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Phylogenetic Tree Estimation With and Without Alignment: New Distance Methods and Benchmarking
Uppsala University, Disciplinary Domain of Science and Technology, Biology, Department of Ecology and Genetics, Evolutionary Biology. (Whelan Lab)ORCID iD: 0000-0003-3056-3173
Uppsala University, Disciplinary Domain of Science and Technology, Biology, Department of Ecology and Genetics, Evolutionary Biology.
2017 (English)In: Systematic Biology, ISSN 1063-5157, E-ISSN 1076-836X, Vol. 66, no 2, p. 218-231Article in journal (Refereed) Published
Abstract [en]

Phylogenetic tree inference is a critical component of many systematic and evolutionary studies. The majority of these studies are based on the two-step process of multiple sequence alignment followed by tree inference, despite persistent evidence that the alignment step can lead to biased results. Here we present a two-part study that first presents PaHMM-Tree, a novel neighbor joining-based method that estimates pairwise distances without assuming a single alignment. We then use simulations to benchmark its performance against a wide-range of other phylogenetic tree inference methods, including the first comparison of alignment-free distance-based methods against more conventional tree estimation methods. Our new method for calculating pairwise distances based on statistical alignment provides distance estimates that are as accurate as those obtained using standard methods based on the true alignment. Pairwise distance estimates based on the two-step process tend to be substantially less accurate. This improved performance carries through to tree inference, where PaHMM-Tree provides more accurate tree estimates than all of the pairwise distance methods assessed. For close to moderately divergent sequence data we find that the two-step methods using statistical inference, where information from all sequences is included in the estimation procedure, tend to perform better than PaHMM-Tree, particularly full statistical alignment, which simultaneously estimates both the tree and the alignment. For deep divergences we find the alignment step becomes so prone to error that our distance-based PaHMM-Tree outperforms all other methods of tree inference. Finally, we find that the accuracy of alignment-free methods tends to decline faster than standard two-step methods in the presence of alignment uncertainty, and identify no conditions where alignment-free methods are equal to or more accurate than standard phylogenetic methods even in the presence of substantial alignment error.

Place, publisher, year, edition, pages
2017. Vol. 66, no 2, p. 218-231
Keyword [en]
Alignment-free, distance-based phylogenetics, pair Hidden Markov Models, phylogenetic inference, statistical alignment
National Category
Evolutionary Biology
Identifiers
URN: urn:nbn:se:uu:diva-316533DOI: 10.1093/sysbio/syw074ISI: 000397703800009PubMedID: 27633353OAI: oai:DiVA.org:uu-316533DiVA, id: diva2:1078108
Available from: 2017-03-02 Created: 2017-03-02 Last updated: 2017-04-20Bibliographically approved

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