4.0 Article

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

期刊

NAR GENOMICS AND BIOINFORMATICS
卷 3, 期 1, 页码 -

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OXFORD UNIV PRESS
DOI: 10.1093/nargab/lqab018

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资金

  1. University of Oslo
  2. Research Council of Norway [273833, 274867]
  3. Olav Thon Foundation [421258]

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The study of resistomes in complex microbial communities using whole metagenomic sequencing has made progress, but exploring large complex datasets remains a key challenge requiring robust computational resources and technical expertise.
The study of resistomes using whole metagenomic sequencing enables high-throughput identification of resistance genes 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. 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 metagenomic 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 derived from metagenomic samples; finally, the 'Antimicrobial Resistance Gene List' module enables users to intuitively explore the associations between antimicrobial resistance genes and the microbial hosts using network visual analytics to gain biological insights.

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