Dataset Information

PhenoDigm: analyzing curated annotations to associate animal models with human diseases.

ABSTRACT: The ultimate goal of studying model organisms is to translate what is learned into useful knowledge about normal human biology and disease to facilitate treatment and early screening for diseases. Recent advances in genomic technologies allow for rapid generation of models with a range of targeted genotypes as well as their characterization by high-throughput phenotyping. As an abundance of phenotype data become available, only systematic analysis will facilitate valid conclusions to be drawn from these data and transferred to human diseases. Owing to the volume of data, automated methods are preferable, allowing for a reliable analysis of the data and providing evidence about possible gene-disease associations. Here, we propose Phenotype comparisons for DIsease Genes and Models (PhenoDigm), as an automated method to provide evidence about gene-disease associations by analysing phenotype information. PhenoDigm integrates data from a variety of model organisms and, at the same time, uses several intermediate scoring methods to identify only strongly data-supported gene candidates for human genetic diseases. We show results of an automated evaluation as well as selected manually assessed examples that support the validity of PhenoDigm. Furthermore, we provide guidance on how to browse the data with PhenoDigm's web interface and illustrate its usefulness in supporting research. Database URL: http://www.sanger.ac.uk/resources/databases/phenodigm

SUBMITTER: Smedley D

PROVIDER: S-EPMC3649640 | biostudies-literature | 2013

REPOSITORIES: biostudies-literature

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PhenoDigm: analyzing curated annotations to associate animal models with human diseases.

Smedley Damian D Oellrich Anika A Köhler Sebastian S Ruef Barbara B Westerfield Monte M Robinson Peter P Lewis Suzanna S Mungall Christopher C

Database : the journal of biological databases and curation 20130509

The ultimate goal of studying model organisms is to translate what is learned into useful knowledge about normal human biology and disease to facilitate treatment and early screening for diseases. Recent advances in genomic technologies allow for rapid generation of models with a range of targeted genotypes as well as their characterization by high-throughput phenotyping. As an abundance of phenotype data become available, only systematic analysis will facilitate valid conclusions to be drawn fr ...[more]

PMID: 23660285

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Project description:Scientists and clinicians who study genetic alterations and disease have traditionally described phenotypes in natural language. The considerable variation in these free-text descriptions has posed a hindrance to the important task of identifying candidate genes and models for human diseases and indicates the need for a computationally tractable method to mine data resources for mutant phenotypes. In this study, we tested the hypothesis that ontological annotation of disease phenotypes will facilitate the discovery of new genotype-phenotype relationships within and across species. To describe phenotypes using ontologies, we used an Entity-Quality (EQ) methodology, wherein the affected entity (E) and how it is affected (Q) are recorded using terms from a variety of ontologies. Using this EQ method, we annotated the phenotypes of 11 gene-linked human diseases described in Online Mendelian Inheritance in Man (OMIM). These human annotations were loaded into our Ontology-Based Database (OBD) along with other ontology-based phenotype descriptions of mutants from various model organism databases. Phenotypes recorded with this EQ method can be computationally compared based on the hierarchy of terms in the ontologies and the frequency of annotation. We utilized four similarity metrics to compare phenotypes and developed an ontology of homologous and analogous anatomical structures to compare phenotypes between species. Using these tools, we demonstrate that we can identify, through the similarity of the recorded phenotypes, other alleles of the same gene, other members of a signaling pathway, and orthologous genes and pathway members across species. We conclude that EQ-based annotation of phenotypes, in conjunction with a cross-species ontology, and a variety of similarity metrics can identify biologically meaningful similarities between genes by comparing phenotypes alone. This annotation and search method provides a novel and efficient means to identify gene candidates and animal models of human disease, which may shorten the lengthy path to identification and understanding of the genetic basis of human disease.

Project description:As sequencing technology improves, the identification of new disease-associated genes and new alleles of known genes is rapidly increasing our understanding of the genetic underpinnings of rare diseases, including neuromuscular diseases. However, precisely because these disorders are rare and often heterogeneous, they are difficult to study in patient populations. In parallel, our ability to engineer the genomes of model organisms, such as mice or rats, has gotten increasingly efficient through techniques such as CRISPR/Cas9 genome editing, allowing the creation of precision human disease models. Such in vivo model systems provide an efficient means for exploring disease mechanisms and identifying therapeutic strategies. Furthermore, animal models provide a platform for preclinical studies to test the efficacy of those strategies. Determining whether the same mechanisms are involved in the human disease and confirming relevant parameters for treatment ideally involves a human experimental system. One system currently being used is induced pluripotent stem cells (iPSCs), which can then be differentiated into the relevant cell type(s) for in vitro confirmation of disease mechanisms and variables such as target engagement. Here we provide a demonstration of these approaches using the example of tRNA-synthetase-associated inherited peripheral neuropathies, rare forms of Charcot-Marie-Tooth disease (CMT). Mouse models have led to a better understanding of both the genetic and cellular mechanisms underlying the disease. To determine if the mechanisms are similar in human cells, we will use genetically engineered iPSC-based models. This will allow comparisons of different CMT-associated GARS alleles in the same genetic background, reducing the variability found between patient samples and simplifying the availability of cell-based models for a rare disease. The necessity of integrating mouse and human models, strategies for accomplishing this integration, and the challenges of doing it at scale are discussed using recently published work detailing the cellular mechanisms underlying GARS-associated CMT as a framework.

Dataset Information

PhenoDigm: analyzing curated annotations to associate animal models with human diseases.

Publications

PhenoDigm: analyzing curated annotations to associate animal models with human diseases.

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