uORFs: Important Cis-Regulatory Elements in Plants is a research paper published in International Journal of Molecular Sciences (2020). On theSindex it has a DataRank of 2.0. It has been cited 68 times, with 66 citing works in its 1-hop citation network.
Gene expression is regulated at many levels, including mRNA transcription, translation, and post-translational modification. Compared with transcriptional regulation, mRNA translational control is a more critical step in gene expression and allows for more rapid changes of encoded protein concentrations in cells. Translation is highly regulated by complex interactions between cis-acting elements and trans-acting factors. Initiation is not only the first phase of translation, but also the core of translational regulation, because it limits the rate of protein synthesis. As potent cis-regulatory elements in eukaryotic mRNAs, upstream open reading frames (uORFs) generally inhibit the translation initiation of downstream major ORFs (mORFs) through ribosome stalling. During the past few years, with the development of RNA-seq and ribosome profiling, functional uORFs have been identified and characterized in many organisms. Here, we review uORF identification, uORF classification, and uORF-mediated translation initiation. More importantly, we summarize the translational regulation of uORFs in plant metabolic pathways, morphogenesis, disease resistance, and nutrient absorption, which open up an avenue for precisely modulating the plant growth and development, as well as environmental adaption. Additionally, we also discuss prospective applications of uORFs in plant breeding.
FAIR checklist signals are shown for context only and do not affect DataRank scoring.
Base Score Contribution
0.635
From this paper's citation signal
Citation Network Contribution
1.4
From 53 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 32% comes from its base citations and 68% from the citation network (53 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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