Biology subjects

Vision, T.

Publications and source records attributed to Vision, T..

7 recordsLinked to original sources

bioRxiv2023

Evolutionary rescue under demographic and environmental stochasticity

Wild populations suffer two types of stochasticity: demographic stochasticity, from sampling error in offspring number, and environmental stochasticity, from temporal variation of the growth rate. By modeling evolution through phenotypic selection following an abrupt environmental change, we investigate how genetic and demographic dynamics, as well as effects of selection intensity after the environmental change and genetic variance on population survival, differ under demographic and environmental stochasticity. We find that the survival probability declines sharply with stronger selection under demographic stochasticity, but declines more continuously under environmental stochasticity. However, the genetic variance that confers the highest survival probability differs little under demographic and environmental stochasticity. Since the influence of demographic stochasticity is stronger when population size is smaller, a slow initial decline of genetic variance which allows quicker evolution and increase of fitness, is important for persistence. In contrast, the influence of environmental stochasticity is density-independent, so higher initial fitness becomes important for survival under strong environmental stochasticity. Combining both types of stochasticity shows that adding even weak environmental stochasticity can exaggerate the effects of different levels of demographic stochasticity on survival probabilities. Our work suggests the importance of explicitly distinguishing and measuring the forms of stochasticity for evolutionary rescue studies.

evolutionary biology↗

bioRxiv2023

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

O_LIOrganismal anatomy is a complex hierarchical system of interconnected anatomical entities often producing dependencies among multiple morphological characters. Ontologies provide a formalized and computable framework for representing and incorporating prior biological knowledge about anatomical dependencies in models of trait evolution. Further, ontologies offer new opportunities for assembling and working with semantic representations of morphological data. C_LIO_LIIn 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 also allows for assembling synthetic phylogenetic character matrices from semantic phenotypes of morphological data. We showcase the new package functionalities with three data sets from bees and fishes. C_LIO_LIWe demonstrate that ontology knowledge can be employed to automatically set up ontologyinformed evolutionary models that account for trait dependencies in the context of 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 of the original data set. C_LIO_LIOntologies will become an increasingly important tool not only for enabling prior anatomical knowledge to be integrated into phylogenetic methods but also to make morphological data FAIR compliant--a critical component of the ongoing phenomics revolution. Our new package offers key advancements toward this goal. C_LI

evolutionary biology↗

bioRxiv2022

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

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 address 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.

evolutionary biology↗

bioRxiv2019

A logical model of homology for comparative biology

There is a growing body of research on the evolution of anatomy in a wide variety of organisms. Discoveries in this field could be greatly accelerated by computational methods and resources that enable these findings to be compared across different studies and different organisms and linked with the genes responsible for anatomical modifications. Homology is a key concept in comparative anatomy; two important types are historical homology (the similarity of organisms due to common ancestry) and serial homology (the similarity of repeated structures within an organism). We explored how to most effectively represent historical and serial homology across anatomical structures to facilitate computational reasoning. We assembled a collection of homology assertions from the literature with a set of taxon phenotypes for vertebrate fins and limbs from the Phenoscape Knowledgebase (KB). Using six competency questions, we evaluated the reasoning ramifications of two logical models: the Reciprocal Existential Axioms Homology Model (REA) and the Ancestral Value Axioms Homology Model (AVA). Both models returned the user-expected results for all but one historical homology query and all serial homology queries. Additionally, for each competency question, the AVA model returns the search term and any subtypes. We identify some challenges of implementing complete homology queries due to limitations of OWL reasoning. This work lays the foundation for homology reasoning to be incorporated into other ontology-based tools, such as those that enable synthetic supermatrix construction and candidate gene discovery.

evolutionary biology↗

bioRxiv2019

Do Synthesis Centers Synthesize? A Semantic Analysis of Diversity and Performance

