AbstractThe use of computational techniques to analyze viral sequence data and ultimately inform public health intervention has become increasingly common in the realm of epidemiology. These methods typically attempt to make epidemiological inferences based on multiple sequence alignments and phylogenies estimated from the raw sequence data. Like all estimation techniques, multiple sequence alignment and phylogenetic inference tools are error-prone, and the impacts of such imperfections on downstream epidemiological inferences are poorly understood. To address this, we executed multiple commonly-used workflows for conducting viral phylogenetic analyses on simulated viral sequence data modeling HIV, HCV, and Ebola, and we computed multiple methods of accuracy motivated by transmission clustering techniques. For multiple sequence alignment, MAFFT consistently outperformed MUSCLE and Clustal Omega in both accuracy and runtime. For phylogenetic inference, FastTree 2, IQ-TREE, RAxML-NG, and PhyML had similar topological accuracies, but branch lengths and pairwise distances were consistently most accurate in phylogenies inferred by RAxML-NG. However, FastTree 2 was orders of magnitude faster than the other tools, and when the other tools were used to optimize branch lengths along a fixed topology provided by FastTree 2 (i.e., no tree search), the resulting phylogenies had accuracies that were indistinguishable from their original counterparts, but with a fraction of the runtime. Our results indicate that an ideal workflow for viral phylogenetic inference is to (1) use MAFFT to perform MSA, (2) use FastTree 2 under the GTR model with discrete gamma-distributed site-rate heterogeneity to quickly obtain a reasonable tree topology, and (3) use RAxML-NG to optimize branch lengths along the fixed FastTree 2 topology.
Identifying Selection in the Within-Host Evolution of Influenza Using Viral Sequence Data
