Performance comparison of genetic markers for high‐throughput sequencing‐based biodiversity assessment in complex communities
Bibliographic record
Abstract
Metabarcode surveys of DNA extracted from environmental samples are increasingly popular for biodiversity assessment in natural communities. Such surveys rely heavily on robust genetic markers. Therefore, analysis of PCR efficiency and subsequent biodiversity estimation for different types of genetic markers and their corresponding primers is important. Here, we test the PCR efficiency and biodiversity recovery potential of three commonly used genetic markers - nuclear small subunit ribosomal DNA (18S), mitochondrial cytochrome c oxidase subunit I (COI) and 16S ribosomal RNA (mt16S) - using 454 pyrosequencing of a zooplankton community collected from Hamilton Harbour, Ontario. We found that biodiversity detection power and PCR efficiency varied widely among these markers. All tested primers for COI failed to provide high-quality PCR products for pyrosequencing, but newly designed primers for 18S and 16S passed all tests. Furthermore, multiple analyses based on large-scale pyrosequencing (i.e. 1/2 PicoTiter plate for each marker) showed that primers for 18S recover more (38 orders) groups than 16S (10 orders) across all taxa, and four vs. two orders and nine vs. six families for Crustacea. Our results showed that 18S, using newly designed primers, is an efficient and powerful tool for profiling biodiversity in largely unexplored communities, especially when amplification difficulties exist for mitochondrial markers such as COI. Universal primers for higher resolution markers such as COI are still needed to address the possible low resolution of 18S for species-level identification.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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".