Mouse model phenotypes provide information about human drug targets

Motivation: Methods for computational drug target identification use information from diverse information sources to predict or prioritize drug targets for known drugs. One set of resources that has been relatively neglected for drug repurposing is animal model phenotype. Results: We investigate the use of mouse model phenotypes for drug target identification. To achieve this goal, we first integrate mouse model phenotypes and drug effects, and then systematically compare the phenotypic similarity between mouse models and drug effect profiles. We find a high similarity between phenotypes resulting from loss-of-function mutations and drug effects resulting from the inhibition of a protein through a drug action, and demonstrate how this approach can be used to suggest candidate drug targets. Availability and implementation: Analysis code and supplementary data files are available on the project Web site at https://drugeffects.googlecode.com. Contact: [email protected] or [email protected] Supplementary information: Supplementary data are available at Bioinformatics online

Analysis of the human diseasome reveals phenotype modules across common, genetic, and infectious diseases

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 8,000 diseases. We demonstrate that our method generates phenotypes that 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 share signs and symptoms cluster together, and we use this network to identify phenotypic disease modules

PathoPhenoDB, linking human pathogens to their phenotypes in support of infectious disease research.

Understanding the relationship between the pathophysiology of infectious disease, the biology of the causative agent and the development of therapeutic and diagnostic approaches is dependent on the synthesis of a wide range of types of information. Provision of a comprehensive and integrated disease phenotype knowledgebase has the potential to provide novel and orthogonal sources of information for the understanding of infectious agent pathogenesis, and support for research on disease mechanisms. We have developed PathoPhenoDB, a database containing pathogen-to-phenotype associations. PathoPhenoDB relies on manual curation of pathogen-disease relations, on ontology-based text mining as well as manual curation to associate host disease phenotypes with infectious agents. Using Semantic Web technologies, PathoPhenoDB also links to knowledge about drug resistance mechanisms and drugs used in the treatment of infectious diseases. PathoPhenoDB is accessible at http://patho.phenomebrowser.net/ , and the data are freely available through a public SPARQL endpoint

Advancing discovery science with fair data stewardship:Findable, accessible, interoperable, reusable

This report summarizes a presentation by Dr. Michel Dumontier. It reviews innovative scientific research methods created by data science, and the need to develop infrastructure, methodologies, and user communities to advance data science. Stakeholders have proposed a set of principles to make digital resources findable, accessible, interoperable, and reusable—FAIR. FAIR principles provide guidelines, do not require specific technologies, and allow communities of stakeholders to define specific FAIR standards and develop metrics to quantify them. Libraries can be part of the new data ecosystemby providing education, data stewardship, and infrastructure

Living Long and Well: Prospects for a Personalized Approach to the Medicine of Ageing.

Research into ageing and its underlying molecular basis enables us to develop and implement targeted interventions to ameliorate or cure its consequences. However, the efficacy of interventions often differs widely between individuals, suggesting that populations should be stratified or even individualized. Large-scale cohort studies in humans, similar systematic 56 studies in model organisms, and detailed investigations into the biology of ageing can provide individual validated biomarkers and mechanisms, leading to recommendations for targeted interventions. Human cohort studies are already ongoing, and can be supplemented by in silico simulations. Systematic studies in animal models are made possible by the use of inbred strains, or genetic reference populations of mice. Combining both, the comprehensive picture of the various determinants of ageing and healthspan can be studied in detail, and an appreciation of the relevance of results from model organisms to humans emerges. The interactions between genotype and environment, particularly the psychosocial environment, are poorly studied in both humans and model organisms, presenting serious challenges to any approach to a personalized medicine of ageing. To increase success of preventive interventions, we argue that there is a pressing need for an individualized evaluation of interventions such as physical exercise, nutrition, nutraceuticals and calorie restriction mimetics as well as psychosocial and environmental factors, separately and in combination. The expected extension of healthspan enables us to refocus healthcare spending on individual prevention starting in late adulthood, and on the brief period of morbidity at very old age

Integrating phenotype ontologies with PhenomeNET

Abstract. PhenomeNET is a system for disease gene prioritization that includes as one of its components an ontology designed to integrate phenotype ontologies. While not applicable to matching arbitrary ontologies, PhenomeNET can be used to identify related phenotypes in different species, including human, mouse, zebrafish, nematode worm, fruit fly, and yeast. Here, we apply the PhenomeNET to identify related classes from four phenotype and disease ontologies using automated reasoning. We demonstrate that we can identify a large number of mappings, some of which require automated reasoning and cannot easily be identified through lexical approaches alone