bioRxivBioinformaticsPreprint
BugBase Predicts Organism Level Microbiome Phenotypes
Tonya Ward, Jake Larson, Jeremy Meulemans, Ben Hillmann, Joshua K. LynchiD, Dimitrios N. SidiropoulosiD, John R. SpeariD, Greg Caporaso, Ran BlekhmaniD, Rob D. KnightiD, Ryan C. FinkiD, Dan KnightsiD
Abstract
Shotgun metagenomics and marker gene amplicon sequencing can be used to directly measure or predict the functional repertoire of the microbiota en masse, but current methods do not readily estimate the functional capability of individual microorganisms. Here we present BugBase, an algorithm that predicts organism-level coverage of functional pathways as well as biologically interpretable phenotypes such as oxygen tolerance, Gram staining and pathogenic potential, within complex microbiomes using either whole-genome shotgun or marker gene sequencing data. We find BugBases organism-level pathway coverage predictions to be statistically higher powered than current bag-of-genes approaches for discerning functional changes in both host-associated and environmental microbiomes.
- organism: bacteria (other)
- organism: human
- assay: metagenomics
- research focus: software / tool
- research focus: benchmarking
- approach: statistical model
In-depth summary
Claude Opus 5.5 · xhigh effort · 17 Oct 2026 · from the full text of version 1 (2 May 2017) · CC BY 4.0
BugBase assigns pathway coverage and traits (Gram stain, oxygen tolerance, pathogenicity) to individual taxa in 16S or shotgun microbiome data, detecting some functional differences bag-of-genes profiles miss.
Standard functional profiling of shotgun or 16S data, through tools such as PICRUSt with KEGG or COG, treats the community as one pooled gene set and cannot readily report experimentally annotated traits like Gram staining or oxygen tolerance. BugBase instead predicts trait coverage organism by organism, picks each pathway's coverage threshold from the data without using sample labels, and wraps this in a web interface.
Key results
SuggestedOrganism-level pathway coverage gives smaller p-values than bag-of-genes profiling for HMP body-site contrasts, particularly at small sample sizes and between taxonomically similar sites.
Mann-Whitney U tests on subsamples of 1 to 250 HMP samples per body site (30 repeats each) for 470 level-three KEGG modules and selected pathways such as starch metabolism: both approaches separate stool from tongue, bag-of-genes degrades faster as sample size falls, and only the organism-level approach separates supra- from subgingival plaque. No p-values or power estimates are printed in the text. · Organism-level predictions are more sensitive…; Organism-level and bag-of-genes prediction…
ShownOn held-out reference OTUs, 16S-based trait predictions are largely accurate, ranging from 76% for aerobes to 97% for pathogenicity-related genome content.
Ten-fold cross-validation, each fold withholding 10% of the fully sequenced, labelled reference OTUs; for non-empirical traits the score is accuracy of predicted gene occurrence rather than of the phenotype. Per-trait values between these extremes are in a table not reproduced in this text. · Reference-based validation of BugBase…; Assessment of accuracy
ShownPredicted traits recover documented oxygen-tolerance differences across human body sites and a plaque biofilm difference consistent with earlier reports, and predict more potential pathogens in stool than in oral sites.
HMP (stool n=394, tongue n=368, subgingival n=373, supragingival n=376): stool had more anaerobes, fewer biofilm formers, more potential pathogens (driven by Bacteroides) and fewer mobile-element carriers than oral sites; subgingival plaque had more anaerobes and supragingival plaque more biofilm formers (driven by Actinomyces); all p < 0.001. · Biological validation of predicted phenotypes in…; Phenotype Predictions for Published Datasets
SuggestedGram-stain and oxygen-tolerance predictions follow clinical Nugent scores in vaginal microbiome samples.
