The DOI matches its Crossref record; 46 of the 46 references it cites were found in a registry or an index.
The RNA degradome: a precious resource for deciphering RNA processing and regulation codes in plants
Abstract
The plant RNA degradome was defined as an aggregate of the RNA fragments degraded from various biochemical pathways, such as RNA turnover, maturation and quality surveillance. In recent years, the degradome sequencing (degradome-seq) libraries became a rich storehouse for researchers to study on RNA processing and regulation. Here, we provided a brief overview of the uses of degradome-seq data in plant RNA biology, especially on non-coding RNA processing and small RNA-guided target cleavages. Some novel applications in RNA research area, such as in vivo mapping of the endoribonucleolytic cleavage sites, identification of conserved motifs at the 5ʹ ends of the uncapped RNA fragments, and searching for the protein-binding regions on the transcripts, were also mentioned. More importantly, we proposed a model for the biologists to deduce the contributions of transcriptional and/or post-transcriptional regulation to gene differential expression based on degradome-seq data. Finally, we hope that the degradome-based analytical methods could be widely applied for the studies on RNA biology in eukaryotes.
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“These and other longer RNAs might be the direct result of the cell degradome, known mostly for proteins (52,53) but which has been shown to be also existent for RNAs (54,55).”
From miRNATissueAtlas2: an update to the human miRNA tissue atlas (2022) · cites this paper as [54]
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Robinson, M. D., McCarthy, D. J., & Smyth, G. K. (2010) · Bioinformatics · doi:10.1093/bioinformatics/btp616 · Cited by 41,364
Full-length transcriptome assembly from RNA-Seq data without a reference genome
Grabherr, M. G., Haas, B. J., Yassour, M., Levin, J. Z., Thompson, D. A., Amit, I., Adiconis, X., Fan, L., Raychowdhury, R., Zeng, Q., et al. (2011) · Nature Biotechnology · doi:10.1038/nbt.1883 · Cited by 21,039
Mapping and quantifying mammalian transcriptomes by RNA-Seq
Mortazavi, A., Williams, B. A., McCue, K., Schaeffer, L., & Wold, B. (2008) · Nature Methods · doi:10.1038/nmeth.1226 · Cited by 13,696
Salmon provides fast and bias-aware quantification of transcript expression
Patro, R., Duggal, G., Love, M. I., Irizarry, R. A., & Kingsford, C. (2017) · Nature Methods · doi:10.1038/nmeth.4197 · Cited by 12,700
MicroRNA genes are transcribed by RNA polymerase II
Lee, Y., Kim, M., Han, J., Yeom, K., Lee, S., Baek, S. H., & Kim, V. N. (2004) · The EMBO Journal · doi:10.1038/sj.emboj.7600385 · Cited by 4,373
Chromatin signature reveals over a thousand highly conserved large non-coding RNAs in mammals
Guttman, M., Amit, I., Garber, M., French, C., Lin, M. F., Feldser, D., Huarte, M., Zuk, O., Carey, B. W., Cassady, J. P., et al. (2009) · Nature · doi:10.1038/nature07672 · Cited by 4,098
psRNATarget: a plant small RNA target analysis server (2017 release)
Dai, X., Zhuang, Z., & Zhao, P. X. (2018) · Nucleic Acids Research · doi:10.1093/nar/gky316 · Cited by 1,449
Endogenous siRNA and miRNA Targets Identified by Sequencing of the Arabidopsis Degradome
Addo-Quaye, C., Eshoo, T. W., Bartel, D. P., & Axtell, M. J. (2008) · Current Biology · doi:10.1016/j.cub.2008.04.042 · Cited by 786
Expression of Arabidopsis MIRNA Genes
Xie, Z., Allen, E., Fahlgren, N., Calamar, A., Givan, S. A., & Carrington, J. C. (2005) · Plant Physiology · doi:10.1104/pp.105.062943 · Cited by 707
Genome-Wide Analysis Uncovers Regulation of Long Intergenic Noncoding RNAs in Arabidopsis
Liu, J., Jung, C., Xu, J. Q., Wang, H., Deng, S., Bernad, L., Arenas‐Huertero, C., & Chua, N. (2012) · The Plant Cell · doi:10.1105/tpc.112.102855 · Cited by 695
Research works printed but not linked: 2
- Thody J, 2018, Nucleic Acids Res, 46, 8730
- Yu D, 2018, Brief Bioinform, 19, 1317
Cited by
miRNATissueAtlas2: an update to the human miRNA tissue atlas
Keller, A., Gröger, L., Tschernig, T., Solomon, J., Laham, O., Schaum, N., Wagner, V., Kern, F., Schmartz, G. P., Li, Y., et al. (2022) · Nucleic Acids Research · doi:10.1093/nar/gkab808 · Cited by 143
Metadata
- Authors
- Ma, X., Yin, X., Tang, Z., Ito, H., Shao, C., Meng, Y., & Xie, T.
- Affiliations
- Hangzhou Normal University , Hangzhou, China
- College of Food Science and Technology, Hunan Agricultural University , Changsha, Hunan, PR China
- Faculty of Science, Hokkaido University , Sapporo, Japan
- College of Life Sciences, Huzhou University , Huzhou, PR China
- ORCID
- Yin, Xiaopu
- Venue
- RNA Biology
- Publisher
- Informa UK Limited
- Published
- 2020-04-26
- Volume
- 17
- Issue
- 9
- Pages
- 1223-1227
- ISSN
- 1547-6286
- 1555-8584
- Cited by
- 17
- References
- 46
- Language
- en
- Funders
- National Natural Science Foundation of China (81730108, 31970637, 81973635)
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- Ma, X., Yin, X., Tang, Z., Ito, H., Shao, C., Meng, Y., & Xie, T. (2020). The RNA degradome: a precious resource for deciphering RNA processing and regulation codes in plants. RNA Biology. https://doi.org/10.1080/15476286.2020.1757898
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