MétaCan
Menu
Back to cohort
Record W2807769204 · doi:10.1038/s41467-018-04562-5

High-resolution crossover mapping reveals similarities and differences of male and female recombination in maize

2018· article· en· W2807769204 on OpenAlexaff
Penny M. A. Kianian, Minghui Wang, Kristin Simons, Farhad Ghavami, Yan He, Stefanie Dukowic‐Schulze, Anitha Sundararajan, Qi Sun, Jarosław Pillardy, Joann Mudge, Changbin Chen, Shahryar F. Kianian, Wojciech P. Pawlowski

Bibliographic record

VenueNature Communications · 2018
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenetic Mapping and Diversity in Plants and Animals
Canadian institutionsNational Research Council Institute for Biodiagnostics
FundersNational Science Foundation
KeywordsH3K4me3BiologyNucleosomeGeneticsChromatinRecombinationGeneEvolutionary biologyMeiosisHistoneGenomePopulationEpigeneticsPromoterGene expression

Abstract

fetched live from OpenAlex

Meiotic crossovers (COs) are not uniformly distributed across the genome. Factors affecting this phenomenon are not well understood. Although many species exhibit large differences in CO numbers between sexes, sex-specific aspects of CO landscape are particularly poorly elucidated. Here, we conduct high-resolution CO mapping in maize. Our results show that CO numbers as well as their overall distribution are similar in male and female meioses. There are, nevertheless, dissimilarities at local scale. Male and female COs differ in their locations relative to transcription start sites in gene promoters and chromatin marks, including nucleosome occupancy and tri-methylation of lysine 4 of histone H3 (H3K4me3). Our data suggest that sex-specific factors not only affect male-female CO number disparities but also cause fine differences in CO positions. Differences between male and female CO landscapes indicate that recombination has distinct implications for population structure and gene evolution in male and in female meioses.

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 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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.284
Threshold uncertainty score0.219

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.024
GPT teacher head0.266
Teacher spread0.242 · 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 teacher head, not a consensus.

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

Citations105
Published2018
Admission routes1
Has abstractyes

Explore more

Same venueNature CommunicationsSame topicGenetic Mapping and Diversity in Plants and AnimalsFrench-language works237,207