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.
Highlights
With the recent advances in sequencing technology, researchers are increasingly able to generate larger amounts of genomic data
Recent technological and computational advances have produced a massive amount of sequencing data that is often inaccessible to the non-bioinformatician. This is 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
Summary
With the recent advances in sequencing technology, researchers are increasingly able to generate larger amounts of genomic data. In the developing brain, early life insults can produce rapid and long-lasting changes to gene expression that alter the neuro-immune system and behaviour [2,3]. The ease at which this data can be generated and incorporated into various experiments has led to gene expression analysis being utilized not just in hypothesis testing, and in hypothesis generation [11]. These microglial gene expression differences have been successfully examined across labs and contexts to identify conserved targets and patterns disrupted across brain disorders [2,12]. There is currently no central repository for published microglial gene lists, nor a user friendly, non-programmatic interface that allows biologists to statistically test their gene list of interest for enrichment of identified microglial gene lists from other studies
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