The <scp>CODEX</scp> Approach: High‐Throughput Sequencing of the <scp> <i>Cox</i> </scp> ‐1 Barcode Fragment in Neogastropods (Mollusca, Gastropoda)
Bibliographic record
Abstract
DNA barcoding traditionally relies on Sanger sequencing but faces limitations with degraded samples. High-throughput sequencing (HTS) offers a cost-effective alternative, enabling rapid barcode generation for extensive datasets. The advantage of HTS is its ability to employ multiplexing strategies, allowing thousands of samples to be processed simultaneously in a single sequencing run. This study presents the CODEX approach, a double-indexed HTS method designed to sequence overlapping cox-1 barcode fragments, suitable for samples with degraded DNA. The approach was applied to neogastropods, a diverse lineage of marine molluscs, using specimens (both recently collected and relatively older) from the Muséum national d'Histoire naturelle (MNHN) collections. The pipeline was used to process 15,076 samples, yielding 10,905 cleaned and assembled sequences, achieving a success rate of 72.33%. The CODEX method demonstrated advantages over Sanger sequencing by enabling the recovery of barcodes from samples previously deemed unsuitable, with significantly reduced costs (€0.5 per sequence vs. €4.5). Notably, DNA quality and sequencing success were strongly correlated with collection date, emphasising the impact of preservation methods and storage conditions. Sequencing success rates varied among families but were not correlated with phylogenetic relationships or specimen size, indicating the robustness of the primers designed for neogastropods. This study highlights the efficiency of the CODEX approach for large-scale DNA barcoding projects, especially when handling degraded samples. The CODEX pipeline and associated resources are publicly accessible, offering a scalable solution for molecular systematics and beyond.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".