ResistoXplorer: a web-based tool for visual, statistical and exploratory data analysis of resistome data

Abstract Background The study of resistomes using whole metagenomic sequencing enables access to the large repertoire of resistance genes usually found in complex microbial communities, such as the human microbiome. Over recent years, sophisticated and diverse pipelines have been established to facilitate raw data processing and annotation. Despite the progress, there are no easy-to-use tools for comprehensive visual, statistical, and functional analysis of resistome data. Thus, exploration of the resulting large complex datasets remains a key bottleneck requiring robust computational resources and technical expertise, which creates a significant hurdle for advancements in the field. Results Here, we introduce ResistoXplorer, a user-friendly tool that integrates recent advancements in statistics and visualization, coupled with extensive functional annotations and phenotype collection, to enable high-throughput analysis of common outputs generated from resistome studies. ResistoXplorer contains three modules-the ‘Antimicrobial Resistance Gene Table’ module offers various options for composition profiling, functional profiling and comparative analysis of resistome data; the ‘Integration’ module supports integrative exploratory analysis of resistome and microbiome abundance profiles in metagenomic samples; finally, the ‘Antimicrobial Resistance Gene List’ module enables users to explore antimicrobial resistance genes according to function and potential microbial hosts using visual analytics to gain biological insights. Within these three modules, ResistoXplorer offers comprehensive assistance for ARG functional annotations along with their microbe and phenotype associations based on data collected from >10 reference databases. In addition, it provides support for a variety of methods for composition profiling, visualization and exploratory data analysis, as well as extensive support for various data normalization methods and machine learning algorithms for identification of resistome signatures. Finally, ResistoXplorer offers also network visualization for intuitive exploration of associations between antimicrobial resistance genes and the microbial hosts, incorporated with functional enrichment analysis support. Conclusions ResistoXplorer is a web-based tool with a user-friendly interface that enables comprehensive and real-time downstream analysis of resistome data. As such, it allows for in-depth exploration of metagenomic datasets focusing on the intrinsic networks and correlations of antimicrobial resistance genes and their underlying microbial determinants. ResistoXplorer will assist researchers and clinicians in the field of AMR to facilitate discovery in large-scale and multi-dimensional metagenomic datasets. ResistoXplorer is publicly available at http://www.resistoxplorer.no/ResistoXplorer.