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Record W4200246977 · doi:10.1016/j.celrep.2021.110176

Assessing kinetics and recruitment of DNA repair factors using high content screens

2021· article· en· W4200246977 on OpenAlexafffund
Bárbara Martínez-Pastor, Giórgia Gobbi da Silveira, Thomas L. Clarke, Dudley Chung, Yuchao Gu, Claudia Cosentino, Lance S. Davidow, Gadea Mata, Sylvana Hassanieh, Jayme Salsman, Alberto Ciccia, Narkhyun Bae, Mark T. Bedford, Diego Megı́as, Lee L. Rubin, Alejo Efeyan, Graham Dellaire, Raúl Mostoslavsky

Bibliographic record

VenueCell Reports · 2021
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicDNA Repair Mechanisms
Canadian institutionsBeatrice Hunter Cancer Research InstituteDalhousie University
FundersNational Institute of Environmental Health SciencesNational Institute of Diabetes and Digestive and Kidney DiseasesNatural Sciences and Engineering Research Council of CanadaBroad InstituteNational Institute of General Medical SciencesSimeon J. Fortin Charitable FoundationMassachusetts General HospitalNational Cancer InstituteNational Institutes of HealthCancer Prevention and Research Institute of Texas
KeywordsKineticsDNACell biologyChemistryBiologyComputational biologyBiochemistryPhysics

Abstract

fetched live from OpenAlex

Repair of genetic damage is coordinated in the context of chromatin, so cells dynamically modulate accessibility at DNA breaks for the recruitment of DNA damage response (DDR) factors. The identification of chromatin factors with roles in DDR has mostly relied on loss-of-function screens while lacking robust high-throughput systems to study DNA repair. In this study, we have developed two high-throughput systems that allow the study of DNA repair kinetics and the recruitment of factors to double-strand breaks in a 384-well plate format. Using a customized gain-of-function open-reading frame library ("ChromORFeome" library), we identify chromatin factors with putative roles in the DDR. Among these, we find the PHF20 factor is excluded from DNA breaks, affecting DNA repair by competing with 53BP1 recruitment. Adaptable for genetic perturbations, small-molecule screens, and large-scale analysis of DNA repair, these resources can aid our understanding and manipulation of DNA repair.

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)0.000
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 score0.011
Threshold uncertainty score0.545

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.084
GPT teacher head0.296
Teacher spread0.212 · 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

Citations26
Published2021
Admission routes2
Has abstractyes

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