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Record W2951694241 · doi:10.1101/gr.237198.118

Nucleosome remodeling at origins of global genome–nucleotide excision repair occurs at the boundaries of higher-order chromatin structure

2018· article· en· W2951694241 on OpenAlexfundno aff
Patrick van Eijk, Shuvro P. Nandi, Shirong Yu, Mark K. Bennett, Matthew Leadbitter, Yumin Teng, Simon H. Reed

Bibliographic record

VenueGenome Research · 2018
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicDNA Repair Mechanisms
Canadian institutionsnot available
FundersMedical Research CouncilMedical Research Council CanadaBiotechnology and Biological Sciences Research CouncilParc Geneteg Cymru
KeywordsNucleosomeChromatinBiologyChromatin remodelingChromatin structure remodeling (RSC) complexGeneticsNucleotide excision repairCell biologyGenomeDNA repairDNAComputational biologyGene

Abstract

fetched live from OpenAlex

Repair of UV-induced DNA damage requires chromatin remodeling. How repair is initiated in chromatin remains largely unknown. We recently demonstrated that global genome-nucleotide excision repair (GG-NER) in chromatin is organized into domains in relation to open reading frames. Here, we define these domains, identifying the genomic locations from which repair is initiated. By examining DNA damage-induced changes in the linear structure of nucleosomes at these sites, we demonstrate how chromatin remodeling is initiated during GG-NER. In undamaged cells, we show that the GG-NER complex occupies chromatin, establishing the nucleosome structure at these genomic locations, which we refer to as GG-NER complex binding sites (GCBSs). We demonstrate that these sites are frequently located at genomic boundaries that delineate chromosomally interacting domains (CIDs). These boundaries define domains of higher-order nucleosome-nucleosome interaction. We demonstrate that initiation of GG-NER in chromatin is accompanied by the disruption of dynamic nucleosomes that flank GCBSs by the GG-NER complex.

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 score0.382
Threshold uncertainty score0.763

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.002
Scholarly communication0.0000.000
Open science0.0010.002
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.027
GPT teacher head0.334
Teacher spread0.307 · 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

Citations13
Published2018
Admission routes1
Has abstractyes

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