High-accuracy meets high-throughput for microbiome profiling with near full-length 16S rRNA amplicon sequencing on the Nanopore platform
Bibliographic record
Abstract
Abstract Amplicon sequencing of small subunit (SSU) rRNA genes is a foundational method for studying microbial communities within various environmental, human, and engineered ecosystems. Currently, short-read platforms are commonly employed for high-throughput applications of SSU rRNA amplicon sequencing, but at the cost of poor taxonomic classification. The low-cost Oxford Nanopore Technologies (ONT) platform is capable of sequencing full-length SSU rRNA genes, but the lower raw-read accuracies of previous ONT sequencing chemistries have limited accurate taxonomic classification and de novo generation of operational taxonomic units (OTUs) and amplicon sequence variants (ASVs). Here, we examine the potential for Nanopore sequencing with newer (R10.4+) chemistry to provide high-throughput and high-accuracy full-length 16S rRNA gene amplicon sequencing. We present a sequencing workflow utilizing unique molecular identifiers (UMIs) for error-correction of SSU rRNA (e.g. 16S rRNA) gene amplicons, termed ssUMI. Using two synthetic microbial community standards, the ssUMI workflow generated consensus sequences with 99.99% mean accuracy using a minimum UMI subread coverage threshold of 3x, and was capable of generating error-free ASVs and 97% OTUs with no false-positives. Non-corrected Nanopore reads generated error-free 97% OTUs but with reduced detection sensitivity, and also generated false-positive ASVs. We showcase the cost-competitive and high-throughput scalability of the ssUMI workflow by sequencing 90 time-series samples from seven different wastewater matrices, generating ASVs that were tightly clustered based on sample matrix type. This work demonstrates that highly accurate full-length 16S rRNA gene amplicon sequencing on Nanopore is possible, paving the way to more accessible microbiome science.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".