Abstract
BackgroundExploration of large data sets, such as shotgun metagenomic sequence or expression data, by biomedical experts and medical professionals remains as a major bottleneck in the scientific discovery process. Although tools for this purpose exist for 16S ribosomal RNA sequencing analysis, there is a growing but still insufficient number of user-friendly interactive visualization workflows for easy data exploration and figure generation. The development of such platforms for this purpose is necessary to accelerate and streamline microbiome laboratory research.ResultsWe developed the Workflow Hub for Automated Metagenomic Exploration (WHAM!) as a web-based interactive tool capable of user-directed data visualization and statistical analysis of annotated shotgun metagenomic and metatranscriptomic data sets. WHAM! includes exploratory and hypothesis-based gene and taxa search modules for visualizing differences in microbial taxa and gene family expression across experimental groups, and for creating publication quality figures without the need for command line interface or in-house bioinformatics.ConclusionsWHAM! is an interactive and customizable tool for downstream metagenomic and metatranscriptomic analysis providing a user-friendly interface allowing for easy data exploration by microbiome and ecological experts to facilitate discovery in multi-dimensional and large-scale data sets.
Highlights
Exploration of large data sets, such as shotgun metagenomic sequence or expression data, by biomedical experts and medical professionals remains as a major bottleneck in the scientific discovery process
As metagenomic and metatranscriptomic shotgun sequencing data become both less expensive to generate and more readily available, researchers have turned to automated pipelines such as MetaPhlAn [1], HUMAnN2 [2, 3] MEGAN [4] and SAMSA [5] for annotation and analysis
The first was derived from 47 human microbiome samples from four body sites made available by the Human Microbiome Project (HMP) [27]
Summary
To demonstrate the utility of WHAM!, we used two independent, publicly available test datasets. We can explore the GO term abundance across samples using the ‘Explore Features’ tab, automatically identifying differentially abundant GO terms across samples based on user-controlled p-value and effect size cutoffs (Fig. 2d) Of those found to be significantly different, several antibiotic resistance-related GO terms were represented, including drug transmembrane transport, differing between stool and all other body sites tested (Fig. 2e). Correlation analyses of functional features can enable users to obtain information about shared selection, or interactions between gene families, according to abundance patterns across different classification groups in the studied datasets From this information, the highly correlated antibiotic transporter activity (GO term 9), kanamycin kinase activity (GO term 11), and response to antibiotic (GO term 7) pathways, suggest shared selection. WHAM! allows for web-based hypothesis generation based on both taxa and functional features, permitting on-the-fly confirmation and figure generation, substantially adding to the current suite of tools available for metagenomic analysis
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