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Record W2891974017 · doi:10.1074/mcp.ra118.000593

The Protein Coded by a Short Open Reading Frame, Not by the Annotated Coding Sequence, Is the Main Gene Product of the Dual-Coding Gene MIEF1

2018· article· en· W2891974017 on OpenAlexafffund
Vivian Delcourt, Mylène Brunelle, Annie V. Roy, Jean‐François Jacques, Michel Salzet, Isabelle Fournier, Xavier Roucou

Bibliographic record

VenueMolecular & Cellular Proteomics · 2018
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicRNA and protein synthesis mechanisms
Canadian institutionsPROTEOUniversité de Sherbrooke
FundersCanadian Institutes of Health ResearchCanada Research Chairs
KeywordsProteogenomicsOpen reading frameGeneORFSBiologyCoding regionRibosome profilingComputational biologyGeneticsPeptide sequenceTranslation (biology)Gene expressionTranscriptomeMessenger RNA

Abstract

fetched live from OpenAlex

Proteogenomics and ribosome profiling concurrently show that genes may code for both a large and one or more small proteins translated from annotated coding sequences (CDSs) and unannotated alternative open reading frames (named alternative ORFs or altORFs), respectively, but the stoichiometry between large and small proteins translated from a same gene is unknown. MIEF1 , a gene recently identified as a dual-coding gene, harbors a CDS and a newly annotated and actively translated altORF located in the 5′UTR. Here, we use absolute quantification with stable isotope-labeled peptides and parallel reaction monitoring to determine levels of both proteins in two human cells lines and in human colon. We report that the main MIEF1 translational product is not the canonical 463 amino acid MiD51 protein but the small 70 amino acid alternative MiD51 protein (altMiD51). These results demonstrate the inadequacy of the single CDS concept and provide a strong argument for incorporating altORFs and small proteins in functional annotations.

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)0.001
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 score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.002

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 head0.017
GPT teacher head0.248
Teacher spread0.231 · 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

Citations67
Published2018
Admission routes2
Has abstractyes

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