Exploring the functional composition of the human microbiome using a hand-curated microbial trait database

Weissman, Jake L.; Dogra, Sonia; Javadi, Keyan; Bolten, Samantha; Flint, Rachel; Davati, Cyrus; Beattie, Jess; Dixit, Keshav; Peesay, Tejasvi; Awan, Shehar; Thielen, Peter; Breitwieser, Florian P.; Johnson, Philip L.; Karig, David; Fagan, William F.; Bewick, Sharon

doi:10.1186/s12859-021-04216-2

Cited by 9 publications

(8 citation statements)

References 56 publications

(68 reference statements)

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“…Finally, even though for a given set of a priori annotations CBEA can generate useful summary scores, such values are limited in their utility if the annotations themselves are not meaningful. As such, curating and validating sets (similar to MSigDB [ 12 ]) based on physiological or genomic characteristics of microbes [ 70 ] or their association with human disease (in beta BugSigDB https://bugsigdb.org/Main_Page ) can allow for incorporating functional insights into microbiome-outcome analyses.…”

Section: Discussionmentioning

confidence: 99%

CBEA: Competitive balances for taxonomic enrichment analysis

2022

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Research in human-associated microbiomes often involves the analysis of taxonomic count tables generated via high-throughput sequencing. It is difficult to apply statistical tools as the data is high-dimensional, sparse, and compositional. An approachable way to alleviate high-dimensionality and sparsity is to aggregate variables into pre-defined sets. Set-based analysis is ubiquitous in the genomics literature and has demonstrable impact on improving interpretability and power of downstream analysis. Unfortunately, there is a lack of sophisticated set-based analysis methods specific to microbiome taxonomic data, where current practice often employs abundance summation as a technique for aggregation. This approach prevents comparison across sets of different sizes, does not preserve inter-sample distances, and amplifies protocol bias. Here, we attempt to fill this gap with a new single-sample taxon enrichment method that uses a novel log-ratio formulation based on the competitive null hypothesis commonly used in the enrichment analysis literature. Our approach, titled competitive balances for taxonomic enrichment analysis (CBEA), generates sample-specific enrichment scores as the scaled log-ratio of the subcomposition defined by taxa within a set and the subcomposition defined by its complement. We provide sample-level significance testing by estimating an empirical null distribution of our test statistic with valid p-values. Herein, we demonstrate, using both real data applications and simulations, that CBEA controls for type I error, even under high sparsity and high inter-taxa correlation scenarios. Additionally, CBEA provides informative scores that can be inputs to downstream analyses such as prediction tasks.

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Section: Discussionmentioning

confidence: 99%

CBEA: Competitive balances for taxonomic enrichment analysis

2022

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“…We utilized pre-compiled trait databases from previous publications: Madin et al 2020 [40] and Weissman et al 2021 [63]. The former was chosen due to the fact that it is the most comprehensive compilation of microbial (bacteria and archaea) physiological traits based on existing sources to date.…”

Section: Methodsmentioning

confidence: 99%

“…While using predictive models can help in assigning traits to a broader category of taxa [62], such automated approaches can result in misclassification of traits and increased noise in downstream analyses. Additionally, high-quality trait annotations require a time-consuming, manual curation process [63]. A source that is based on user submission such as GOLD [44] can cover a larger number of taxa and traits, but unfortunately can have erroneous and duplicated assignments due to the lack of a standardized nomenclature.…”

Section: Limitations and Future Directionsmentioning

confidence: 99%

“…As such, these databases are limited in generating meaningful hypotheses linking taxonomic changes to ecosystem function especially in novel disease conditions. Trait-based analysis [4, 40, 63], with its long history in traditional macroecological studies [19, 34], is a promising approach to address this gap. Traits directly represent microbial physiological characteristics and metabolic phenotypes (for example, sulfur reduction, nitrate utilization, or gram positivity) and therefore can serve as annotations for potential ecosystem function.…”

Section: Introductionmentioning

confidence: 99%

“…Even though trait-based approaches have been utilized in various studies [4, 23, 34, 63], to our knowledge there is currently no effort to formalize trait-based databases in terms of microbial sets and evaluate their utility in a typical enrichment analysis of 16s rRNA metabarcoding or metagenomic data. Here, we constructed taxon sets from pre-existing trait databases at both the species and genus level.…”

Section: Introductionmentioning

confidence: 99%

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Evaluating trait-based sets for taxonomic enrichment analysis applied to human microbiome data sets

Nguyen

Hoen

Frost

2022

Preprint

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BackgroundSet-based pathway analysis is a powerful tool that allows researchers to summarize complex genomic variables in the form of biologically interpretable sets. Since the microbiome is characterized by a high degree of inter-individual variability in taxonomic compositions, applying enrichment methods using functionally driven taxon sets can increase both the reproducibility and interpretability of microbiome association studies. However, there is still an open question of which knowledge base to utilize for set construction. Here, we evaluate microbial trait databases, which aggregate experimentally determined microbial phenotypes, as a potential avenue for meaningful construction of taxon sets.MethodUsing publicly available microbiome sequencing data sets (both 16S rRNA gene metabarcoding and whole-genome metagenomics), we assessed these trait-based sets on two criteria: first, do they cover the diversity of microbes obtained from a typical data set, and second, do they confer additional predictive power on disease prediction tasks when assessed against measured pathway abundances and PICRUSt2 prediction.ResultsTrait annotations are well annotated to a small number but most abundant taxa within the community, concordant with the concept of the core-peripheral microbiome. This pattern is consistent across all categories of traits and body-sites for whole genome sequencing data, but much more heterogenous and inconsistent in 16S rRNA metabarcoding data due to difficulties in assigning species-level traits to genus. However, trait-set features are well predictive of disease outcomes compared against predicted and measured pathway abundances. Most important trait-set features are more interpreable and reveal interesting insights on the relationship between microbiome, its function, and health outcomes.

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