MétaCan
Menu
Back to cohort
Record W2320670917 · doi:10.1101/pdb.prot080234

High-Throughput RNAi Screening for Germline Apoptosis Genes in <i>Caenorhabditis elegans</i>

2014· article· en· W2320670917 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueCold Spring Harbor Protocols · 2014
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenetics, Aging, and Longevity in Model Organisms
Canadian institutionsUniversity of TorontoHospital for Sick Children
FundersCanadian Institutes of Health Research
KeywordsCaenorhabditis elegansRNA interferenceGermlineBiologyGeneAcridine orangePhenotypeMutantApoptosisGeneticsCell biologyTransgeneGenomeComputational biologyRNA

Abstract

fetched live from OpenAlex

Among the greatest tools that Caenorhabditis elegans can provide researchers are the capabilities to perform high-throughput, genome-wide screens. Using bacterial RNAi libraries, which cover the majority (>85%) of the worm genome, genes can be rapidly and systematically evaluated for apoptosis phenotypes in the germline. Screens can be designed to directly assess the levels of apoptotic corpses under normal physiological conditions using transgenic strains expressing fluorescent reporters that mark apoptotic bodies. Vital dyes that are selectively retained in apoptotic cells, such as acridine orange (AO), can also be used to screen for genes that regulate germline apoptosis. Using these reagents, screens can be performed in wild-type worms or mutant backgrounds that suppress or enhance apoptosis phenotypes. This protocol describes methods for designing and carrying out high-throughput or targeted RNAi screens for germline apoptosis regulators.

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.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
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 score0.350
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.016
GPT teacher head0.262
Teacher spread0.246 · 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