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Record W2662175080 · doi:10.1002/pmic.201700058

Small Proteins Encoded by Unannotated ORFs are Rising Stars of the Proteome, Confirming Shortcomings in Genome Annotations and Current Vision of an mRNA

2017· review· en· W2662175080 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

VenuePROTEOMICS · 2017
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicRNA and protein synthesis mechanisms
Canadian institutionsPROTEOUniversité de Sherbrooke
FundersCanadian Institutes of Health ResearchCanada Research Chairs
KeywordsORFSBiologyProteomeRibosome profilingProteogenomicsGenomeComputational biologyProteomicsGeneticsHuman proteome projectOpen reading frameAnnotationCoding regionRibosomeGeneGenomicsRNAPeptide sequence

Abstract

fetched live from OpenAlex

Short ORF-encoded peptides and small proteins in eukaryotes have been hiding in the shadow of large proteins for a long time. Recently, improved identifications in MS-based proteomics and ribosome profiling resulted in the detection of large numbers of small proteins. The variety of functions of small proteins is also emerging. It seems to be the right time to reflect on why small proteins remained invisible. In addition to the obvious technical challenge of detecting small proteins, they were mostly forgotten from annotations and they escaped detection because they were not sought. In this review, we identify conventions that need to be revisited, including the assumption that mature mRNAs carry only one coding sequence. The large-scale discovery of small proteins and of their functions will require changing some paradigms and undertaking the annotation of ORFs that are still largely perceived as irrelevant coding information compared to already annotated coding sequences.

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: Review · Consensus signal: Review
Teacher disagreement score0.398
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.060
GPT teacher head0.333
Teacher spread0.272 · 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