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Updated: Sep 26, 2026

An Integrated Approach for Microprotein Identification and Sequence Analysis
Published on: July 12, 2022
The multiomics landscape of small peptides encoded by long noncoding RNA-derived small open reading frames in rice
Zhipeng Chen1,2, Haocheng Lin1, Lirong Wei1
1Department of Plant Pathology, Key Laboratory of Integrated Management of Crop Diseases and Pests, Ministry of Education, Nanjing Agricultural University, Nanjing 210095, China.
Abstract:
Long noncoding RNAs (lncRNAs), as a major class of noncoding transcripts, constitute a large proportion of the genomes in complex organisms. Accumulating evidence has revealed that a subset of lncRNAs harbors peptide-encoding potential. Nevertheless, the existence and biological functions of lncRNA-derived small open reading frames-encoded polypeptides (LSEPs) in plants remain largely elusive. In this study, we established an integrated multiomics pipeline combining transcriptomics, translatomics (Ribo-seq), and proteomics to systematically identify LSEPs in rice. Among the 2,764 lncRNAs detected in our transcriptomic profiling, 42.69% were shown to be associated with ribosomes, implying their potential to be translated into polypeptides. To further validate LSEP expression at the protein level, we optimized an extraction protocol for small peptides. Using this optimized method, low-molecular-weight peptides were isolated from rice leaves and subjected to LC-MS/MS analysis. Across 4 custom-constructed search databases, a total of 376 LSEPs were confidently identified. Our work provides direct experimental evidence supporting the peptide-encoding capacity of rice lncRNAs. Collectively, this study establishes a robust and efficient multiomics workflow for identifying lncRNA-encoded small peptides, which will facilitate the large-scale discovery and functional characterization of LSEPs across diverse plant species.
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