Assessing the influence of sample tagging and library preparation on DNA metabarcoding
Bibliographic record
Abstract
Metabarcoding is increasingly used to assess species diversity by high-throughput sequencing where millions of sequences can be generated in parallel and multiple samples can be analysed in one sequencing run. Generating amplified fragments with a unique sequence identifier ('tag') for each sample is crucial, as it allows assigning sequences to the original samples. The tagging through so-called fusion primers is a fast and cheap alternative to commercially produced ligation-based kits. However, little is known about potential bias and inconsistencies introduced by the long nucleotide tail attached to those primers, which could lead to deficient reports of community composition in metabarcoding studies. We therefore tested the consistency and taxa detection efficiency of fusion primers in (1) a one-step and (2) two-step PCR protocol as well as (3) a commercially manufactured Illumina kit using mock communities of known composition. The Illumina kit delivered the most consistent results and detected the highest number of taxa. However, success of the two-step PCR approach was only marginally lower compared to the kit with the additional advantage of a much more competitive price per library. While most taxa were also detected with the one-step PCR approach, the consistency between replicates including read abundance was substantially lower. Our results highlight that method choice depends on the precision needed for analysis as well as on economic considerations and recommend the Illumina kit to obtain most accurate results and the two-step PCR approach as a much cheaper yet very robust alternative.
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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 | 0.025 | 0.071 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| 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".