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Record W2045791565 · doi:10.1104/pp.108.121491

Genome-Wide Analysis of Transposon Insertion Polymorphisms Reveals Intraspecific Variation in Cultivated Rice  

2008· article· en· W2045791565 on OpenAlexfundno aff
Xuehui Huang, Guojun Lu, Qiang Zhao, Xiaohui Liu, Bin Han

Bibliographic record

VenuePLANT PHYSIOLOGY · 2008
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicChromosomal and Genetic Variations
Canadian institutionsnot available
FundersChinese Academy of SciencesMcGill University
KeywordsBiologyTransposable elementRetrotransposonGenomeGeneticsGeneOryza sativaGenome evolutionGenetic variationgenomic DNA

Abstract

fetched live from OpenAlex

Insertions and precise eliminations of transposable elements generated numerous transposon insertion polymorphisms (TIPs) in rice (Oryza sativa). We observed that TIPs represent more than 50% of large insertions and deletions (>100 bp) in the rice genome. Using a comparative genomic approach, we identified 2,041 TIPs between the genomes of two cultivars, japonica Nipponbare and indica 93-11. We also identified 691 TIPs between Nipponbare and indica Guangluai 4 in the 23-Mb collinear regions of chromosome 4. Among them, retrotransposon-based insertion polymorphisms were used to reveal the evolutionary relationships of these three cultivars. Our conservative estimates suggest that the TIPs generated approximately 14% of the genomic DNA sequence differences between subspecies indica and japonica. It was also found that more than 10% of TIPs were located in expressed gene regions, representing an important source of genetic variation. Transcript evidence implies that these TIPs induced a series of genetic differences between two subspecies, including interrupting host genes, creating different expression forms, drastically changing intron length, and affecting expression levels of adjacent genes. These analyses provide genome-wide insights into evolutionary history and genetic variation of rice.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.958
Threshold uncertainty score0.382

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.021
GPT teacher head0.200
Teacher spread0.179 · 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

Citations94
Published2008
Admission routes1
Has abstractyes

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