Erasing errors due to alignment ambiguity when estimating positive selection.

ABSTRACT: Current estimates of diversifying positive selection rely on first having an accurate multiple sequence alignment. Simulation studies have shown that under biologically plausible conditions, relying on a single estimate of the alignment from commonly used alignment software can lead to unacceptably high false-positive rates in detecting diversifying positive selection. We present a novel statistical method that eliminates excess false positives resulting from alignment error by jointly estimating the degree of positive selection and the alignment under an evolutionary model. Our model treats both substitutions and insertions/deletions as sequence changes on a tree and allows site heterogeneity in the substitution process. We conduct inference starting from unaligned sequence data by integr

SUBMITTER: Redelings B 

PROVIDER: S-EPMC4155473 | biostudies-literature | 2014 Aug

REPOSITORIES: biostudies-literature