Toward synthesizing our knowledge of morphology: using ontologies and machine reasoning to extract presence/absence evolutionary phenotypes across studies

Dececchi, T. Alexander; Balhoff, James P.; Lapp, Hilmar; Mabee, Paula M.

doi:10.7287/peerj.preprints.807v1

Cited by 3 publications

(3 citation statements)

References 13 publications

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“…The incompleteness of large data sets stymies comparative research. However, relationships formalized through ontologies can be used to populate sparse character matrices (Dececchi et al, 2015). Jackson et al (2018) investigated fin evolution across 12,500+ teleost fishes, but many taxa were not coded for some traits.…”

Section: Ontologies and Phylogeniesmentioning

confidence: 99%

1, 2, 3, GO! Venture beyond gene ontologies in plant evolutionary research

Howard

Tribble

Martínez‐Gómez

et al. 2021

American J of Botany

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Section: Ontologies and Phylogeniesmentioning

confidence: 99%

1, 2, 3, GO! Venture beyond gene ontologies in plant evolutionary research

Howard

Tribble

Martínez‐Gómez

et al. 2021

American J of Botany

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“…Triples can be reasoned and inferred over, and knowledge from the ontologies can be used to create new triples based on existing triples (e.g. Dececchi et al, 2015). This analytical potential is relevant in the numerical tree inference procedure introduced next.…”

Section: Semantic Instance Anatomiesmentioning

confidence: 99%

Towards a semantic approach to numerical tree inference in phylogenetics

Vogt

2017

Cladistics

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Conventional approaches to phylogeny reconstruction require a character analysis step prior to and methodologically separated from a numerical tree inference step. The former results in a character matrix that contains the empirical data analysed in the latter. This separation of steps involves various methodological and conceptual problems (e.g. homology assessment independent of tree inference and character optimization, character dependencies, discounting of alternative homology hypotheses). In morphology, the character analysis step covers the stages of morphological comparative studies, homology assessment and the identification and coding of morphological characters. Unfortunately, only the last stage requires some formalism, whereas the preceding stages are commonly regarded to be pre‐rational and intuitive, which is why their reproducibility and analytical accessibility is limited. Here, I introduce a rational for a semantic approach to numerical tree inference that uses sets of semantic instance anatomies as data source instead of character matrices, thereby avoiding the above‐mentioned problems. A semantic instance anatomy is an ontology‐based description of the anatomical organization of a specimen in the form of a semantic graph. The semantic approach to numerical tree inference combines and integrates the steps of character analysis and numerical tree inference and makes both analytically accessible and communicable. Before outlining first steps for a research programme dedicated to the semantic approach to numerical tree inference, I discuss in detail the methodological, conceptual, and computational challenges and requirements that first have to be dealt with before adequate algorithms can be developed.

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“…An ontology is a computer-based representation of concepts and their logical relationships for a specific domain of knowledge (Deans et al, 2012(Deans et al, , 2015Balhoff et al, 2013). Ontological representation facilitates the conversion of NL into machine-parsable statements, thereby, providing new opportunities for computer-aided comparative phenomics and trait analysis (Deans et al, 2015;Dececchi et al, 2015;Burleigh et al, 2013;Tarasov, 2019).…”

Section: Introductionmentioning

confidence: 99%

ontoFAST: An R package for interactive and semi-automatic annotation of characters with biological ontologies

Tarasov

Mikó

Yoder

2021

Preprint

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The commonly used Entity-Quality (EQ) syntax provides rich semantics and high granularity for annotating phenotypes and characters using ontologies. However, EQ syntax might be time inefficient if this granularity is unnecessary for downstream analysis. We present an R package ontoFAST that aid production of fast annotations of characters and character matrices with biological ontologies. Its interactive interface allows quick and convenient tagging of character statements with necessary ontology terms. The annotations produced in ontoFAST can be exported in csv format for downstream analysis. Additinally, OntoFAST provides: (i) functions for constructing simple queries of characters against ontologies, and (ii) helper function for exporting and visualising complex ontological hierarchies and their relationships. OntoFAST enhances data interoperability between various applications and support further integration of ontological and phylogenetic methods. Ontology tools are underrepresented in R environment and we hope that ontoFAST will stimulate their further development.

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Toward synthesizing our knowledge of morphology: using ontologies and machine reasoning to extract presence/absence evolutionary phenotypes across studies

Abstract: 15The reality of larger and larger molecular databases and the need to integrate 16 data scalably have presented a major challenge for the use of phenotypic data.

Cited by 3 publications

References 13 publications

1, 2, 3, GO! Venture beyond gene ontologies in plant evolutionary research

1, 2, 3, GO! Venture beyond gene ontologies in plant evolutionary research

Towards a semantic approach to numerical tree inference in phylogenetics

ontoFAST: An R package for interactive and semi-automatic annotation of characters with biological ontologies

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