Synthesis centers are a recently-developed form of scientific organization that catalyzes and supports a form of interdisciplinary research that integrates diverse theories, methods and data across spatial or temporal scales, scientific phenomena, and forms of expertise to increase the generality, parsimony, applicability, or empirical soundness of scientific explanations. Research has shown the synthesis working group to be a distinctive form of scientific collaboration that reliably produces consequential, high-impact publications, but no one has asked: do synthesis working groups produce publications that are substantially more diverse than those produced outside of synthesis centers, and if so, how and with what effects? We have investigated these questions through a novel textual analysis. We found that if diversity is measured solely by mean difference in the Rao-Stirling (aggregate) measure of diversity, then the answer is no. But synthesis center papers have significantly greater variety and balance, but significantly lower disparity, than papers in the reference corpus. Synthesis center influence is mediated by the greater size of synthesis center collaborations (numbers of authors, distinct institutions, and references) but even when taking size into account, there is a persistent direct effect: synthesis center papers have significantly greater variety and balance, but less disparity, than papers in the reference corpus. We conclude by inviting further exploration of what this novel textual analysis approach might reveal about interdisciplinary research and by offering some practical implications of our results.

scientific communication and education↗

bioRxiv2018

An analysis and comparison of the statistical sensitivity of semantic similarity metrics

1Semantic similarity has been used for comparing genes, proteins, phenotypes, diseases, etc. for various biological applications. The rise of ontology-based data representation in biology has also led to the development of several semantic similarity metrics that use different statistics to estimate similarity.\n\nAlthough semantic similarity has become a crucial computational tool in several applications, there has not been a formal evaluation of the statistical sensitivity of these metrics and their ability to recognize similarity between distantly related biological objects.\n\nHere, we present a statistical sensitivity comparison of five semantic similarity metrics (Jaccard, Resnik, Lin, Jiang& Conrath, and Hybrid Relative Specificity Similarity) representing three different kinds of metrics (Edge based, Node based, and Hybrid) and explore key parameter choices that can impact sensitivity. Furthermore, we compare four methods of aggregating individual annotation similarities to estimate similarity between two biological objects - All Pairs, Best Pairs, Best Pairs Symmetric, and Groupwise.\n\nTo evaluate sensitivity in a controlled fashion, we explore two different models for simulating data with varying levels of similarity and compare to the noise distribution using resampling. Source data are derived from the Phenoscape Knowledgebase of evolutionary phenotypes.\n\nOur results indicate that the choice of similarity metric along with different parameter choices can substantially affect sensitivity. Among the five metrics evaluated, we find that Resnik similarity shows the greatest sensitivity to weak semantic similarity. Among the ways to combine pairwise statistics, the Groupwise approach provides the greatest discrimination among values above the sensitivity threshold, while the Best Pairs statistic can be parametrically tuned to provide the highest sensitivity.\n\nOur findings serve as a guideline for an appropriate choice and parameterization of semantic similarity metrics, and point to the need for improved reporting of the statistical significance of semantic similarity matches in cases where weak similarity is of interest

bioinformatics↗

bioRxiv2018

Annotation of phenotypes using ontologies: a Gold Standard for the training and evaluation of natural language processing systems

Natural language descriptions of organismal phenotypes - a principal object of study in biology, are abundant in biological literature. Expressing these phenotypes as logical statements using formal ontologies would enable large-scale analysis on phenotypic information from diverse systems. However, considerable human effort is required to make the semantics of phenotype descriptions amenable to machine reasoning by (a) recognizing appropriate on-tological terms for entities in text and (b) stringing these terms into logical statements. Most existing Natural Language Processing tools stop at entity recognition, leaving a need for tools that can assist with both aspects of the task. The recently described Semantic CharaParser aims to meet this need. We describe the first expert-curated Gold Standard corpus for ontology-based annotation of phenotypes from the systematics literature. We use it to evaluate Semantic CharaParsers annotations and explore differences in performance between humans and machine. We use four annotation accuracy metrics that can account for both semantically identical and similar matches. We found that machine-human consistency was significantly lower than inter-curator (human-human) consistency. Surprisingly, allowing curators access to external information that was not available to Semantic CharaParser did not significantly increase the similarity of their annotations to the Gold Standard nor have a significant effect on inter-curator consistency. We found that the similarity of machine annotations to the Gold Standard increased after new ontology terms relevant to the input text had been added. Evaluation by the original authors of the character descriptions indicated that the Gold Standard annotations came closer to representing their intended meaning than did either the curator or machine annotations. These findings point toward ways to better design of software to augment human curators, and the Gold Standard corpus will allow training and assessment of new tools to improve phenotype annotation accuracy at scale.

evolutionary biology↗