The Invariant Nature of a Morphological Character and Character State: Insights from Gene Regulatory Networks

Tarasov, Sergei

doi:10.1093/sysbio/syz050

Cited by 8 publications

(12 citation statements)

References 55 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

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“…At the core of our method lies the property of character and character state invariance that exists in Markov models of discrete trait evolution (Tarasov, 2018, 2019). This property removes the distinction between character and character state, meaning that multiple individual characters can be represented as a single character and vice versa, which makes the two concepts equivalent.…”

Section: Methodological Backgroundmentioning

confidence: 99%

PARAMO pipeline: reconstructing ancestral anatomies using ontologies and stochastic mapping

Tarasov

Mikó

Yoder

et al. 2019

Preprint

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15Comparative phylogenetics has been largely lacking a method for reconstructing the evolution of phenotypic entities that consist of ensembles of multiple discrete traits -entire organismal anatomies or organismal body regions. In this study, we provide a new approach named PARAMO (Phylogenetic Ancestral Reconstruction of Anatomy by Mapping Ontologies) that appropriately models anatomical dependencies and uses ontology-informed amalgamation of stochastic maps to reconstruct phenotypic evolution at different levels of anatomical hierarchy including entire phenotypes. This approach provides new opportunities for tracking phenotypic radiations and evolution of organismal anatomies. 16 17 18 19 20 21 22 31Peters et al. (2014)]. Nevertheless, even these studies still employ individual character approaches, which 32 remains the predominant paradigm in comparative phylogenetics due to a lack of methods for modeling an 33 entire phenotype (or its parts) as a single complex character. These limitations thereby prevent researchers 34 from reconstructing entire organismal anatomies. To our knowledge, the only approach that attempts to 35 overcome this problem is the parsimony-based method of Ramírez and Michalik (2014). 36In this paper, we propose a new pipeline called PARAMO (Phylogenetic Ancestral Reconstruction of 37 Anatomy by Mapping Ontologies) that takes into account anatomical dependencies and uses stochastic 38 mapping (Huelsenbeck et al., 2003) along with anatomy ontologies to reconstruct the evolution of entire 39 organismal anatomies; this pipeline can be implemented in likelihood or Bayesian frameworks. Our 40 approach treats the entire phenotype or its component body regions as single complex characters and 41 allows exploring and comparing phenotypic evolution at different levels of anatomical hierarchy. These 42 complex characters are constructed by ontology-informed amalgamation of elementary characters (i.e., 43 those coded in character matrix) using stochastic maps. In our approach, characters are linked with the 44 terms from an anatomy ontology, which allows viewing them not just as an ensemble of character state 45 tokens but as entities that have their own biological meaning provided by the ontology. 46The goal of this paper is to give the description of PARAMO pipeline and R (R Core Team, 2018) 47 scripts that can be used to run it. Additionally, we use a Hymenopteran dataset to demonstrate the 48 workflow of the pipeline. At the end of the paper, we discuss biological questions that can be addressed 49 using our method. We believe that reconstructing evolutionary dynamics of entire phenotypes and their 50 major parts opens up new perspectives for comparative morphology and phylogenetics, which, in turn, 51 allows tracking phenotypic radiations across time and phylogeny. 52 METHODOLOGICAL BACKGROUND 53The core ingredient: character and character state invariance 54 At the core of our method lies the property of character and character state invariance that exists in 55 Markov models of discrete ...

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“…Correlated CTMC evolution.-When dealing with a set of evolving CTMCs, it is sometimes convenient to consider their evolution as a joint process instead of a set of individual processes (Tarasov, 2020). Both such representations are equivalent.…”

Section: (Rwr)mentioning

confidence: 99%

Nonidentifiability of state-dependent diversification models (SSEs) is ubiquitous but not problematic for phylogenetics

Tarasov

Uyeda

2022

Preprint

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This paper investigates model congruence (= asymptotic unidentifiability) in phylogenetics for continuous-time Markov chains (CTMCs) that include models for DNA, protein, discrete trait evolution, and state-dependent diversification. Without exception, all CTMCs have infinite classes of congruent models. Congruent models vary in the number of parameters from one to infinity and may have drastically different evolutionary dynamics, and standard model selection criteria are not applicable to them. We classify the structure of a congruence class and show how the “best” model can be selected based on mathematical and biological reasoning. Thus, congruent models in CTMCs do not suffer from the model selection problem previously identified for the time-dependent diversification process. Moreover, we demonstrate that congruence may serve to explain some evolutionary phenomena, specifically linking macro-and microevolution. We also discuss other types of congruence that may occur in phylogenetics and ways to handle them.

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The Invariant Nature of a Morphological Character and Character State: Insights from Gene Regulatory Networks

Cited by 8 publications

References 55 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

PARAMO pipeline: reconstructing ancestral anatomies using ontologies and stochastic mapping

Nonidentifiability of state-dependent diversification models (SSEs) is ubiquitous but not problematic for phylogenetics

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