MétaCan
Menu
Back to cohort
Record W4395070678 · doi:10.1261/rna.079890.123

Mapping RNA-Protein Interactions with Subcellular Resolution Using Colocalization CLIP

2024· article· en· W4395070678 on OpenAlexaff
Soon Yi, Shashi Singh, Kathryn Rozen-Gagnon, Joseph M. Luna

Bibliographic record

VenueRNA · 2024
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicBiotin and Related Studies
Canadian institutionsUniversity of Toronto
FundersCase Western Reserve UniversityNational Institutes of HealthNational Cancer InstituteNational Institute of General Medical SciencesCase Comprehensive Cancer Center, Case Western Reserve UniversitySchool of Medicine, Case Western Reserve University
KeywordsColocalizationBiologyRNA-binding proteinRNAStress granuleSubcellular localizationImmunoprecipitationComputational biologyCell biologyTranslation (biology)GeneticsMessenger RNAGeneCytoplasm

Abstract

fetched live from OpenAlex

RNA-binding proteins (RBPs) are essential for RNA metabolism and profoundly impact health and disease. The subcellular organization of RBP interaction networks with target RNAs remains largely unexplored. Here, we develop colocalization CLIP (coCLIP), a method that combines cross-linking and immunoprecipitation (CLIP) with proximity labeling, to explore in-depth the subcellular RNA interactions of the RBP human antigen R (HuR). Using this method, we uncover HuR's dynamic and location-specific interactions with RNA, revealing alterations in sequence preferences and interactions in the nucleus, cytosol, or stress granule (SG) compartments. We uncover HuR's unique binding preferences within SGs during arsenite stress, illuminating intricate interactions that conventional methodologies cannot capture. Overall, coCLIP provides a powerful method for revealing RBP-RNA interactions based on localization and lays the foundation for an advanced understanding of RBP models that incorporate subcellular location as a critical determinant of their functions.

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 score0.206
Threshold uncertainty score0.259

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 head0.016
GPT teacher head0.256
Teacher spread0.240 · 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

Citations3
Published2024
Admission routes1
Has abstractyes

Explore more

Same venueRNASame topicBiotin and Related StudiesFrench-language works237,207