Abstract:
:Inaccurate inference of positional homologies in multiple sequence alignments and systematic errors introduced by alignment heuristics obfuscate phylogenetic inference. Alignment masking, the elimination of phylogenetically uninformative or misleading sites from an alignment before phylogenetic analysis, is a common practice in phylogenetic analysis. Although masking is often done manually, automated methods are necessary to handle the much larger data sets being prepared today. In this study, we introduce the concept of subsplits and demonstrate their use in extracting phylogenetic signal from alignments. We design a clustering approach for alignment masking where each cluster contains similar columns-similarity being defined on the basis of compatible subsplits; our approach then identifies noisy clusters and eliminates them. Trees inferred from the columns in the retained clusters are found to be topologically closer to the reference trees. We test our method on numerous standard benchmarks (both synthetic and biological data sets) and compare its performance with other methods of alignment masking. We find that our method can eliminate sites more accurately than other methods, particularly on divergent data, and can improve the topologies of the inferred trees in likelihood-based analyses. Software available upon request from the author.
journal_name
Mol Biol Evoljournal_title
Molecular biology and evolutionauthors
Rajan Vdoi
10.1093/molbev/mss264subject
Has Abstractpub_date
2013-03-01 00:00:00pages
689-712issue
3eissn
0737-4038issn
1537-1719pii
mss264journal_volume
30pub_type
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