Suppression/competition PCR: A novel method to minimize unwanted amplicons in metabarcoding, with applications to parasite detection in fecal samples
Bibliographic record
Abstract
Metabarcoding is widely used for detecting microorganisms in fecal samples, but its effectiveness is often limited by the co-amplification of abundant non-target DNA. In this study, a novel metabarcoding assay was developed to amplify a near-complete 18S rRNA gene fragment suitable for long-read nanopore sequencing, enhancing taxonomic resolution. The primers were optimized to maximize detection of parasitic taxa while minimizing off-target amplification of bacterial and archaeal sequences, thereby improving assay specificity. In this study, the 18S metabarcoding assay worked well on clinical fecal samples containing clinically relevant levels of parasites. However, analysis of ungulate fecal samples revealed that fungal and plant sequences vastly outnumbered other eukaryotic taxa in many samples, obscuring the detection of low-abundance protozoan and helminth parasites. To address this, Suppression/Competition PCR was developed, a novel method that selectively reduces amplification of unwanted DNA. This approach reduced fungal and plant reads by over 99 %, enabling sequences from other taxa to comprise an average of over 98 % of total reads as opposed to an initial 36 %. Utilizing this newly-developed metabarcoding assay in either the standard or Suppression/Competition configuration on fecal DNA extracts from a range of host species, parasites of interest such as Cryptosporidium sp., Cyclospora cayetanensis, Blastocystis sp., Entamoeba sp., Eimeria sp., Ancylostoma sp., and Toxocara sp. were detected, demonstrating its broad applicability.
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".