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Record W2332689805 · doi:10.1093/gbe/evv069

Mutation Rates in Plastid Genomes: They Are Lower than You Might Think

2015· letter· en· W2332689805 on OpenAlexafffund
David Roy Smith

Bibliographic record

VenueGenome Biology and Evolution · 2015
Typeletter
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicPhotosynthetic Processes and Mechanisms
Canadian institutionsWestern University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsPlastidBiologyGenomeMitochondrial DNAMitochondrionMutation rateGeneticsEvolutionary biologyMutationChloroplastGene

Abstract

fetched live from OpenAlex

Within plastid-bearing species, the mutation rate of the plastid genome is often assumed to be greater than that of the mitochondrial genome. This assumption is based on early, pioneering studies of land plant molecular evolution, which uncovered higher rates of synonymous substitution in plastid versus mitochondrial DNAs. However, much of the plastid-containing eukaryotic diversity falls outside of land plants, and the patterns of plastid DNA evolution for embryophytes do not necessarily reflect those of other groups. Recent analyses of plastid and mitochondrial substitution rates in diverse lineages have uncovered very different trends than those recorded for land plants. Here, I explore these new data and argue that for many protists the plastid mutation rate is lower than that of the mitochondrion, including groups with primary or secondary plastids as well as nonphotosynthetic algae. These findings have far-reaching implications for how we view plastid genomes and how their sequences are used for evolutionary analyses, and might ultimately reflect a general tendency toward more efficient DNA repair mechanisms in plastids than in mitochondria.

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)0.004
metaresearch head score (Gemma)0.022
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.015
Threshold uncertainty score0.023

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.022
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0010.005
Scholarly communication0.0020.004
Open science0.0010.001
Research integrity0.0150.018
Insufficient payload (model declined to judge)0.0040.007

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 head0.010
GPT teacher head0.235
Teacher spread0.224 · 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 designTheoretical or conceptual
Domainnot available
GenreCommentary

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

Citations320
Published2015
Admission routes2
Has abstractyes

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