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Record W1962512322 · doi:10.1139/bcb-2015-0053

Connecting the dots: chromatin and alternative splicing in EMT

2015· review· en· W1962512322 on OpenAlexaffvenue
Jessica A. Warns, James Davie, Archana Dhasarathy

Bibliographic record

VenueBiochemistry and Cell Biology · 2015
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicRNA modifications and cancer
Canadian institutionsChildren's Hospital Research Institute of Manitoba
FundersNational Institute of General Medical Sciences
KeywordsRNA splicingBiologyChromatinEpigeneticsAlternative splicingGeneticsExonic splicing enhancerSplicing factorExonDNA methylationCell biologyComputational biologyRNADNAGeneGene expression

Abstract

fetched live from OpenAlex

Nature has devised sophisticated cellular machinery to process mRNA transcripts produced by RNA Polymerase II, removing intronic regions and connecting exons together, to produce mature RNAs. This process, known as splicing, is very closely linked to transcription. Alternative splicing, or the ability to produce different combinations of exons that are spliced together from the same genomic template, is a fundamental means of regulating protein complexity. Similar to transcription, both constitutive and alternative splicing can be regulated by chromatin and its associated factors in response to various signal transduction pathways activated by external stimuli. This regulation can vary between different cell types, and interference with these pathways can lead to changes in splicing, often resulting in aberrant cellular states and disease. The epithelial to mesenchymal transition (EMT), which leads to cancer metastasis, is influenced by alternative splicing events of chromatin remodelers and epigenetic factors such as DNA methylation and non-coding RNAs. In this review, we will discuss the role of epigenetic factors including chromatin, chromatin remodelers, DNA methyltransferases, and microRNAs in the context of alternative splicing, and discuss their potential involvement in alternative splicing during the EMT process.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.988
Threshold uncertainty score0.760

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 head0.031
GPT teacher head0.321
Teacher spread0.289 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations28
Published2015
Admission routes2
Has abstractyes

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