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Record W2136384144 · doi:10.1007/s13238-014-0071-y

Analysis of the p53/CEP-1 regulated non-coding transcriptome in C. elegans by an NSR-seq strategy

2014· article· en· W2136384144 on OpenAlexfundno aff
Guifeng Wei, Ping Lü, Jianjun Luo, Geir Skogerbø, Runsheng Chen

Bibliographic record

VenueProtein & Cell · 2014
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenetics, Aging, and Longevity in Model Organisms
Canadian institutionsnot available
FundersInstitute of GeneticsInstitute of Genetics and Developmental Biology, Chinese Academy of SciencesChinese Academy of Sciences
KeywordsBiologyTranscriptomeGeneRNACaenorhabditis elegansGeneticsPromoterTranscription (linguistics)Non-coding RNAComputational biologyLong non-coding RNATranscription factorGene expression

Abstract

fetched live from OpenAlex

In recent years, large numbers of non-coding RNAs (ncRNAs) have been identified in C. elegans but their functions are still not well studied. In C. elegans, CEP-1 is the sole homolog of the p53 family of genes. In order to obtain transcription profiles of ncRNAs regulated by CEP-1 under normal and UV stressed conditions, we applied the 'not-so-random' hexamers priming strategy to RNA sequencing in C. elegans, This NSR-seq strategy efficiently depleted rRNA transcripts from the samples and showed high technical replicability. We identified more than 1,000 ncRNAs whose apparent expression was repressed by CEP-1, while around 200 were activated. Around 40% of the CEP-1 activated ncRNAs promoters contain a putative CEP-1-binding site. CEP-1 regulated ncRNAs were frequently clustered and concentrated on the X chromosome. These results indicate that numerous ncRNAs are involved in CEP-1 transcriptional network and that these are especially enriched on the X chromosome in C. elegans.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

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 integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.006
GPT teacher head0.211
Teacher spread0.205 · 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 source (direct Gemma or distilled Codex), 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

Citations6
Published2014
Admission routes1
Has abstractyes

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