From classification to confirmation: verifying taxonomic classifications by mapping metagenomic reads to reference genomes
Bibliographic record
Abstract
Abstract Background : Obtaining high precision while maintaining high recall is an ongoing problem for metagenomic taxonomic classification in microbial ecology research. Parameter adjustments can achieve this in simulated samples, but in real samples –especially from environments like marine and soil– the proportion of classified reads drops sharply with precision increases. We therefore suggest verification of metagenomic taxonomic classifications obtained from a tool like Kraken by mapping their assigned reads to reference genomes to assess genomic coverage. Results : In simulations, filtering the identified species to only those with ³0.5% reference genome coverage removed 99.7% of false-positive taxa. Applying this method to samples from real datasets requires a more nuanced approach that considers sequencing depth, whether the samples are high- or low-microbial biomass, and database completeness with respect to the sampled environment. Nevertheless, we show that clinically relevant Kraken-identified taxa such as Helicobacter pylori identified in human stool samples lack any reads mapping to their reference genome and are likely false positives driven by contaminating phage sequences within reference genomes. Similarly, in human blood and tumour datasets, only 18 and 11 species, respectively, have ³1% reference genome coverage and likely represent sample collection or sequencing contaminants. Marine and soil samples pose additional challenges due to lower representation in reference databases, leading to low nucleotide identity between sequenced reads and reference genomes and similarity only at higher taxonomic ranks. Conclusions : We recommend genome coverage checking to researchers in all fields of microbial ecology and provide an open-source pipeline on Github (GeCoCheck): https://github.com/R-Wright-1/GeCoCheck.
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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 | 0.011 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".