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Record W2898294741 · doi:10.1093/nar/gky914

A new full-length circular DNA sequencing method for viral-sized genomes reveals that RNAi transgenic plants provoke a shift in geminivirus populations in the field

2018· article· en· W2898294741 on OpenAlexaff
Devang Mehta, Matthias Hirsch‐Hoffmann, Mariam Were, Andrea Patrignani, Syed Shan‐e‐Ali Zaidi, Hassan Karakacha Wéré, Wilhelm Gruissem, Hervé Vanderschuren

Bibliographic record

VenueNucleic Acids Research · 2018
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant Virus Research Studies
Canadian institutionsUniversity of Alberta
FundersMasinde Muliro University of Science and TechnologySeventh Framework ProgrammeEidgenössische Technische Hochschule Zürich
KeywordsBiologyGenomeGeneticsTransgeneDNA sequencingComputational biologyDNARNA interferenceRNA silencingDeep sequencingVirusRNAVirologyGene

Abstract

fetched live from OpenAlex

We present a new method, CIDER-Seq (Circular DNA Enrichment sequencing) for the unbiased enrichment and long-read sequencing of viral-sized circular DNA molecules. We used CIDER-Seq to produce single-read full-length virus genomes for the first time. CIDER-Seq combines PCR-free virus enrichment with Single Molecule Real Time sequencing and a new sequence de-concatenation algorithm. We apply our technique to produce >1200 full-length, highly accurate geminivirus genomes from RNAi-transgenic and control plants in a field trial in Kenya. Using CIDER-Seq we can demonstrate for the first time that the expression of antiviral double-stranded RNA (dsRNA) in transgenic plants causes a consistent shift in virus populations towards species sharing low homology to the transgene derived dsRNA. Our method and its application in an economically important crop plant opens new possibilities in periodic virus sequence surveillance and accurate profiling of diverse circular DNA elements.

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)0.005
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.791
Threshold uncertainty score0.689

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.199
GPT teacher head0.405
Teacher spread0.206 · 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 designBench or experimental
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

Citations34
Published2018
Admission routes1
Has abstractyes

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