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Record W2990560198 · doi:10.1101/847160

Strand asymmetry influences mismatch resolution during single-strand annealing

2019· preprint· en· W2990560198 on OpenAlexaff
Victoria O. Pokusaeva, Aránzazu Rosado Diez, Lorena Espinar, Guillaume J. Filion

Bibliographic record

VenuebioRxiv (Cold Spring Harbor Laboratory) · 2019
Typepreprint
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenomics and Chromatin Dynamics
Canadian institutionsUniversity of Toronto
FundersMinisterio de Asuntos Económicos y Transformación Digital, Gobierno de EspañaMinisterio de Economía y CompetitividadGeneralitat de CatalunyaEuropean CommissionCentres de Recerca de Catalunya
KeywordsChromatinBiologyGenomeDNAGeneticsComputational biologyGene

Abstract

fetched live from OpenAlex

ABSTRACT Background Biases of DNA repair can shape the nucleotide landscape of genomes at evolutionary timescales. However, such biases have not yet been measured in chromatin for lack of technologies. Results Here we develop a genome-wide assay whereby the same DNA lesion is repaired in different chromatin contexts. We insert thousands of barcoded transposons carrying a reporter of DNA mismatch repair in the genome of mouse embryonic stem cells. Upon inducing a double-strand break between tandem repeats, a mismatch is generated when the single strand annealing repair pathway is used. Regardless of the mismatch, we observed a 60-80% bias in the resolution in favor of one strand. The location of the lesion in the genome and the type of mismatch had little influence on the bias in this context. Instead, changing the position of the double-strand break in the reporter gave a complete reversion of the bias. Conclusion These results suggest that the processing of the double-strand break has a major influence on the repair of mismatches during single-strand annealing, irrespective of the surrounding chromatin context.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.009
GPT teacher head0.205
Teacher spread0.196 · 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 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

Citations2
Published2019
Admission routes1
Has abstractyes

Explore more

Same venuebioRxiv (Cold Spring Harbor Laboratory)→Same topicGenomics and Chromatin Dynamics→French-language works237,207→