Multi-omics characterization of the Canada goose fecal microbiome reveals selective efficacy of simulated metagenomes
Bibliographic record
Abstract
Abstract BackgroundUnderstanding the composition and function of the microbiome is essential to understanding vertebrates. The taxonomic composition of a microbiome is often identified using amplicon sequencing of the 16S rRNA gene, but as a single marker, it cannot identify functions (genes). Metagenome and metatranscriptome sequencing can determine microbiome function but can be cost prohibitive. Therefore, computational methods have been developed to generate simulated metagenomes derived from 16S rRNA sequences and full-length genomes. Simulated metagenomes can be an effective alternative to empirical sequencing, but accuracy depends on the genomic database used and whether the database contains organisms closely related to the 16S sequences. The effectiveness of simulated metagenomes in non-model systems is poorly known. We sought to determine the accuracy of simulated metagenomes in a non-model organism, the Canada goose (Branta canadensis), by comparing metagenomes and metatranscriptomes to simulated metagenomes derived from 16S amplicon sequencing of the same samples. ResultsThe Canada goose fecal microbiome is rich in microbial taxa and functions, including those involved in fermentation. There were significant differences between the metagenomes, metatranscriptomes, and simulated metagenomes when comparing enzymes (ECs), KEGG orthologies (KOs), and metabolic pathways. The simulated metagenomes, when compared to the metagenomes, accurately identified the majority of the total ECs, KOs and pathways. The simulated metagenomes accurately identified the majority of the short-chain fatty acid metabolic pathways crucial to organisms with a nutrient poor diet. When narrowed in scope to specific groups of genes, the simulated metagenomes overestimated the number antimicrobial resistance genes and underestimated the number of genes relating to digestion in folivores.ConclusionsOur data show simulated metagenomes may be a useful tool when studying the functional potential of a non-model organism’s microbiome, depending on the question being asked. Simulated metagenomes were selectively accurate when identifying certain characteristics of the microbiome, like metabolic pathways, but not when compared to the entire sequenced metagenome. While simulated metagenomes are an important tool for inferring function, the lack of whole microbial genomes from understudied systems provides an impetus for continued study and sequencing of whole genomes and metagenomes from non-model systems.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".