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Record W4309264060 · doi:10.1016/j.xpro.2022.101861

Protocol to measure end resection intermediates at sequence-specific DNA double-strand breaks by quantitative polymerase chain reaction using ER-AsiSI U2OS cells

2022· article· en· W4309264060 on OpenAlexafffund
Ajit Kumar Sharma, Amira Fitieh, Jana Yasser Hafez Ali, Ismail Hassan Ismail

Bibliographic record

VenueSTAR Protocols · 2022
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCRISPR and Genetic Engineering
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of CanadaInstitute of Circulatory and Respiratory HealthCanadian Institutes of Health ResearchCancer Research Society
KeywordsPolymerase chain reactionChemistryDNAMolecular biologySequence (biology)PolymeraseBiologyBiochemistryGene

Abstract

fetched live from OpenAlex

DNA end resection is a critical step in the homologous recombination pathway of repairing DNA double-strand breaks (DSBs) that can be visualized in cells by detecting the generation of single-stranded DNA (ssDNA) intermediates formed during the resection of the DSBs. Here, we describe quantitative polymerase-chain-reaction-based procedures to quantitatively measure ssDNA intermediates formed during the DNA end resection. Using the ER-A si SI system, we use differential digestion patterns by restriction endonucleases that digest unresected double-stranded DNA at DSB sites. For complete details on the use and execution of this protocol, please refer to Fitieh et al. (2022). 1

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Protocol · Consensus signal: none
Teacher disagreement score0.030
Threshold uncertainty score0.101

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0040.003
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0040.002
Science and technology studies0.0020.001
Scholarly communication0.0010.001
Open science0.0030.001
Research integrity0.0020.008
Insufficient payload (model declined to judge)0.0300.025

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 head0.050
GPT teacher head0.369
Teacher spread0.318 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreProtocol

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

Citations4
Published2022
Admission routes2
Has abstractyes

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