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Record W4210660522 · doi:10.1111/mec.16377

The importance of alternative splicing in adaptive evolution

2022· article· en· W4210660522 on OpenAlexaff
Pooja Singh, Ehsan Pashay Ahi

Bibliographic record

VenueMolecular Ecology · 2022
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicRNA Research and Splicing
Canadian institutionsUniversity of Calgary
FundersKarl-Franzens-Universität GrazMedizinische Universität GrazUniversity of Bern
KeywordsBiologyAdaptive evolutionEvolutionary biologyAlternative splicingComputational biologyGeneticsGeneExon

Abstract

fetched live from OpenAlex

Although alternative splicing is a ubiquitous co-transcriptional gene regulatory mechanism in plants, animals and fungi, its contribution to evolutionary transitions is understudied. Alternative splicing enables different mRNA isoforms to be generated from the same gene, expanding transcriptomic and thus proteomic diversity. While the role of gene expression variation in adaptive evolution is widely accepted, biologists still debate the functional impact of alternative isoforms on phenotype. In light of recent empirical research linking splice variation to ecological adaptations, we propose that alternative splicing is an important substrate for adaptive evolution and speciation, particularly at short timescales. In this article we synthesise what is known about the role of alternative splicing in adaptive evolution. We discuss the contribution of standing splice variation to phenotypic plasticity and how hybridisation can produce novel splice forms. Going forwards, we propose that alternative splicing be included as a standard analysis alongside gene expression analysis so we can better understand of how alternative splicing contributes to adaptive divergence at the micro- and macroevolutionary levels.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.292
Threshold uncertainty score0.255

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 head0.008
GPT teacher head0.262
Teacher spread0.253 · 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

Citations137
Published2022
Admission routes1
Has abstractyes

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