Metabarcoding using multiplexed markers increases species detection in complex zooplankton communities
Bibliographic record
Abstract
Metabarcoding combines DNA barcoding with high-throughput sequencing, often using one genetic marker to understand complex and taxonomically diverse samples. However, species-level identification depends heavily on the choice of marker and the selected primer pair, often with a trade-off between successful species amplification and taxonomic resolution. We present a versatile metabarcoding protocol for biomonitoring that involves the use of two barcode markers (COI and 18S) and four primer pairs in a single high-throughput sequencing run, via sample multiplexing. We validate the protocol using a series of 24 mock zooplanktonic communities incorporating various levels of genetic variation. With the use of a single marker and single primer pair, the highest species recovery was 77%. With all three COI fragments, we detected 62%-83% of species across the mock communities, while the use of the 18S fragment alone resulted in the detection of 73%-75% of species. The species detection level was significantly improved to 89%-93% when both markers were used. Furthermore, multiplexing did not have a negative impact on the proportion of reads assigned to each species and the total number of species detected was similar to when markers were sequenced alone. Overall, our metabarcoding approach utilizing two barcode markers and multiple primer pairs per barcode improved species detection rates over a single marker/primer pair by 14% to 35%, making it an attractive and relatively cost-effective method for biomonitoring natural zooplankton communities. We strongly recommend combining evolutionary independent markers and, when necessary, multiple primer pairs per marker to increase species detection (i.e., reduce false negatives) in metabarcoding studies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".