PARAMO: A Pipeline for Reconstructing Ancestral Anatomies Using Ontologies and Stochastic Mapping

Tarasov, Sergei; Mikó, István; Yoder, Matthew; Uyeda, Josef C.

doi:10.1093/isd/ixz009

Cited by 19 publications

(30 citation statements)

References 23 publications

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“…The types of relations included as well as the ontology structure itself can influence the overall similarity values between concepts (Pesquita et al 2009; Manda and Vision 2018). Another possibility is to use ontological knowledge to explicitly account for anatomical dependencies among individual traits when specifying models of character evolution (Tarasov 2019, 2020; Tarasov et al 2019). This can be achieved by constructing models of discrete trait evolution enabling ontology-aware transition matrices through structured Markov models equipped with hidden states (Tarasov 2019).…”

Section: Methodsmentioning

confidence: 99%

Assessing Bayesian Phylogenetic Information Content of Morphological Data Using Knowledge from Anatomy Ontologies

Porto

Dahdul

Lapp

et al. 2022

Preprint

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Morphology remains a primary source of phylogenetic information for many groups of organisms, and the only one for most fossil taxa. Organismal anatomy is not a collection of randomly assembled and independent "parts", but instead a set of dependent and hierarchically nested entities resulting from ontogeny and phylogeny. How do we make sense of these dependent and at times redundant characters? One promising approach is using ontologies---structured controlled vocabularies that summarize knowledge about different properties of anatomical entities, including developmental and structural dependencies. Here we assess whether the proximity of ontology-annotated characters within an ontology predicts evolutionary patterns. To do so, we measure phylogenetic information across characters and evaluate if it is hierarchically structured by ontological knowledge---in much the same way as phylogeny structures across-species diversity. We implement an approach to evaluate the Bayesian phylogenetic information (BPI) content and phylogenetic dissonance among ontology-annotated anatomical data subsets. We applied this to datasets representing two disparate animal groups: bees (Hexapoda: Hymenoptera: Apoidea, 209 chars) and characiform fishes (Actinopterygii: Ostariophysi: Characiformes, 463 chars). For bees, we find that BPI is not substantially structured by anatomy since dissonance is often high among morphologically related anatomical entities. For fishes, we find substantial information for two clusters of anatomical entities instantiating concepts from the jaws and branchial arch bones, but among-subset information decreases and dissonance increases substantially moving to higher-level subsets in the ontology. We further applied our approach to addressing particular evolutionary hypotheses with an example of morphological evolution in miniature fishes. While we show that ontology does indeed structure phylogenetic information, additional relationships and processes, such as convergence, likely play a substantial role in explaining BPI and dissonance, and merit future investigation. Our work demonstrates how complex morphological datasets can be interrogated with ontologies by allowing one to access how information is spread hierarchically across anatomical concepts, how congruent this information is, and what sorts of processes may structure it: phylogeny, development, or convergence.

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Section: Methodsmentioning

confidence: 99%

Assessing Bayesian Phylogenetic Information Content of Morphological Data Using Knowledge from Anatomy Ontologies

Porto

Dahdul

Lapp

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…The types of relations included as well as the ontology structure itself can influence the overall similarity values between concepts ( Pesquita et al 2009 ; Manda and Vision 2018 ). Another possibility is to use ontological knowledge to explicitly account for anatomical dependencies among individual traits when specifying models of character evolution ( Tarasov 2019 , 2020 ; Tarasov et al 2019 ). This can be achieved by constructing models of discrete trait evolution enabling ontology-aware transition matrices through structured Markov models equipped with hidden states ( Tarasov 2019 ).…”

Section: Methodsmentioning

confidence: 99%

Assessing Bayesian Phylogenetic Information Content of Morphological Data Using Knowledge From Anatomy Ontologies

et al. 2022

View full text Add to dashboard Cite

Morphology remains a primary source of phylogenetic information for many groups of organisms, and the only one for most fossil taxa. Organismal anatomy is not a collection of randomly assembled and independent ‘parts’, but instead a set of dependent and hierarchically nested entities resulting from ontogeny and phylogeny. How do we make sense of these dependent and at times redundant characters? One promising approach is using ontologies—structured controlled vocabularies that summarize knowledge about different properties of anatomical entities, including developmental and structural dependencies. Here we assess whether evolutionary patterns can explain the proximity of ontology-annotated characters within an ontology. To do so, we measure phylogenetic information across characters and evaluate if it matches the hierarchical structure given by ontological knowledge—in much the same way as across-species diversity structure is given by phylogeny. We implement an approach to evaluate the Bayesian phylogenetic information (BPI) content and phylogenetic dissonance among ontology-annotated anatomical data subsets. We applied this to datasets representing two disparate animal groups: bees (Hexapoda: Hymenoptera: Apoidea, 209 chars) and characiform fishes (Actinopterygii: Ostariophysi: Characiformes, 463 chars). For bees, we find that BPI is not substantially explained by anatomy since dissonance is often high among morphologically related anatomical entities. For fishes, we find substantial information for two clusters of anatomical entities instantiating concepts from the jaws and branchial arch bones, but among-subset information decreases and dissonance increases substantially moving to higher level subsets in the ontology. We further applied our approach to address particular evolutionary hypotheses with an example of morphological evolution in miniature fishes. While we show that phylogenetic information does match ontology structure for some anatomical entities, additional relationships and processes, such as convergence, likely play a substantial role in explaining BPI and dissonance, and merit future investigation. Our work demonstrates how complex morphological datasets can be interrogated with ontologies by allowing one to access how information is spread hierarchically across anatomical concepts, how congruent this information is, and what sorts of processes may play a role in explaining it: phylogeny, development, or convergence.

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“…Integrating hidden state information into phylogenetic analysis is an exciting new direction. It's also very challenging: in subsequent work, use of ontologies is proposed to assist in annotating characters (Tarasov et al 2019). Ontologies establish a shared, machine-readable syntax for discussing characters and representing relationships among characters.…”

Section: Ontogeny-aware Phylogenetic Modelsmentioning

confidence: 99%

A Systematist’s Guide to Estimating Bayesian Phylogenies From Morphological Data

Wright

2019

Insect Systematics and Diversity

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Phylogenetic trees are crucial to many aspects of taxonomic and comparative biology. Many researchers have adopted Bayesian methods to estimate their phylogenetic trees. In this family of methods, a model of morphological evolution is assumed to have generated the data observed by the researcher. These models make a variety of assumptions about the evolution of morphological characters, and these assumptions are translated into mathematics as parameters. The incorporation of prior distributions further allows researchers to quantify their prior beliefs about the value any one parameter can take. How to translate biological knowledge into mathematical language is confusing to many biologists. This review aims to help systematics researchers understand the biological meaning of common models and assumptions. Using examples from the insect fossil record, I will demonstrate empirically what assumptions mean in concrete terms, and discuss how researchers can use and understand Bayesian methods for phylogenetic estimation.

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PARAMO: A Pipeline for Reconstructing Ancestral Anatomies Using Ontologies and Stochastic Mapping

Cited by 19 publications

References 23 publications

Assessing Bayesian Phylogenetic Information Content of Morphological Data Using Knowledge from Anatomy Ontologies

Assessing Bayesian Phylogenetic Information Content of Morphological Data Using Knowledge from Anatomy Ontologies

Assessing Bayesian Phylogenetic Information Content of Morphological Data Using Knowledge From Anatomy Ontologies

A Systematist’s Guide to Estimating Bayesian Phylogenies From Morphological Data

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