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Record W4394354991 · doi:10.6084/m9.figshare.5895559

DNA polymerase <b>η</b> mutational signatures are found in a variety of different types of cancer

2023· dataset· en· W4394354991 on OpenAlexaff
Igor B. Rogozin, Alexander Goncearenco, Artem G. Lada, Subhajyoti De, Vyacheslav Yurchenko, German Nudelman, Anna R. Panchenko, D.N. Cooper, Youri I. Pavlov

Bibliographic record

VenueFigshare · 2023
Typedataset
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCancer Genomics and Diagnostics
Canadian institutionsQueen's University
Fundersnot available
KeywordsDNA polymeraseGeneticsBiologyPolymeraseComputational biologyDNACancerMolecular biology

Abstract

fetched live from OpenAlex

DNA polymerase (pol) η is a specialized error-prone polymerase with at least two quite different and contrasting cellular roles: to mitigate the genetic consequences of solar UV irradiation, and promote somatic hypermutation in the variable regions of immunoglobulin genes. Misregulation and mistargeting of pol η can compromise genome integrity. We explored whether the mutational signature of pol η could be found in datasets of human somatic mutations derived from normal and cancer cells. A substantial excess of single and tandem somatic mutations within known pol η mutable motifs was noted in skin cancer as well as in many other types of human cancer, suggesting that somatic mutations in A:T bases generated by DNA polymerase η are a common feature of tumorigenesis. Another peculiarity of pol ηmutational signatures, mutations in YCG motifs, led us to speculate that error-prone DNA synthesis opposite methylated CpG dinucleotides by misregulated pol η in tumors might constitute an additional mechanism of cytosine demethylation in this hypermutable dinucleotide.

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)0.001
metaresearch head score (Gemma)0.007
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: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.019
Threshold uncertainty score0.063

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.007
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0020.005
Science and technology studies0.0010.000
Scholarly communication0.0020.001
Open science0.0020.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0190.015

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 head0.017
GPT teacher head0.283
Teacher spread0.266 · 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
GenreDataset

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

Citations0
Published2023
Admission routes1
Has abstractyes

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