Improving the performance of positive selection inference by filtering unreliable alignment regions

Eyal Privman, Osnat Penn, Tal Pupko

Research output: Contribution to journalArticlepeer-review


Errors in the inferred multiple sequence alignment may lead to false prediction of positive selection. Recently, methods for detecting unreliable alignment regions were developed and were shown to accurately identify incorrectly aligned regions. While removing unreliable alignment regions is expected to increase the accuracy of positive selection inference, such filtering may also significantly decrease the power of the test, as positively selected regions are fast evolving, and those same regions are often those that are difficult to align. Here, we used realistic simulations that mimic sequence evolution of HIV-1 genes to test the hypothesis that the performance of positive selection inference using codon models can be improved by removing unreliable alignment regions. Our study shows that the benefit of removing unreliable regions exceeds the loss of power due to the removal of some of the true positively selected sites.

Original languageEnglish
Pages (from-to)1-5
Number of pages5
JournalMolecular Biology and Evolution
Issue number1
StatePublished - Jan 2012
Externally publishedYes


  • Alignment reliability
  • Molecular evolution
  • Multiple sequence alignment
  • Phylogeny
  • Positive selection

ASJC Scopus subject areas

  • Ecology, Evolution, Behavior and Systematics
  • Molecular Biology
  • Genetics


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