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Record W2791424786 · doi:10.1038/s41598-018-29364-z

Slippage of degenerate primers can cause variation in amplicon length

2018· article· en· W2791424786 on OpenAlexafffund
Vasco Elbrecht, Paul D. N. Hebert, Dirk Steinke

Bibliographic record

VenueScientific Reports · 2018
Typearticle
Languageen
FieldEnvironmental Science
TopicEnvironmental DNA in Biodiversity Studies
Canadian institutionsUniversity of Guelph
FundersCanada First Research Excellence FundEuropean Cooperation in Science and Technology
KeywordsPrimer (cosmetics)AmpliconBiologySlippageGeneticsComputational biologyPolymerase chain reactionGeneChemistry

Abstract

fetched live from OpenAlex

It is well understood that homopolymer regions should be avoided for primer binding to prevent off-target amplification. However, in metabarcoding, it is often difficult to avoid primer degeneracy in order to maximize taxa detection. We here investigate primer binding specificity using different primer sets from several invertebrate metabarcoding studies. Our results indicate that primers frequently bound 1-2 bp upstream in taxa where a homopolymer region was present in the amplification direction. Primer binding 1 bp downstream was observed less frequently. This primer slippage leads to taxon-specific length variation in amplicons and subsequent length variation in recovered sequences. Some widely used primer sets were severely affected by this bias, while others were not. While this variation will only have small impacts on the designation of Operational Taxonomic Units (OTUs) by clustering algorithms that ignore terminal gaps, primer sets employed in metabarcoding projects should be evaluated for their sensitivity to slippage. Moreover, steps should be taken to reduce slippage by improving protocols for primer design. For example, the flanking region adjacent to the 3' end of the primer is not considered by current primer development software although GC clamps in this position could mitigate slippage.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.153
Threshold uncertainty score0.866

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.016
GPT teacher head0.219
Teacher spread0.203 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations40
Published2018
Admission routes2
Has abstractyes

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