Biology subjects

Balk, M. A.

Publications and source records attributed to Balk, M. A..

3 recordsLinked to original sources

bioRxiv2024

The Unified Phenotype Ontology (uPheno): A framework for cross-species integrative phenomics

Phenotypic data are critical for understanding biological mechanisms and consequences of genomic variation, and are pivotal for clinical use cases such as disease diagnostics and treatment development. For over a century, vast quantities of phenotype data have been collected in many different contexts covering a variety of organisms. The emerging field of phenomics focuses on integrating and interpreting these data to inform biological hypotheses. A major impediment in phenomics is the wide range of distinct and disconnected approaches to recording the observable characteristics of an organism. Phenotype data are collected and curated using free text, single terms or combinations of terms, using multiple vocabularies, terminologies, or ontologies. Integrating these heterogeneous and often siloed data enables the application of biological knowledge both within and across species. Existing integration efforts are typically limited to mappings between pairs of terminologies; a generic knowledge representation that captures the full range of cross-species phenomics data is much needed. We have developed the Unified Phenotype Ontology (uPheno) framework, a community effort to provide an integration layer over domain-specific phenotype ontologies, as a single, unified, logical representation. uPheno comprises (1) a system for consistent computational definition of phenotype terms using ontology design patterns, maintained as a community library; (2) a hierarchical vocabulary of species-neutral phenotype terms under which their species-specific counterparts are grouped; and (3) mapping tables between species-specific ontologies. This harmonized representation supports use cases such as cross-species integration of genotype-phenotype associations from different organisms and cross-species informed variant prioritization.

bioinformatics↗

bioRxiv2023

The Ontology of Biological Attributes (OBA) - Computational Traits for the Life Sciences

Existing phenotype ontologies were originally developed to represent phenotypes that manifest as a character state in relation to a wild-type or other reference. However, these do not include the phenotypic trait or attribute categories required for the annotation of genome-wide association studies (GWAS), Quantitative Trait Loci (QTL) mappings or any population-focused measurable trait data. Moreover, variations in gene expression in response to environmental disturbances even without any genetic alterations can also be associated with particular biological attributes. The integration of trait and biological attribute information with an ever increasing body of chemical, environmental and biological data greatly facilitates computational analyses and it is also highly relevant to biomedical and clinical applications. The Ontology of Biological Attributes (OBA) is a formalised, species-independent collection of interoperable phenotypic trait categories that is intended to fulfil a data integration role. OBA is a standardised representational framework for observable attributes that are characteristics of biological entities, organisms, or parts of organisms. OBA has a modular design which provides several benefits for users and data integrators, including an automated and meaningful classification of trait terms computed on the basis of logical inferences drawn from domain-specific ontologies for cells, anatomical and other relevant entities. The logical axioms in OBA also provide a previously missing bridge that can computationally link Mendelian phenotypes with GWAS and quantitative traits. The term components in OBA provide semantic links and enable knowledge and data integration across specialised research community boundaries, thereby breaking silos.

bioinformatics↗

bioRxiv2018

A constraint-based model reveals hysteresis in island biogeography

AimWe present a Constraint-based Model of Dynamic Island Biogeography (C-DIB) that predicts how species functional traits interact with dynamic environments to determine the candidate species available for local community assembly on real and habitat islands through time.\n\nLocationReal and habitat islands globally.\n\nMethodsWe develop the C-DIB model concept, synthesize the relevant literature, and present a toolkit for evaluating model predictions for a wide variety of \"island\" systems and taxa.\n\nResultsThe C-DIB model reveals that as islands cycle between phases of increasing or decreasing size and connectivity to a source pool, the dominant process driving species presence or absence switches between colonization and extinction. Both processes are mediated by interactions between organismal traits and environmental constraints. Colonization probability is predicted by a species ability to cross the intervening matrix between a population source and the island; population persistence (or extinction) is predicted by the minimum spatial requirements to sustain an isolated population. The non-random distributions of mammals on islands of the Sunda Shelf and Great Basin \"sky islands\" provide example study systems for evaluating the C-DIB model.\n\nMain conclusionsBecause different suites of traits impose constraints on the processes of colonization and extinction, similar environmental conditions can host different candidate species despite the same predicted richness. Thus, the model exemplifies the specific yet underappreciated role of hysteresis -the dependency of outcomes not only on the current system state -but also the historical contingency of environmental change in affecting populations and communities in insular systems.

ecology↗