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Record W2161438622 · doi:10.1093/molbev/msm235

Intron Loss and Gain in Drosophila

2007· article· en· W2161438622 on OpenAlexafffund
Jasmin Coulombe‐Huntington, Jacek Majewski

Bibliographic record

VenueMolecular Biology and Evolution · 2007
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicRNA Research and Splicing
Canadian institutionsMcGill University
FundersCanada Research Chairs
KeywordsIntronBiologyRNA splicingGeneticsLineage (genetic)Drosophila melanogasterExonGenomeEvolutionary biologySplice site mutationGeneRNA

Abstract

fetched live from OpenAlex

Although introns were first discovered almost 30 years ago, their evolutionary origin remains elusive. In this work, we used multispecies whole-genome alignments to map Drosophila melanogaster introns onto 10 other fully sequenced Drosophila genomes. We were able to find 1,944 sites where an intron was missing in one or more species. We show that for most (>80%) of these cases, there is no leftover intronic sequence or any missing exonic sequence, indicating exact intron loss or gain events. We used parsimony to classify these differences as 1,754 intron loss events and 213 gain events. We show that lost and gained introns are significantly shorter than average and flanked by longer than average exons. They also display quite distinct phase distributions and show greater than average similarity between the 5' splice site and its 3' partner splice site. Introns that have been lost in one or more species evolve faster than other introns, occur in slowly evolving genes, and are found adjacent to each other more often than would be expected for independent single losses. Our results support the cDNA recombination mechanism of intron loss, suggest that selective pressures affect site-specific loss rates, and show conclusively that intron gain has occurred within the Drosophila lineage, solidifying the "introns-middle" hypothesis and providing some hints about the gain mechanism.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.563
Threshold uncertainty score0.330

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 head0.005
GPT teacher head0.278
Teacher spread0.273 · 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 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

Citations81
Published2007
Admission routes2
Has abstractyes

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