Assessing DNA Barcoding as a Tool for Species Identification and Data Quality Control
Bibliographic record
Abstract
In recent years, the number of sequences of diverse species submitted to GenBank has grown explosively and not infrequently the data contain errors. This problem is extensively recognized but not for invalid or incorrectly identified species, sample mixed-up, and contamination. DNA barcoding is a powerful tool for identifying and confirming species and one very important application involves forensics. In this study, we use DNA barcoding to detect erroneous sequences in GenBank by evaluating deep intraspecific and shallow interspecific divergences to discover possible taxonomic problems and other sources of error. We use the mitochondrial DNA gene encoding cytochrome b (Cytb) from turtles to test the utility of barcoding for pinpointing potential errors. This gene is widely used in phylogenetic studies of the speciose group. Intraspecific variation is usually less than 2.0% and in most cases it is less than 1.0%. In comparison, most species differ by more than 10.0% in our dataset. Overlapping intra- and interspecific percentages of variation mainly involve problematic identifications of species and outdated taxonomies. Further, we detect identical problems in Cytb from Insectivora and Chiroptera. Upon applying this strategy to 47,524 mammalian CoxI sequences, we resolve a suite of potentially problematic sequences. Our study reveals that erroneous sequences are not rare in GenBank and that the DNA barcoding can serve to confirm sequencing accuracy and discover problems such as misidentified species, inaccurate taxonomies, contamination, and potential errors in sequencing.
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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 | 0.142 | 0.303 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| 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".