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rphenoscate: An R package for semantics‐aware evolutionary analyses of anatomical traits.

Authors :
Porto, Diego S.
Tarasov, Sergei
Charpentier, Caleb
Lapp, Hilmar
Balhoff, James P.
Vision, Todd J.
Dahdul, Wasila M.
Mabee, Paula M.
Uyeda, Josef
Source :
Methods in Ecology & Evolution; Oct2023, Vol. 14 Issue 10, p2531-2540, 10p
Publication Year :
2023

Abstract

Organismal anatomy is a hierarchical system of anatomical entities often imposing dependencies among multiple morphological characters. Ontologies provide a formal and computable framework for incorporating prior biological knowledge about anatomical dependencies in models of trait evolution. They also offer new opportunities for working with semantic representations of morphological data.In this work, we present a new R package—rphenoscate—that enables incorporating ontological knowledge in evolutionary analyses and exploring semantic patterns of morphological data. In conjunction with rphenoscape, it allows for assembling synthetic phylogenetic character matrices from semantic phenotypes of morphological data. We showcase the package functionality with data sets from bees and fishes.We demonstrate that ontologies can be employed to automatically set up evolutionary models accounting for trait dependencies in stochastic character mapping. We also demonstrate how ontology annotations can be explored to interrogate patterns of morphological evolution. Finally, we demonstrate that synthetic character matrices assembled from semantic phenotypes retain most of the phylogenetic information from their original data sets.Ontologies will become important tools for integrating anatomical knowledge into phylogenetic methods and making morphological data FAIR compliant—a critical step of the ongoing 'phenomics' revolution. Our new package offers key advancements towards this goal. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
2041210X
Volume :
14
Issue :
10
Database :
Complementary Index
Journal :
Methods in Ecology & Evolution
Publication Type :
Academic Journal
Accession number :
172782148
Full Text :
https://doi.org/10.1111/2041-210X.14210