Bioinformatics Analysis of Phenotypic Data of ASD Rodent Models

Friday, May 13, 2016: 11:30 AM-1:30 PM
Hall A (Baltimore Convention Center)
I. Das, M. A. Estevez and S. B. Basu, MindSpec, Inc., McLean, VA
Background: The Autism Database (AutDB) is a publicly available, manually annotated, modular database that serves as an ongoing collection of genes linked to Autism Spectrum Disorders (ASD). The animal model module of AutDB catalogues over 600 ASD-related rodent models, extracted from primary literature.

Objectives: Although there have been other comparative analyses of ASD rodent models, they have been limited in scope, both in terms of the number of animal models and the extent of phenotypic assessments used. By looking at the total data sets of rodent models that is available in AutDB, we are expanding our analysis to more than 600 ASD rodent models, and about 375 phenotypic parameters, divided in 16 larger categories. A bioinformatics analysis of this scope can be used to elucidate ASD research trends and etiology

Methods: All data is extracted from published, peer-reviewed primary reports. The metadata is standardized in a phenotypic database, which is a routinely updated comprehensive list of phenotypic terms (pheno-terms) and experimental paradigms. These pheno-terms reflect the actual research and are divided into categories that align with human ASD phenotypic features. For each individual model, annotated pheno-terms contain a given value (e.g. increased, decreased, no change, abnormal). Using the aggregate of these pheno-term values, models are clustered into functional groups.

Results:  ASD rodent models cluster based on phenotypic data that reflect neurophysiological, behavioral and developmental complexity. By looking at a broad genetic and environmental model set we are able to ascertain common underlying biological mechanisms in ASD etiology.

Conclusions: The AutDB animal model module serves as a detailed repository of rodent model phenotypes reported in the ASD field. The scientific standardization of phenotypic parameters allows for data mining and bioinformatics analysis. Our present analysis provides a glimpse of the complexity of ASD etiology, and allows us to visualize the contribution of both genetic and environmental factors by using animal models.

See more of: Animal Models
See more of: Animal Models