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App Helps Pinpoint Potential Gene Associations for Asthma Research


CHICAGO (GenomeWeb) – Barely a year after building an application to integrate and scan genome-wide association studies, whole-genome sequencing, RNA-seq, and related omics test results, informaticians at the University of Pennsylvania have presented results of a study on asthma genes that provides a glimpse of the potential power of their technology.

"We found a couple of genes that might be interesting to follow up on based on the results," said Maya Shumyatcher, a junior bioinformatician in the Department of Biostatistics, Epidemiology, and Informatics at Penn.

"SCNN1A should be prioritized for study as an asthma gene, and specifically as a modulator of glucocorticoid and/or immune response," according to a paper Shumyatcher presented last month at the American Medical Informatics Association's annual symposium in Washington.

This conclusion comes courtesy of an app called Reducing Associations by Linking Genes and Transcriptomic Results, or REALGAR, developed in the laboratory of Blanca Himes, who applies biomedical informatics to respiratory diseases. The Himes Lab in the Department of Biostatistics, Epidemiology, and Informatics collaborates with researchers across Penn's Perelman School of Medicine to help researchers find answers to specific data-related questions.

"We use asthma as a disease model to show how gene-centric information that is gathered in a disease-specific fashion can be useful to better understand associations," according to the researchers.

"We collaborate with a lot of wet-lab researchers, and a lot of times, we will get requests from people" to look up specific genes to try to find whether there appear to be any links with asthma, Shumyatcher said. "We thought that it would be useful to have a tool where researchers could look up that information themselves and have it all in one place and have it easy to interpret, easy to use."

Shumyatcher, Himes, and other colleagues in their department built REALGAR in Shiny, an open-source platform for creating interactive web apps, targeting "wet-lab researchers with little computing experience," according to the AMIA paper. They released their creation on the Himes Lab website and posted the source code for other open-source developers on GitHub.

The initial build of REALGAR includes 27 data sets on asthma and glucocorticoid response from the Gene Expression Omnibus, which provided microarray and RNA sequencing data, as well as SNPs.

"There is all this publicly available data that exists, but it is not necessarily very easily accessible," Shumyatcher said. "We wanted to make a resource that would be something that researchers could use at a glance. Otherwise, it's just so computationally intensive. It's time-consuming. It's a lot of work to go from the raw data files to a result that you can draw conclusions from."

The Penn group ran the RNA-seq data in the school's high-performance computing environment. "With the size of the data sets, you need a lot of computational power and resources," Shumyatcher said.

The RNA-seq data sets came from from previous work Himes had performed, but the AMIA presentation, published in the official conference proceedings, contains the first public, peer-reviewed results from REALGAR.

Users enter a SNP identifier or an "official" symbol of a gene, then select the types of tissue, types of asthma, results from gene expression tests of drug therapies, and which GWAS results to display. If they choose to see GWAS results from the EVE Consortium, users can drill down further to sort by race or ethnicity.

REALGAR scans the GEO datasets to produce a list of GEO entries and PubMed listings of relevant literature. The app also creates visualizations of gene-specific fold changes for the appropriate tissue and asthma type.

"We tried to present the right amount of information where users can make informed choices and seize information that they need without it being overwhelming. We also wanted it to look nice and [be] easy to understand," Shumyatcher said.

"A lot of the time, the researchers that we work with are specifically interested in changes associated with a particular disease state or a particular treatment," Shumyatcher explained. "These things can really vary across different tissue types. Somebody who's designing follow-up experiments, they really want to be able to see in a particular disease state in a particular tissue type, and that's something that our app allows users to do."

Shumyatcher said that the app remains under development and that Penn researchers are looking at how they might be able to take advantage of this technology. "At this point, we're focusing on adding more datasets to the app," she said.

"I think it will be a good research for wet-lab researchers just to explore the data in a way that is hands-on. I think it eliminates a lot of the inefficiencies of having to email somebody and ask" for them to look up data on specific genes, Shumyatcher said. "I hope it will help people do more of the good work that that they do."