FUBAR (Fast, Unconstrained Bayesian AppRoximation)#

What question does this method answer?

Which site(s) in a gene are subject to pervasive (consistently across the entire phylogeny) diversifying selection?

Recommended Applications

  • Pervasive Selection / Pathogen Evolution: Ideally suited to identify sites under positive selection representing candidate sites subject to strong selective pressures across the entire phylogeny, such as adaptive immune escape by viruses.
  • Large Datasets: FUBAR is the recommended method for detecting pervasive selection at individual sites on large datasets (> 500 sequences) for which other methods (like FEL) have prohibitive runtimes.

FUBAR (Fast, Unconstrained Bayesian AppRoximation) uses a Bayesian approach to infer nonsynoymous () and synonymous () substitution rates on a per-site basis for a given coding alignment and corresponding phylogeny. This method assumes that the selection pressure for each site is constant along the entire phylogeny.

Key Differences from FEL#

Although FUBAR produces similar information to FEL, it has several key differences:

  • Posterior Probabilities: FUBAR employs a Bayesian algorithm to infer rates, and therefore it reports evidence for positive selection using posterior probabilities (which range from 0 to 1), rather than p-values. Generally, posterior probabilities are strongly suggestive of positive selection.
  • High Performance: FUBAR runs extremely quickly and is well-suited for analyzing large alignments with hundreds or thousands of sequences. This speed-up results from the novel strategy of employing a pre-specified discrete grid of and values to be applied across sites. This approach contrasts with the time-consuming FEL strategy of fitting a new MG94xREV model at each site.
  • Higher Power: FUBAR may have more power than FEL, in particular when positive selection is present but relatively weak (i.e., low values of ).

Citation#

If you use FUBAR in your analysis, please cite the following:

Murrell, B et al. "FUBAR: A Fast, Unconstrained Bayesian AppRoximation for inferring selection." Mol. Biol. Evol. 30, 1196–1205 (2013).