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Record W2908217616 · doi:10.15698/mic2019.01.665

Guidelines for DNA recombination and repair studies: Mechanistic assays of DNA repair processes

2019· review· en· W2908217616 on OpenAlexaff
Hannah L. Klein, Kenny K. H. Ang, Michelle R. Arkin, Emily C. Beckwitt, Yi-Hsuan Chang, Jun Fan, Youngho Kwon, Michael J. Morten, Sucheta Mukherjee, Oliver J. Pambos, Hafez El Sayyed, Elizabeth S. Thrall, João P. Vieira‐da‐Rocha, Quan Wang, Shuang Wang, Hsin‐Yi Yeh, Julie S. Biteen, Peter Chi, Wolf‐Dietrich Heyer, Achillefs N. Kapanidis, Joseph J. Loparo, Terence R. Strick, Patrick Sung, Bennett Van Houten, Hengyao Niu, Eli Rothenberg

Bibliographic record

VenueMicrobial Cell · 2019
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicDNA Repair Mechanisms
Canadian institutionsDiscovery Centre
FundersNational Institute of Environmental Health SciencesNational Institute of General Medical SciencesBiotechnology and Biological Sciences Research CouncilNational Cancer InstituteNational Institutes of HealthAgence Nationale de la RechercheWellcome TrustU.S. Department of Defense
KeywordsDNA repairMutagenesisDNAHomologous recombinationComputational biologyRecombinationBiologyGeneticsChemistryMutationGene

Abstract

fetched live from OpenAlex

Guidelines for DNA recombination and repair studies: Mechanistic assays of DNA repair processes – Klein et al. Genomes are constantly in flux, undergoing changes due to recombination, repair and mutagenesis. In vivo, many of such changes are studies using reporters for specific types of changes, or through cytological studies that detect changes at the single-cell level. Single molecule assays, which are reviewed here, can detect transient intermediates and dynamics of events. Biochemical assays allow detailed investigation of the DNA and protein activities of each step in a repair, recombination or mutagenesis event. Each type of assay is a powerful tool but each comes with its particular advantages and limitations. Here the most commonly used assays are reviewed, discussed, and presented as the guidelines for future studies.

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)0.004
metaresearch head score (Gemma)0.004
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: Methods · Consensus signal: none
Teacher disagreement score0.014
Threshold uncertainty score0.046

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.004
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0030.002
Bibliometrics0.0070.006
Science and technology studies0.0010.002
Scholarly communication0.0020.004
Open science0.0040.002
Research integrity0.0040.006
Insufficient payload (model declined to judge)0.0140.028

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 head0.090
GPT teacher head0.359
Teacher spread0.269 · 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
GenreMethods

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

Citations11
Published2019
Admission routes1
Has abstractyes

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