Taming the Selection of Optimal Substitution Models in Phylogenomics by Site Subsampling and Upsampling

The selection of the optimal substitution model of molecular evolution imposes a high computational burden for long sequence alignments in phylogenomics. We discovered that the analysis of multiple tiny subsamples of site patterns from a full sequence alignment recovers the correct optimal substitution model when sites in the subsample are upsampled to match the total number of sites in the full alignment. The computational costs of maximum-likelihood analyses are reduced by orders of magnitude in the subsample-upsample (SU) approach because the upsampled alignment contains only a small fraction of all site patterns. We present an adaptive protocol, ModelTamer, that implements the new SU approach and automatically selects subsamples to estimate optimal models reliably. ModelTamer selects models hundreds to thousands of times faster than the full data analysis while needing megabytes rather than gigabytes of computer memory.

Automated summary

The study discovers that using subsample-upsample approach can significantly reduce the computational costs in analyzing long sequence alignments in molecular evolution, while still recovering the correct optimal substitution model. An adaptive protocol called ModelTamer is proposed, which can select the optimal models in much shorter time and with much less memory usage.