Neuroinformatics for gene expression: networks, function and meta-analysis

University Of British Columbia, Vancouver BC

Investigators

Linked publications, trials & patents

Abstract

? DESCRIPTION (provided by applicant): Our project helps address challenges neurobiologists face when trying to interpret the results of genomics and genetics studies, to identify candidate genes for specific normal or disease processes, and to leverage the huge quantities of available gene expression data. In this phase of the project, we are moving further beyond gene expression into other areas for which integration with genomics data is increasingly important and challenging for neuroscientists, including electrophysiology, diseases, cell types, and epigenetics. Our first aim is to develop and apply computational methods and tools for enhanced gene function analysis and prediction in the nervous system. We are proposing to expand our database of genomics data, Gemma, to include re- analyzed data from DNA methylation studies from public databases. We will also enhance Gemma to allow more condition-centric analysis for coexpression and differential expression studies, and comparative analysis of conditions from a gene network and regulation point of view. We will develop and apply novel methods for evaluating genomics data quality to increase the value of the data in Gemma, and to provide contextual information to protect against biases caused by gene multifunctionality (genericity). Second, we are developing and applying NeuroElectro, a knowledgebase system linking quantitative electrophysiological properties and cell types. The system will be expanded to include more cell types and more parameters, especially synaptic properties and treatment and disease conditions, as well as more experimental parameters such as ion concentrations to permit normalization. We will integrate information on gene expression in specific neuron types and brain regions, and perform analyses to identify genes that contribute to electrophysiological properties and their regulators, including potential relationships to diseases. Finally, we are proposing to bring together the data resources and tools we have developed along with third-party resources to add new user- friendly components to Gemma for accessing and analyzing the data to address common questions such as enrichment of gene expression within specific brain regions. This project provides a step in the path to enabling the types of interdisciplinary data analyses that are increasingly essential to progress in neuroscience, putting them in the hands of the researchers where they will have the most direct impact. All of our data and software are designed to be user-friendly, open-source, and freely available to the community, and will be disseminated through our own web site, programmatic interfaces as well as via integration with third-party systems.