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Record W2750569114 · doi:10.1098/rstb.2016.0280

Reading chromatin signatures after DNA double-strand breaks

2017· review· en· W2750569114 on OpenAlexafffund
Marcus D. Wilson, Daniel Durocher

Bibliographic record

VenuePhilosophical Transactions of the Royal Society B Biological Sciences · 2017
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicDNA Repair Mechanisms
Canadian institutionsLunenfeld-Tanenbaum Research InstituteUniversity of TorontoMount Sinai Hospital
FundersCanadian Institutes of Health ResearchKrembil FoundationHuman Frontier Science Program
KeywordsChromatinHistoneNucleosomeBiologyCell biologyDNADNA repairDNA damageSignallingChromatin remodelingComputational biologyGenetics

Abstract

fetched live from OpenAlex

DNA double-strand breaks (DSBs) are DNA lesions that must be accurately repaired in order to preserve genomic integrity and cellular viability. The response to DSBs reshapes the local chromatin environment and is largely orchestrated by the deposition, removal and detection of a complex set of chromatin-associated post-translational modifications. In particular, the nucleosome acts as a central signalling hub and landing platform in this process by organizing the recruitment of repair and signalling factors, while at the same time coordinating repair with other DNA-based cellular processes. While current research has provided a descriptive overview of which histone marks affect DSB repair, we are only beginning to understand how these marks are interpreted to foster an efficient DSB response. Here we review how the modified chromatin surrounding DSBs is read, with a focus on the insights gleaned from structural and biochemical studies.This article is part of the themed issue 'Chromatin modifiers and remodellers in DNA repair and signalling'.

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)0.000
metaresearch head score (Gemma)0.001
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: Review · Consensus signal: Review
Teacher disagreement score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.002

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 head0.083
GPT teacher head0.339
Teacher spread0.256 · 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
GenreReview

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

Citations60
Published2017
Admission routes2
Has abstractyes

Explore more

Same venuePhilosophical Transactions of the Royal Society B Biological SciencesSame topicDNA Repair MechanismsFrench-language works237,207