MGEnrichment: A web application for microglia gene list enrichment analysis

Abstract

Gene expression analysis is becoming increasingly utilized in neuro-immunology research, and there is a growing need for non-programming scientists to be able to analyze their own genomic data. MGEnrichment is a web application developed both to disseminate to the community our curated database of microglia-relevant gene lists, and to allow non-programming scientists to easily conduct statistical enrichment analysis on their gene expression data. Users can upload their own gene IDs to assess the relevance of their expression data against gene lists from other studies. We include example datasets of differentially expressed genes (DEGs) from human postmortem brain samples from Autism Spectrum Disorder (ASD) and matched controls. We demonstrate how MGEnrichment can be used to expand the interpretations of these DEG lists in terms of regulation of microglial gene expression and provide novel insights into how ASD DEGs may be implicated specifically in microglial development, microbiome responses and relationships to other neuropsychiatric disorders. This tool will be particularly useful for those working in microglia, autism spectrum disorders, and neuro-immune activation research. MGEnrichment is available at https://ciernialab.shinyapps.io/MGEnrichmentApp/ and further online documentation and datasets can be found at https://github.com/ciernialab/MGEnrichmentApp. The app is released under the GNU GPLv3 open source license. Author summary Recent technological and computational advances have produced a massive amount of sequencing data that is often inaccessible to the non-bioinformatician. This is particularly true in multi-disciplinary areas of study such as neuro-immunology, where scientists come from a diversity of background fields. We developed a tool to allow wet-lab scientists without computational skills to utilize previous findings on microglia, the innate immune cells of the brain. Our web hosted tool allows users to compare their genes of interest against a large database of previously published gene lists relevant to microglia and brain disorders. With just a few clicks on the interface, users can upload their genes of interest from mouse or human studies, and query their list by selecting options for running statistical analysis. The application compares the user input to each database list, performs a statistical comparison and returns the results to the user, which can be viewed within the application or downloaded for publication. We have included two example datasets of genes from Autism Spectrum Disorder human brain samples. With these example datasets we demonstrate that this type of analysis can be utilized to identify new biological insights and high priority targets for further study in the lab.