Phenotyping in the era of genomics: MaTrics—a digital character matrix to document mammalian phenotypic traits

Abstract

A new and uniquely structured matrix of mammalian phenotypes, MaTrics (Mammalian Traits for Comparative Genomics) in a digital form is presented. By focussing on mammalian species for which genome assemblies are available, MaTrics provides an interface between mammalogy and comparative genomics.MaTrics was developed within a project aimed to find genetic causes of phenotypic traits of mammals using Forward Genomics. This approach requires genomes and comprehensive and recorded information on homologous phenotypes that are coded as discrete categories in a matrix. MaTrics is an evolving online resource providing information on phenotypic traits in numeric code; traits are coded either as absent/present or with several states as multistate. The state record for each species is linked to at least one reference (e.g., literature, photographs, histological sections, CT scans, or museum specimens) and so MaTrics contributes to digitalization of museum collections. Currently, MaTrics covers 147 mammalian species and includes 231 characters related to structure, morphology, physiology, ecology, and ethology and available in a machine actionable NEXUS-format*. Filling MaTrics revealed substantial knowledge gaps, highlighting the need for phenotyping efforts. Studies based on selected data from MaTrics and using Forward Genomics identified associations between genes and certain phenotypes ranging from lifestyles (e.g., aquatic) to dietary specializations (e.g., herbivory, carnivory). These findings motivate the expansion of phenotyping in MaTrics by filling research gaps and by adding taxa and traits. Only databases like MaTrics will provide machine actionable information on phenotypic traits, an important limitation to genomics. MaTrics is available within the data repository Morph·D·Base (www.morphdbase.de).

Reconstruction of evolutionary changes in fat and toxin consumption reveals associations with gene losses in mammals: A case study for the lipase inhibitor PNLIPRP1 and the xenobiotic receptor NR1I3

“…These research gaps ranged between 1% and up to 60%, depending on the requirements of the diet composition models M1-M4. Apart from nutrition, the knowledge gap on general/basic phenotypic information is substantial for mammals (Stefen et al, 2021). Nonetheless, the present study serves as an example that the combination of comparative genomics (e.g., Forward Genomics) and comparative trait analyses is a powerful tool.…”

“…Recording feeding habits and dietary components as distinct categories in a trait matrix (recording as traits and states) is one approach to associate gene loss with species-specific dietary components such as fat or toxins (Stefen et al, 2021). However, definitions and the level of details given on feeding habit categories or description of main dietary components varies from study to study (e.g., Hecker et al, 2019b;Román-Palacios et al, 2019).…”

“…Similar to the diet model, it focuses on the major proportion of food actually found in the stomach. Primary data were collected from the literature, and the results of trait recording are deposited as part of a larger trait matrix (Stefen et al, 2021; https://www.morph dbase.de/?MaTri cs-Mx-v1).…”

“…Although this is showcased in our study of PNLIPRP1, such approach is also relevant for research on detoxification molecules. By recording dietary components and feeding habits in a fully documented and publicly available data repository (Morph•D•Base; MaTrics: https://www.morph dbase.de/?MaTri cs-Mx-v1, Stefen et al, 2021), the data collection used for this study can be applied to further post hoc studies of comparative genomic data (e.g., initiatives such as Zoonomia; Genereux et al, 2020; https://zoono miapr oject.org/). Our study also revealed the substantial lack of knowledge about diet, diet composition and feeding habits.…”

rphenoscate: An R package for semantic-aware evolutionary analyses of anatomical traits

“…Furthermore, ontologies not only offer a solution for the longstanding 'dependency problem' among anatomical traits in phylogenetics (Tarasov, 2019) but also, an interoperable framework for representing morphological knowledge and integrating it with other knowledge types. Based on theoretical and practical grounds, recent works have suggested new schema for employing morphological data in phylogenetics (Vogt, 2018a,b), for example, by using semantically-enriched character matrices (e.g., Ramírez et al, 2007;Stefen et al, 2022), semantic instance anatomies (e.g., Vogt, 2018aVogt, ,b, 2019, or semantic phenotypes (e.g., Deans et al, 2012;Balhoff et al, 2010Balhoff et al, , 2014. By representing organismal anatomy in a semantically-aware format (i.e., ontology-annotated) and moving beyond the standard phylogenetic character matrices, it is possible to make morphological data more easily reusable, parsable, and integrated across different studies and domains.…”