These surprising findings may change the way evolutionary biologists infer the relationships among species - a cornerstone of modern biology - according to researchers at the University of Oregon.
Joe Thornton, a UO assistant professor of biology, and Bryan Kolaczkowski, a graduate student in computer and information science, used a small supercomputer to simulate the evolution of thousands of gene sequences on a hypothetical evolutionary tree. They examined which methods for inferring historical relationships correctly recovered that tree from the simulated data.
They found that a simple logical method known as maximum parsimony is far more accurate under a wide range of conditions than the state-of-the-art technique known as maximum likelihood, which uses a mathematic model of the evolutionary process.
"It turns out that the complicated method performs well when reality is simple, but the simpler method is much more accurate when reality is complex," Thornton said.
During the past decade, maximum likelihood has eclipsed maximum parsimony as a tool for evolutionary biologists, largely because of studies that found it to be a more accurate and powerful tool.
Thornton and Kolaczkowski were not convinced by these studies, which simulated evolution using a simplistic and unrealistic process in which the various parts of a gene evolve at the same rate in all species. So they evaluated, for the first time, the performance of the methods when the evolutionary process changes over time, as it is known to do.
"Maximum likelihood often gets the wrong tree because it assumes evolution can be accurately captured in a statistical model, but the assumptions
Contact: Melody Ward Leslie
University of Oregon