Identifying Clusters of High Confidence Homologies in Multiple Sequence Alignments

Abstract

Abstract Multiple sequence alignment (MSA) is ubiquitous in evolution and bioinformatics. MSAs are usually taken to be a known and fixed quantity on which to perform downstream analysis despite extensive evidence that MSA accuracy and uncertainty affect results. These errors are known to cause a wide range of problems for downstream evolutionary inference, ranging from false inference of positive selection to long branch attraction artifacts. The most popular approach to dealing with this problem is to remove (filter) specific columns in the MSA that are thought to be prone to error. Although popular, this approach has had mixed success and several studies have even suggested that filtering might be detrimental to phylogenetic studies. We present a graph-based clustering method to address MSA uncertainty and error in the software Divvier (available at https://github.com/simonwhelan/Divvier), which uses a probabilistic model to identify clusters of characters that have strong statistical evidence of shared homology. These clusters can then be used to either filter characters from the MSA (partial filtering) or represent each of the clusters in a new column (divvying). We validate Divvier through its performance on real and simulated benchmarks, finding Divvier substantially outperforms existing filtering software by retaining more true pairwise homologies calls and removing more false positive pairwise homologies. We also find that Divvier, in contrast to other filtering tools, can alleviate long branch attraction artifacts induced by MSA and reduces the variation in tree estimates caused by MSA uncertainty.