Ontologies are widely used in biological and biomedical research. Their success lies in their combination of four main features present in almost all ontologies: provision of standard identifiers for ...classes and relations that represent the phenomena within a domain; provision of a vocabulary for a domain; provision of metadata that describes the intended meaning of the classes and relations in ontologies; and the provision of machine-readable axioms and definitions that enable computational access to some aspects of the meaning of classes and relations. While each of these features enables applications that facilitate data integration, data access and analysis, a great potential lies in the possibility of combining these four features to support integrative analysis and interpretation of multimodal data. Here, we provide a functional perspective on ontologies in biology and biomedicine, focusing on what ontologies can do and describing how they can be used in support of integrative research. We also outline perspectives for using ontologies in data-driven science, in particular their application in structured data mining and machine learning applications.
Purpose: In the late 1990s, it had become clear that the long-standing paradigm for the action of radiation on living cells and organisms did not have sufficient power to explain the observed effects ...of low dose ionizing radiation. The purpose of this commentary is to examine the experiments that lead up to the modification of the classic paradigm consequent on these observations, their historical precedents, and the development of our understanding of the role of epigenetics in low dose radiation effects.
Results and conclusions: We discuss how parallel advances in epigenetics from developmental biology and cancer studies, and the discovery of epigenetic modifications of chromatin, such as DNA methylation, impacted on the development of an epigenetic paradigm for low dose effects. We also assess the impact of technology development in supporting the paradigm shift. We then examine recent accumulated data on epigenetic modification in response to irradiation since that shift took place, and identify areas where bringing together data from developmental biology and cancer might answer some of the paradoxes and contradictions in this data. We predict that further paradigm shifts are imminent.
Guidelines for primary prevention of cardiovascular disease (CVD) with statins, including the most recent, fail to make the best use of the evidence from clinical trials by concentrating on absolute ...CVD risk as a statin indication and not also considering that a major determinant of therapeutic benefit is the magnitude of the low-density lipoprotein (LDL) (or non-HDL) cholesterol reduction achieved. This decrease is proportional to the pretreatment concentration. We set out to apply this knowledge to the calculation of the number needed to treat to prevent one event (NNT) and to assess critically how current guidelines performed at different degrees of CVD risk across a range of LDL (or non-HDL) cholesterol concentrations.
Number needed to treat to prevent one event revealed exclusion from the treatment of some people with higher cholesterol levels, who may benefit more than others needlessly exposed to statins with no realistic prospect of benefit. Furthermore, abandonment of cholesterol therapeutic goals disadvantaged people with higher levels.
These problems can be overcome by basing the decision to treat on the NNT calculated both from absolute CVD risk and also on the LDL (or non-HDL) cholesterol reduction achievable with statin treatment. This need not adds an additional layer of complexity for the clinician, because computer programmes already used to estimate CVD risk could be easily amended, thus permitting more effective deployment of statins in the population.
Phenotypes are investigated in model organisms to understand and reveal the molecular mechanisms underlying disease. Phenotype ontologies were developed to capture and compare phenotypes within the ...context of a single species. Recently, these ontologies were augmented with formal class definitions that may be utilized to integrate phenotypic data and enable the direct comparison of phenotypes between different species. We have developed a method to transform phenotype ontologies into a formal representation, combine phenotype ontologies with anatomy ontologies, and apply a measure of semantic similarity to construct the PhenomeNET cross-species phenotype network. We demonstrate that PhenomeNET can identify orthologous genes, genes involved in the same pathway and gene-disease associations through the comparison of mutant phenotypes. We provide evidence that the Adam19 and Fgf15 genes in mice are involved in the tetralogy of Fallot, and, using zebrafish phenotypes, propose the hypothesis that the mammalian homologs of Cx36.7 and Nkx2.5 lie in a pathway controlling cardiac morphogenesis and electrical conductivity which, when defective, cause the tetralogy of Fallot phenotype. Our method implements a whole-phenome approach toward disease gene discovery and can be applied to prioritize genes for rare and orphan diseases for which the molecular basis is unknown.
Prioritization of variants in personal genomic data is a major challenge. Recently, computational methods that rely on comparing phenotype similarity have shown to be useful to identify causative ...variants. In these methods, pathogenicity prediction is combined with a semantic similarity measure to prioritize not only variants that are likely to be dysfunctional but those that are likely involved in the pathogenesis of a patient's phenotype.
