Accurate large-scale phylogeny-aware alignment using BAli-Phy

Motivation: BAli-Phy, a popular Bayesian method that co-estimates multiple sequence alignments and phylogenetic trees, is a rigorous statistical method, but due to its computational requirements, it has generally been limited to relatively small datasets (at most about 100 sequences). Here, we repurpose BAli-Phy as a 'phylogeny-aware' alignment method: we estimate the phylogeny from the input of unaligned sequences, and then use that as a fixed tree within BAli-Phy. Results: We show that this approach achieves high accuracy, greatly superior to Prank, the current most popular phylogeny-aware alignment method, and is even more accurate than MAFFT, one of the top performing alignment methods in common use. Furthermore, this approach can be used to align very large datasets (up to 1000 sequences in this study).

Automated summary

BAli-Phy, originally designed for co-estimation of multiple sequence alignments and phylogenetic trees, has been repurposed as a 'phylogeny-aware' alignment method in this study. By utilizing estimated phylogenies as fixed trees, the approach achieves higher accuracy than Prank and even outperforms MAFFT, allowing for alignment of large datasets up to 1000 sequences.