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Transcription–Replication Conflicts as a Source of Genome Instability

2023· review· en· W4385649227 on OpenAlexfundno aff
Liana Goehring, Tony T. Huang, Duncan J. Smith

Bibliographic record

VenueAnnual Review of Genetics · 2023
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicDNA Repair Mechanisms
Canadian institutionsnot available
FundersNational Institute of Environmental Health SciencesNational Institute of General Medical SciencesNational Institutes of HealthYork University
KeywordsBiologyGenome instabilityMulticellular organismGeneticsTranscription (linguistics)DNA replicationOrigin recognition complexChromatinControl of chromosome duplicationEukaryotic DNA replicationDNA re-replicationDNACell biologyGeneComputational biologyDNA damage

Abstract

fetched live from OpenAlex

Transcription and replication both require large macromolecular complexes to act on a DNA template, yet these machineries cannot simultaneously act on the same DNA sequence. Conflicts between the replication and transcription machineries (transcription-replication conflicts, or TRCs) are widespread in both prokaryotes and eukaryotes and have the capacity to both cause DNA damage and compromise complete, faithful replication of the genome. This review will highlight recent studies investigating the genomic locations of TRCs and the mechanisms by which they may be prevented, mitigated, or resolved. We address work from both model organisms and mammalian systems but predominantly focus on multicellular eukaryotes owing to the additional complexities inherent in the coordination of replication and transcription in the context of cell type-specific gene expression and higher-order chromatin organization.

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

Direct model labels (unvalidated)

Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.

Model armCategoriesStudy designConfidence
gemmano category
Domain: not available · Genre: Review
About the Canadian research system: no · About a Canadian topic: no
Not applicablelow
gptno category
Domain: not available · Genre: Review
About the Canadian research system: no · About a Canadian topic: no
Theoretical or conceptualhigh
models splitAgreement compares identical category sets and study designs across arms.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.975
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.041
GPT teacher head0.346
Teacher spread0.305 · 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

Labeled directly by 2 models reading the full record.

The models applied no category: nothing in the taxonomy fit this work.

The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.

Study designNot applicable · Theoretical or conceptual
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

Citations71
Published2023
Admission routes1
Has abstractyes

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