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Inferring the Deep Past from Molecular Data

Lookup NU author(s): Emeritus Professor T. Martin Embley FMedSci FRS


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© The Author(s) 2021. Published by Oxford University Press on behalf of the Society for Molecular Biology and Evolution.There is an expectation that analyses of molecular sequences might be able to distinguish between alternative hypotheses for ancient relationships, but the phylogenetic methods used and types of data analyzed are of critical importance in any attempt to recover historical signal. Here, we discuss some common issues that can influence the topology of trees obtained when using overly simple models to analyze molecular data that often display complicated patterns of sequence heterogeneity. To illustrate our discussion, we have used three examples of inferred relationships which have changed radically as models and methods of analysis have improved. In two of these examples, the sister-group relationship between thermophilic Thermus and mesophilic Deinococcus, and the position of long-branch Microsporidia among eukaryotes, we show that recovering what is now generally considered to be the correct tree is critically dependent on the fit between model and data. In the third example, the position of eukaryotes in the tree of life, the hypothesis that is currently supported by the best available methods is fundamentally different from the classical view of relationships between major cellular domains. Since heterogeneity appears to be pervasive and varied among all molecular sequence data, and even the best available models can still struggle to deal with some problems, the issues we discuss are generally relevant to phylogenetic analyses. It remains essential to maintain a critical attitude to all trees as hypotheses of relationship that may change with more data and better methods.

Publication metadata

Author(s): Williams TA, Schrempf D, Szollosi GJ, Cox CJ, Foster PG, Embley TM

Publication type: Article

Publication status: Published

Journal: Genome Biology and Evolution

Year: 2021

Volume: 13

Issue: 5

Online publication date: 27/03/2021

Acceptance date: 22/03/2021

ISSN (print): 1759-6653

Publisher: Oxford University Press


DOI: 10.1093/gbe/evab067

PubMed id: 33772552


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