We have developed DeepPVP, a variant prioritization method that combined automated inference with deep neural networks to identify the likely causative variants in whole exome or whole genome sequence data. We demonstrate that DeepPVP performs significantly better than existing methods, including phenotype-based methods that use similar features. DeepPVP is freely available at https://github.com/bio-ontology-research-group/phenomenet-vp .
DeepPVP further improves on existing variant prioritization methods both in terms of speed as well as accuracy.
Antioxidant properties of HDL Soran, Handrean; Schofield, Jonathan D; Durrington, Paul N
Frontiers in pharmacology,
10/2015, Letnik:
6
Journal Article
Recenzirano
Odprti dostop
High-density lipoprotein (HDL) provides a pathway for the passage of lipid peroxides and lysophospholipids to the liver via hepatic scavenger receptors. Perhaps more importantly, HDL actually ...metabolizes lipid hydroperoxides preventing their accumulation on low-density lipoprotein (LDL), thus impeding its atherogenic structural modification. A number of candidates have been suggested to be responsible for HDL's antioxidant function, with paraoxonase-1 (PON1) perhaps the most prominent. Here we review the evidence for HDL anti-oxidative function and the potential contributions of apolipoproteins, lipid transfer proteins, paraoxonases and other enzymes associated with HDL.
Phenotypes are the observable characteristics of an organism arising from its response to the environment. Phenotypes associated with engineered and natural genetic variation are widely recorded ...using phenotype ontologies in model organisms, as are signs and symptoms of human Mendelian diseases in databases such as OMIM and Orphanet. Exploiting these resources, several computational methods have been developed for integration and analysis of phenotype data to identify the genetic etiology of diseases or suggest plausible interventions. A similar resource would be highly useful not only for rare and Mendelian diseases, but also for common, complex and infectious diseases. We apply a semantic text-mining approach to identify the phenotypes (signs and symptoms) associated with over 6,000 diseases. We evaluate our text-mined phenotypes by demonstrating that they can correctly identify known disease-associated genes in mice and humans with high accuracy. Using a phenotypic similarity measure, we generate a human disease network in which diseases that have similar signs and symptoms cluster together, and we use this network to identify closely related diseases based on common etiological, anatomical as well as physiological underpinnings.
Abstract
The past decade has seen an explosion in the collection of genotype data in domains as diverse as medicine, ecology, livestock and plant breeding. Along with this comes the challenge of ...dealing with the related phenotype data, which is not only large but also highly multidimensional. Computational analysis of phenotypes has therefore become critical for our ability to understand the biological meaning of genomic data in the biological sciences. At the heart of computational phenotype analysis are the phenotype ontologies. A large number of these ontologies have been developed across many domains, and we are now at a point where the knowledge captured in the structure of these ontologies can be used for the integration and analysis of large interrelated data sets. The Phenotype And Trait Ontology framework provides a method for formal definitions of phenotypes and associated data sets and has proved to be key to our ability to develop methods for the integration and analysis of phenotype data. Here, we describe the development and products of the ontological approach to phenotype capture, the formal content of phenotype ontologies and how their content can be used computationally.
The impact of the laboratory environment on animal models of human disease, particularly the mouse, has recently come under intense scrutiny regarding both the reproducibility of such environments ...and their ability to accurately recapitulate elements of human environmental conditions. One common objection to the use of mice in highly controlled facilities is that humans live in much more diverse and stressful environments, which affects the expression and characteristics of disease phenotypes. In this Special Article, we review some of the known effects of the laboratory environment on mouse phenotypes and compare them with environmental effects on humans that modify phenotypes or, in some cases, have driven genetic adaptation. We conclude that the 'boxes' inhabited by mice and humans have much in common, but that, when attempting to tease out the effects of environment on phenotype, a controlled and, importantly, well-characterized environment is essential.
Many ontologies have been developed in biology and these ontologies increasingly contain large volumes of formalized knowledge commonly expressed in the Web Ontology Language (OWL). Computational ...access to the knowledge contained within these ontologies relies on the use of automated reasoning.
We have developed the Aber-OWL infrastructure that provides reasoning services for bio-ontologies. Aber-OWL consists of an ontology repository, a set of web services and web interfaces that enable ontology-based semantic access to biological data and literature. Aber-OWL is freely available at http://aber-owl.net .
Aber-OWL provides a framework for automatically accessing information that is annotated with ontologies or contains terms used to label classes in ontologies. When using Aber-OWL, access to ontologies and data annotated with them is not merely based on class names or identifiers but rather on the knowledge the ontologies contain and the inferences that can be drawn from it.
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