Standardized annotation of translated open reading frames is a dataset published in Nature Biotechnology (2022). On theSindex it has a DataRank of 3.5, placing it in the top 31.2% of the data-sharing corpus. It has been cited 245 times, with 136 citing works in its 1-hop citation network. Its calibrated FAIR score is 37/100.
Ribosome profiling (Ribo-seq) has extended our understanding of the translational ‘vocabulary’ of the human genome, uncovering thousands of open reading frames (ORFs) within long noncoding RNAs (lncRNAs) and presumed untranslated regions (UTRs) of protein-coding genes. However, reference gene annotation projects have been circumspect in their incorporation of these ORFs because of uncertainties about their experimental reproducibility and physiological roles. Yet, it is clear that certain ‘Ribo-seq ORFs’ make stable proteins, others mediate gene regulation, and many have medical implications. Ultimately, the absence of standardized ORF annotation has created a circular problem: while Ribo-seq ORFs remain unrecognized by reference annotation databases, this lack of recognition will thwart studies examining their roles. Here, we outline a community-led effort involving Ensembl/GENCODE, the HUGO Gene Nomenclature Committee (HGNC), UniProtKB, HUPO/HPP and PeptideAtlas to produce a standardized catalog of 7,264 human Ribo-seq ORFs; a path to bring protein-level evidence for Ribo-seq ORFs into reference annotation databases; and a roadmap to facilitate research in the global community.
FAIR checklist signals are shown for context only and do not affect DataRank scoring.
Calibrated FAIR score — a parallel quality metric, independent of the DataRank citation score. See the full evaluation →
Base Score Contribution
0.817
From this paper's citation signal
Citation Network Contribution
2.7
From 131 citing papers with measurable signal
Ranked by citation count — the same ordering the engine uses when summing log1p(Cq) over citers.
DataRank blends this paper's own citation count with the influence of the papers that cite it. Here, roughly 23% comes from its base citations and 77% from the citation network (131 citing papers contributed measurable signal).
Citers are pulled from OpenAlex sorted by cited_by_count:descand capped per paper, so when the cap binds we keep the highest-signal references and the score is reproducible across reruns.
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