Proteogenomics-enabled discovery of novel small open reading frame (sORF)-encoded polypeptides in human and mouse tissues is a research paper published in Nucleic Acids Research (2025). On theSindex it has a DataRank of 0.104. It has been cited 1 time. Its calibrated FAIR score is 13/100.
Small open reading frames (sORFs) encode an emerging class of functional proteins less than 100 amino acids in length. However, sORFs are incompletely characterized in mice and humans. The development of proteomics and Ribo-seq techniques has enabled the discovery of a number of sORF-encoded peptides (SEPs), but previous proteogenomics studies have been limited to a few cell lines or tissues. Given these limitations, a potentially vast number of sORFs remains to be discovered. We collected community-scale previously published proteomics data including one billion experimental spectra derived from a wide range of mouse and human tissues in order to identify novel sORFs and reveal the tissue expression status of novel and recently annotated sORF-encoded proteins. We have detected several novel sORFs in specific tissues, including a conserved protein-coding upstream overlapping ORF in HNRNPUL2 expressed in human lymphocytes, which may hold important biological functions. This work introduces a simple and efficient filtration strategy to detect novel sORFs. Our workflow will likely prove useful for future studies on sORFs in humans and other animals.
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Base Score Contribution
0.104
From this paper's citation signal
Citation Network Contribution
0
Citation network not refreshed for this result
This paper's DataRank is currently driven only by its base citation score. Citation network data was not refreshed for this result.
Learn more about DataRank methodology →DataRank blends this paper's own citation count with the influence of the papers that cite it. Here, roughly 100% comes from its base citations and 0% from the citation network.
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.