Many species in one: DNA barcoding overestimates the number of species when nuclear mitochondrial pseudogenes are coamplified
Bibliographic record
Abstract
Nuclear mitochondrial pseudogenes (numts) are nonfunctional copies of mtDNA in the nucleus that have been found in major clades of eukaryotic organisms. They can be easily coamplified with orthologous mtDNA by using conserved universal primers; however, this is especially problematic for DNA barcoding, which attempts to characterize all living organisms by using a short fragment of the mitochondrial cytochrome c oxidase I (COI) gene. Here, we study the effect of numts on DNA barcoding based on phylogenetic and barcoding analyses of numt and mtDNA sequences in two divergent lineages of arthropods: grasshoppers and crayfish. Single individuals from both organisms have numts of the COI gene, many of which are highly divergent from orthologous mtDNA sequences, and DNA barcoding analysis incorrectly overestimates the number of unique species based on the standard metric of 3% sequence divergence. Removal of numts based on a careful examination of sequence characteristics, including indels, in-frame stop codons, and nucleotide composition, drastically reduces the incorrect inferences of the number of unique species, but even such rigorous quality control measures fail to identify certain numts. We also show that the distribution of numts is lineage-specific and the presence of numts cannot be known a priori. Whereas DNA barcoding strives for rapid and inexpensive generation of molecular species tags, we demonstrate that the presence of COI numts makes this goal difficult to achieve when numts are prevalent and can introduce serious ambiguity into DNA barcoding.
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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 | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".