Letter Limited Utility of Residue Masking for Positive-Selection Inference

Errors in multiple sequence alignments (MSAs) can reduce accuracy in positive-selection inference. Therefore, it has been suggested to filter MSAs before conducting further analyses. One widely used filter, Guidance, allows users to remove MSA positions aligned with low confidence. However, Guidance’s utility in positive-selection inference has been disputed in the literature. We have conducted an extensive simulation-based study to characterize fully how Guidance impacts positive-selection inference, specifically for protein-coding sequences of realistic divergence levels. We also investigated whether novel scoring algorithms, which phylogenetically corrected confidence scores, and a new gap-penalization score-normalization scheme improved Guidance’s performance. We found that no filter, including original Guidance, consis-tently benefitted positive-selection inferences. Moreover, all improvements detected were exceedingly minimal, and in certain circumstances, Guidance-based filters worsened inferences. Key words: multiple sequence alignment, alignment filters, positive-selection inference, sequence simulation. Multiple sequence alignment (MSA) construction represents the most fundamental step in nearly all molecular evolution analyses. Recently, several studies have shown that poor MSA quality can hinder accuracy in positive-selection inferenc