The DOI matches its Crossref record; 61 of the 61 references it cites were found in a registry or an index.
Identification of novel and candidate miRNAs in rice by high throughput sequencing
Open accessAbstract
BACKGROUND: Small RNA-guided gene silencing at the transcriptional and post-transcriptional levels has emerged as an important mode of gene regulation in plants and animals. Thus far, conventional sequencing of small RNA libraries from rice led to the identification of most of the conserved miRNAs. Deep sequencing of small RNA libraries is an effective approach to uncover rare and lineage- and/or species-specific microRNAs (miRNAs) in any organism. RESULTS: In order to identify new miRNAs and possibly abiotic-stress regulated small RNAs in rice, three small RNA libraries were constructed from control rice seedlings and seedlings exposed to drought or salt stress, and then subjected to pyrosequencing. A total of 58,781, 43,003 and 80,990 unique genome-matching small RNAs were obtained from the control, drought and salt stress libraries, respectively. Sequence analysis confirmed the expression of most of the conserved miRNAs in rice. Importantly, 23 new miRNAs mostly each derived from a unique locus in rice genome were identified. Six of the new miRNAs are conserved in other monocots. Additionally, we identified 40 candidate miRNAs. Allowing not more than 3 mis-matches between a miRNA and its target mRNA, we predicted 20 targets for 9 of the new miRNAs. CONCLUSION: Deep sequencing proved to be an effective strategy that allowed the discovery of 23 low-abundance new miRNAs and 40 candidate miRNAs in rice.
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Bartel (2004) · doi:10.1016/s0092-8674(04)00045-5 · Cited by 34,714
Data Mining: Practical Machine Learning Tools and Techniques
(2011) · Elsevier eBooks · doi:10.1016/c2009-0-19715-5 · Cited by 25,437
The C. elegans heterochronic gene lin-4 encodes small RNAs with antisense complementarity to lin-14
Lee, R. C., Feinbaum, R. L., & Ambros, V. (1993) · Cell · doi:10.1016/0092-8674(93)90529-y · Cited by 12,948
EMBOSS: the European molecular biology open software suite.
Rice, P., Longden, I., & Bleasby, A. (2000) · Trends in Genetics · doi:10.1016/s0168-9525(00)02024-2 · Cited by 9,466
Wrappers for feature subset selection
Kohavi, R., & John, G. H. (1997) · Artificial Intelligence · doi:10.1016/s0004-3702(97)00043-x · Cited by 8,947
Genome sequencing in microfabricated high-density picolitre reactors
Margulies, M., Egholm, M., Altman, W. E., Attiya, S., Bader, J. S., Bemben, L. A., Berka, J., Braverman, M. S., Chen, Y.-J., Chen, Z., et al. (2005) · Nature · doi:10.1038/nature03959 · Cited by 7,693
Data mining: practical machine learning tools and techniques with Java implementations
Witten, I. H., & Frank, E. (2002) · ACM SIGMOD Record · doi:10.1145/507338.507355 · Cited by 5,137
Wightman, B., Ha, I., & Ruvkun, G. (1993) · Cell · doi:10.1016/0092-8674(93)90530-4 · Cited by 4,292
miRBase: microRNA sequences, targets and gene nomenclature
Griffiths-Jones, S. (2006) · Nucleic Acids Research · doi:10.1093/nar/gkj112 · Cited by 3,950
A Mammalian microRNA Expression Atlas Based on Small RNA Library Sequencing
Landgraf, P., Rusu, M., Sheridan, R., Sewer, A., Iovino, N., Aravin, A., Pfeffer, S., Rice, A., Kamphorst, A. O., Landthaler, M., et al. (2007) · Cell · doi:10.1016/j.cell.2007.04.040 · Cited by 3,626
Cited by
miRDeepFinder: a miRNA analysis tool for deep sequencing of plant small RNAs
Xie, F., Xiao, P., Chen, D., Xu, L., & Zhang, B. (2012) · Plant Molecular Biology · doi:10.1007/s11103-012-9885-2 · Cited by 1,536
Transcriptome-wide identification of microRNA targets in rice: Rice microRNA targets
Li, Y.-F., Zheng, Y., Addo-Quaye, C., Zhang, L., Saini, A., Jagadeeswaran, G., Axtell, M. J., Zhang, W., & Sunkar, R. (2010) · The Plant Journal · doi:10.1111/j.1365-313x.2010.04187.x · Cited by 396
Self-citationMultiple Rice MicroRNAs Are Involved in Immunity against the Blast Fungus Magnaporthe oryzae
Li, Y., Lu, Y.-G., Shi, Y., Wu, L., Xu, Y.-J., Huang, F., Guo, X.-Y., Zhang, Y., Fan, J., Zhao, J.-Q., et al. (2014) · Plant Physiology · doi:10.1104/pp.113.230052 · Cited by 383
Conserved miR164-targeted NAC genes negatively regulate drought resistance in rice
Fang, Y., Xie, K., & Xiong, L. (2014) · Journal of Experimental Botany · doi:10.1093/jxb/eru072 · Cited by 379
Zhang, Z., Yu, J., Li, D., Zhang, Z., Liu, F., Zhou, X., Wang, T., Ling, Y., & Su, Z. (2010) · Nucleic Acids Research · doi:10.1093/nar/gkp818 · Cited by 377
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Metadata
- Authors
- Sunkar, R., Zhou, X., Zheng, Y., Zhang, W., & Zhu, J.-K.
- Venue
- BMC Plant Biology
- Published
- 2008
- Volume
- 8
- Issue
- 1
- Pages
- 25
- ISSN
- 1471-2229 (ISSN-L)
- OpenAlex
- W2142214917
- Cited by
- 477
- References
- 61
- FWCI
- 44.73
- Type
- journal article
- Language
- en
- Open access
- gold
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