A graph-based approach for improving the homologyinference in multiple sequence alignments

• Multiple sequence alignment (MSA) is ubiquitous in evolutionary studies and other areas ofbioinformatics. In nearly all cases MSAs are taken to be a known and xed quantity on which toperform downstream analysis despite extensive evidence that MSA accuracy and uncertainty aectsresults. Mistakes in the MSA are known to cause a wide range of problems for downstream evolutionaryinference, ranging from false inference of positive selection to long branch attraction artifacts. The mostpopular approach to dealing with this problem is to remove (lter) specic columns in the MSA thatare thought to be prone to error, either through proximity to gaps or through some scoring function.Although popular, this approach has had mixed success and several studies have even suggested thatltering might be detrimental to phylogenetic studies. Here we present a dierent approach to dealingwith MSA accuracy and uncertainty through a graph-based approach implemented in the freely availablesoftware Divvier. The aim of Divvier is to identify clusters of characters that have strong statisticalevidence of shared homology, based on the output of a pair hidden Markov model. These clusters canthen be used to either lter characters out the MSA, through a process we call partial ltering, or torepresent each of the clusters in a new column, through a process we call divvying up. We validateour approach through its performance on real and simulated benchmarks, nding Divvier substantiallyoutperforms all other ltering software for treating MSAs by retaining more true positive homology callsand removing more false positive homology calls. We also nd that Divvier, in contrast to other lteringtools, can alleviate long branch attraction artifacts induced by MSA and reduces the variation in treeestimates caused by MSA uncertainty.

Subject headings

NATURVETENSKAP  -- Biologi -- Evolutionsbiologi (hsv//swe)

NATURAL SCIENCES  -- Biological Sciences -- Evolutionary Biology (hsv//eng)

Publication and Content Type

vet (subject category)

ovr (subject category)