) methods (MAFFT, T-COFFEE, CLUSTAL, and MUSCLE) and six commonly used programs of tree estimation (Distance-based: Neighbor-Joining; Character-based: PhyML, RAxML, GARLI, Maximum Parsimony, and Bayesian) against a novel MSA-independent method (PHYRN) described here. Strikingly, at “midnight zone” genetic distances (∼7% pairwise identity and 4.0 gaps per position), PHYRN returns high-resolution phylogenies that outperform traditional approaches. We reason this is due to PHRYN's capability to amplify informative positions, even at the most extreme levels of sequence divergence. We also assess the applicability of the PHYRN algorithm for inferring deep evolutionary relationships in the divergent DANGER protein superfamily, for which PHYRN infers a more robust tree compared to MSA-based approaches. Taken together, these results demonstrate that PHYRN represents a powerful mechanism for mapping uncharted frontiers in highly divergent protein sequence data sets.