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Record W4410005197 · doi:10.1242/jcs.263483

Focus on numbers – characterizing protein accumulation at DNA double-strand breaks

2025· article· en· W4410005197 on OpenAlexafffund
Graziana Modica, Joannie Roy, Antoine G. Godin, Hugo Würtele, Santiago Costantino

Bibliographic record

VenueJournal of Cell Science · 2025
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicDNA Repair Mechanisms
Canadian institutionsCegep Edouard MontpetitUniversité LavalInstitut Universitaire en Santé Mentale de QuébecUniversité de MontréalHôpital Maisonneuve-Rosemont
FundersSentinelle Nord, Université LavalFonds de Recherche du Québec - SantéNatural Sciences and Engineering Research Council of CanadaCancer Research Society
KeywordsBiologyDNADNA damageDNA repairComputational biologyCell biologyPipeline (software)BiophysicsBiological systemGeneticsComputer science

Abstract

fetched live from OpenAlex

Unrepaired DNA double-strand breaks can lead to cell death or genomic rearrangements. The DNA damage response (DDR) is a complex signaling cascade in which a plethora of factors act to finely tune repair pathway choice. Several DDR proteins have been shown to accumulate at sites of DNA lesions in characteristic dot-like structures known as DNA repair foci. Changes in foci brightness, commonly expressed in arbitrary intensity units, are often used as readout for DNA repair dynamics. However, due in part to technical challenges, the stoichiometry, absolute number of proteins recruited to DDR foci, and their impact on the resolution of the break remain incompletely characterized. Here, we combine spatial intensity distribution analysis (SpIDA) and a custom foci detection algorithm into an easy-to-use pipeline that, starting from confocal images, allows quantitative description of protein accumulation in DNA repair foci. Moreover, by quantifying foci based on their molecular count, SpIDA overcomes the limitations of ambiguous intensity units, enabling stoichiometric quantification between repair factors and providing a unifying means for experimental comparisons.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.284

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.014
GPT teacher head0.280
Teacher spread0.266 · 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

Citations0
Published2025
Admission routes2
Has abstractyes

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