Do pseudogenes pose a problem for metabarcoding marine animal communities?
Bibliographic record
Abstract
Because DNA metabarcoding typically employs sequence diversity among mitochondrial amplicons to estimate species composition, nuclear mitochondrial pseudogenes (NUMTs) can inflate diversity. This study quantifies the incidence and attributes of NUMTs derived from the 658 bp barcode region of cytochrome c oxidase I (COI) in 156 marine animal genomes. The number of NUMTs meeting four length criteria (>150 bp, >300 bp, >450 bp, >600 bp) was determined, and they were examined to ascertain if they could be recognized by their possession of indels or stop codons. In total, 389 NUMTs <100 bp were detected, with an average of 2.49 per species (range = 0–50) and a mean length of 336 bp +/- 208 bp. Among NUMTs lacking diagnostic features, 52.5% were ≤300 bp, 63.9% were ≤450 bp, and 76.2% were ≤600 bp. Studies examing 150 bp amplicons inflate the OTU count by 1.57x compared to the true species count and increase perceived intraspecific variation at COI by 1.19x (when sequence variants with >2% sequence divergence are recognized as different OTUs). There was a weak positive correlation between genome size and NUMT count but no variation among phyla, trophic groups or life history traits. While bioinformatic advances will improve NUMT detection, the best defense involves targeting long amplicons and developing reference databases that include both mitochondrial sequences and their NUMT derivatives.
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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 | 0.029 | 0.123 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
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".