Author(s) |
Wang, Shunxi
Tian, Lei
Liu, Haijun
Li, Xiang
Zhang, Jinghua
Chen, Xueyan
Jia, Xingmeng
Zheng, Xu
Wu, Shubiao
Chen, Yanhui
Yan, Jianbing
Wu, Liuji
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Publication Date |
2020-07-06
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Abstract |
Non-conventional peptides (NCPs), which include small open reading frame-encoded peptides, play critical roles in fundamental biological processes. In this study, we developed an integrated peptidogenomic pipeline using high-throughput mass spectra to probe a customized six-frame translation database and applied it to large-scale identification of NCPs in plants.A total of 1993 and 1860 NCPs were unambiguously identified in maize and <i>Arabidopsis</i>, respectively. These NCPs showed distinct characteristics compared with conventional peptides and were derived from introns, 3' UTRs, 5' UTRs, junctions, and intergenic regions. Furthermore, our results showed that translation events in unannotated transcripts occur more broadly than previously thought. In addition, we found that dozens of maize NCPs are enriched within regions associated with phenotypic variations and domestication selection, indicating that they potentially are involved in genetic regulation of complex traits and domestication in maize. Taken together, our study developed an integrated peptidogenomic pipeline for large-scale identification of NCPs in plants, which would facilitate global characterization of NCPs from other plants. The identification of large-scale NCPs in both monocot (maize) and dicot (<i>Arabidopsis</i>) plants indicates that a large portion of plant genome can be translated into biologically functional molecules, which has important implications for functional genomic studies.
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Citation |
Molecular Plant, 13(7), p. 1078-1093
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ISSN |
1752-9867
1674-2052
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Pubmed ID |
32445888
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Link | |
Language |
en
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Publisher |
Cell Press
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Rights |
Attribution-NonCommercial-NoDerivatives 4.0 International
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Title |
Large-Scale Discovery of Non-conventional Peptides in Maize and Arabidopsis Through an Integrated Peptidogenomic Pipeline
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Type of document |
Journal Article
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Entity Type |
Publication
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Name | Size | format | Description | Link |
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openpublished/LargeScaleWu2020JournalArticle.pdf | 3385.562 KB | application/pdf | Published version | View document |