Samples with high Nugent scores (bacterial vaginosis, n=97) were predicted to hold more anaerobes and Gram-negative bacteria than intermediate (n=49) or low (n=248) samples; the text prints the test result as 'p > 0.001' and gives no concordance statistic. · Biological validation of predicted phenotypes in…; Discussion; Phenotype Predictions for Published Datasets
ShownPredicted trait shifts also track lifestyle and environmental gradients, including water temperature and soil pH.
Gut: USA (n=263) vs Malawi (n=54) and Venezuela (n=69) had fewer facultative anaerobes and pathogens and more Gram-positive bacteria (p < 0.001), with no aerobe difference (p > 0.620). Yellowstone hot-spring water (n=412): aerobes fell and facultative anaerobes rose with temperature (both p < 0.001). Soil: aerobes (p < 0.05) and oxidative-stress-tolerant taxa (p < 0.01) declined as pH rose. · Biological validation of predicted phenotypes in…; Phenotype Predictions for Published Datasets
Methods, data and reproducibility
BugBase maps 16S OTUs (via PICRUSt-precalculated, copy-number-normalised gene content) or shotgun strain tables to trait gene sets curated from IMG, KEGG and PATRIC, sweeps coverage thresholds from 0.01 to 1 and keeps the one maximising cross-sample variance, then reports the fraction of each sample carrying each trait with Mann-Whitney U, Kruskal-Wallis or Spearman tests against metadata.
- Human Microbiome Project 16S samples from tongue dorsum, stool, subgingival and supragingival plaque (at least 500 sequences each) · human · n = tongue n=368, stool n=394, subgingival n=373, supragingival n=376
- Fecal microbiomes of non-infants (>3 years old) from the USA, Malawi and Venezuela, downloaded from Qiita · human · n = USA n=263, Malawi n=54, Venezuela n=69
- Vaginal microbiome 16S samples with Nugent scores from a bacterial vaginosis study · human · n = low n=248, intermediate n=49, high n=97 · SRA022855
- Yellowstone National Park hot-spring water samples across a temperature gradient, from Qiita · n = 412 water samples
- Soil microbiome sampled across a pH gradient over two years, from Qiita
- Methods & tools
- BugBase, PICRUSt, QIIME v1.8.0, USEARCH (usearch_global), NINJA-OPS, GraPhlAn v0.9, Greengenes 97% OTUs v13.5.8, IMG version 4.0, KEGG, PATRIC, R
- Reagents & environment
- Only R and its required libraries are needed for the default traits; analyses used QIIME v1.8.0, Greengenes v13.5.8, IMG version 4.0 and GraPhlAn v0.9, run with the run.bugbase.r command.
- Reproducibility gaps
- The paper says BugBase is a web application and open-source package and that the full analysis workflow with its data files is online, but none of these links survive in this text.
Limitations (6)
- With 16S input BugBase inherits PICRUSt's limits: no eukaryotes or viruses, limited strain resolution, poorer accuracy in sparsely sequenced branches of the tree, and no account of horizontal gene transfer. · stated by the authors
- Predictions rest on fully sequenced reference genomes and are meant for hypothesis generation rather than definitive quantification; non-empirical phenotypes were not tested experimentally. · stated by the authors
- Requiring a trait within a single genome can miss pathways completed by co-metabolism between cells, producing false negatives. · stated by the authors
- Within-strain variation from mutation or horizontal transfer is not modelled, and mobile-element predictions should be interpreted cautiously. · stated by the authors
- The comparison with bag-of-genes judges power by smaller p-values on HMP body-site contrasts, with no ground truth for which pathways truly differ, and no environmental dataset is described for it despite the abstract's wording. · noted in this summary
- Biological validation is agreement with expected patterns, reported only as p-value thresholds without effect sizes. · noted in this summary
Read this if you want per-taxon trait or pathway predictions (Gram stain, oxygen tolerance, pathogenicity, biofilm formation) from 16S or shotgun OTU tables instead of community-wide gene totals.
- Citations
- 491
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