Implementing the gEAR for Data Sharing Within the HRP
Ronna Hertzano, M.D., Ph.D., University of Maryland School of Medicine

One of the successes of the HRP has been the development of the gEAR portal (gene Expression Analysis Resource, umgear.org). The gEAR has many public and private datasets, and these complex datasets can be compared by scientists without the need for sophisticated programming expertise. The gEAR is also the primary data sharing, visualization, and analysis tool for auditory researchers outside of the HRP, becoming a platform that supports the hearing research community at large. This year we will build on past successes, continuing to support data upload, develop new visualization tools, and further enable the greater research community to exploit this resource.

Implementing the gEAR for Data Sharing Within the HRP
Ronna Hertzano, M.D., Ph.D., University of Maryland School of Medicine

When a group of geographically dispersed scientists collaborate on hair cell regeneration in three different animal models—chicken, zebrafish, and mouse—and use multiple methods to track how genes “instruct” cells (multi-omics), an enormous amount of data results. The work of visualizing, conceptualizing, and analyzing these data presents a considerable challenge, and as technology has advanced, much of the multi-omic data is generated at the single cell level, resulting in datasets and files that are too big to process with traditional tools, such as Excel worksheets. The gEAR portal (gene Expression Analysis Resource, umgear.org) responds to this need by enabling meaningful visualization and analysis of these complex datasets in the public or private domain—no advanced programming skills required. It has also evolved to become a primary data sharing, visualization, and analysis tool for auditory researchers outside of the HRP to become a platform that supports the hearing research community at large.

Implementing the gEAR for data sharing within the HRP
Ronna Hertzano, M.D., Ph.D. University of Maryland

The HRP takes a multi-investigator, multi-species, multi-omic (methods of tracking gene expression, or instructions), and cell type‐specific approach to define the underpinnings of differences among hair cell regeneration in the chick, fish, and mouse with the aim of identifying keys for hair cell regeneration in mammals. Consequently, the consortium generates large amounts of data that are difficult to visualize, conceptualize, and analyze. The gEAR portal (gene Expression Analysis Resource, umgear.org) allows for simple visualization of multi-omic, multi-species datasets in the public or private domain—without the need for advanced informatics skills. In the first two years of funding from the HRP, we focused primarily on developing tools for multi-omic, multi-species data upload and visualization. Numerous features were added, and all available HRP datasets were uploaded for sharing within the consortium. In parallel, all tools and features developed for the consortium were made available in the public domain—leading the gEAR to be a primary portal for multi-omic data sharing and visualization within the field. With the next two years of funding committed (years three and four), the vast majority of our efforts will be focused on (a) the continued upload of HRP and public datasets, and (b) the development and integration of analysis tools.

Integrative systems biology of hearing restoration
Ronna Hertzano, M.D., Ph.D. University of Maryland

The HRP consortium aims to identify factors that can either block or promote regeneration and has generated multiple genomics datasets from inner ears of regenerating and non-regenerating model organisms. This proposal aims to predict these causal factors by a detailed analysis of those datasets. The project’s two investigators—Hertzano, who is well versed in inner ear development and genomics, and Seth Ament, Ph.D., an expert in systems biology and neurobiology—will develop a quantitative model for the gene regulatory networks in hair cells and hair cell precursors that will allow them to predict key genes (e.g., master regulator transcription factors) that are associated with the ability to regenerate hair cells. They will use cutting-edge network biology approaches that integrate information about the preservation and divergence of gene co-expression across species and conditions, with information about the targets of hundreds of transcription factors.

Implementing the gEAR for data sharing within the HRP
Ronna Hertzano, M.D., Ph.D. University of Maryland

Here the gEAR (gene expression for auditory research) portal will be further developed for a second year to perform key gene comparison tasks for the HRP. The gEAR allows for the graphic visualization of gene expression data in an intuitive way: Multiple datasets can be displayed on a single page, all represented by cartoons and dynamically colored based on levels of gene expression. Additional features will be added through dedicated developer time to specifically serve the needs of the consortium, including additional dataset-specific graphics, tools for cross-dataset and cross-species comparisons, and intuitive integration of DNA-structure and gene expression.

Implementing the gEAR for discretionary data sharing within the HRP
Ronna Hertzano, M.D., Ph.D. University of Maryland School of Medicine

Here the gEAR portal (gene Expression for Auditory Research portal) will be adapted to perform key gene comparison tasks for the HRP. The gEAR allows for graphic visualization of gene expression data in an intuitive way: multiple datasets can be displayed in a single page, all represented by cartoons and dynamically colored based on levels of gene expression. Additional features will be added through dedicated developer time to specifically serve the needs of the consortium, including additional dataset-specific graphics, tools for cross-dataset and cross-species comparisons, and intuitive integration of DNA structure and gene expression. Once the gEAR has been adapted for the HRP, all HRP members will be able to browse and interrogate the consortium data on a regular basis, allowing for computational identification of targets that lead to hair cell regeneration.