The DOI matches its Crossref record; 7 of the 7 references it cites were found in a registry or an index.
Deep RNA sequencing at single base-pair resolution reveals high complexity of the rice transcriptome
Open accessAbstract
Understanding the dynamics of eukaryotic transcriptome is essential for studying the complexity of transcriptional regulation and its impact on phenotype. However, comprehensive studies of transcriptomes at single base resolution are rare, even for modern organisms, and lacking for rice. Here, we present the first transcriptome atlas for eight organs of cultivated rice. Using high-throughput paired-end RNA-seq, we unambiguously detected transcripts expressing at an extremely low level, as well as a substantial number of novel transcripts, exons, and untranslated regions. An analysis of alternative splicing in the rice transcriptome revealed that alternative cis-splicing occurred in ∼33% of all rice genes. This is far more than previously reported. In addition, we also identified 234 putative chimeric transcripts that seem to be produced by trans-splicing, indicating that transcript fusion events are more common than expected. In-depth analysis revealed a multitude of fusion transcripts that might be by-products of alternative splicing. Validation and chimeric transcript structural analysis provided evidence that some of these transcripts are likely to be functional in the cell. Taken together, our data provide extensive evidence that transcriptional regulation in rice is vastly more complex than previously believed.
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Cites
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
RNA-Seq: a revolutionary tool for transcriptomics
Wang, Z., Gerstein, M., & Snyder, M. (2009) · Nature Reviews Genetics · doi:10.1038/nrg2484 · Cited by 13,174
Alternative isoform regulation in human tissue transcriptomes
Wang, E. T., Sandberg, R., Luo, S., Khrebtukova, I., Zhang, L., Mayr, C., Kingsmore, S. F., Schroth, G. P., & Burge, C. B. (2008) · Nature · doi:10.1038/nature07509 · Cited by 5,181
SOAP: Short oligonucleotide alignment program
Li, R., Li, Y., Kristiansen, K., & Wang, J. (2008) · Bioinformatics · doi:10.1093/bioinformatics/btn025 · Cited by 3,597
Self-citationGilbert, W. (1978) · Nature · doi:10.1038/271501a0 · Cited by 2,300
Stanke, M., Schöffmann, O., Morgenstern, B., & Waack, S. (2006) · BMC Bioinformatics · doi:10.1186/1471-2105-7-62 · Cited by 1,304
Stem cell transcriptome profiling via massive-scale mRNA sequencing
Cloonan, N., Forrest, A. R. R., Kolle, G., Gardiner, B. B. A., Faulkner, G. J., Brown, M. K., Taylor, D. F., Steptoe, A. L., Wani, S., Bethel, G., et al. (2008) · Nature Methods · doi:10.1038/nmeth.1223 · Cited by 1,035 · cited 2×
“technology is more sensitive at detecting lowly expressed transcripts compared to traditional SAGE (serial analysis of gene expression) and microarray hybridizations (Cloonan et al. 2008; Mortazavi et al. 2008; Nagalakshmi et al. 2008; Sultan et al. 2008; Wang et al. 2008; Wilhelm et al. 2008).”
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Cited by
Upstream ORFs are prevalent translational repressors in vertebrates
Johnstone, T. G., Bazzini, A. A., & Giraldez, A. J. (2016) · The EMBO Journal · doi:10.15252/embj.201592759 · Cited by 346
A comparison of statistical methods for detecting differentially expressed genes from RNA‐seq data
Kvam, V. M., Liu, P., & Si, Y. (2012) · American Journal of Botany · doi:10.3732/ajb.1100340 · Cited by 281
Sekhon, R. S., Briskine, R., Hirsch, C. N., Myers, C. L., Springer, N. M., Buell, C. R., de Leon, N., & Kaeppler, S. M. (2013) · PLoS ONE · doi:10.1371/journal.pone.0061005 · Cited by 163
Metadata
- Authors
- Zhang, G., Guo, G., Hu, X., Zhang, Y., Li, Q., Li, R., Zhuang, R., Lu, Z., He, Z., Fang, X., Chen, L., Tian, W., Tao, Y., Kristiansen, K., Zhang, X., Li, S., Yang, H., Wang, J., & Wang, J.
- Venue
- Genome Research
- Publisher
- Cold Spring Harbor Laboratory
- Published
- 2010-03-19
- Volume
- 20
- Issue
- 5
- Pages
- 646-654
- ISSN
- 1088-9051 (ISSN-L)
- OpenAlex
- W2099738370
- Cited by
- 485
- References
- 7
- Type
- posted content
- Language
- en
- Open access
- green
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- Zhang, G., Guo, G., Hu, X., Zhang, Y., Li, Q., Li, R., Zhuang, R., Lu, Z., He, Z., Fang, X., Chen, L., Tian, W., Tao, Y., Kristiansen, K., Zhang, X., Li, S., Yang, H., Wang, J., & Wang, J. (2010). Deep RNA sequencing at single base-pair resolution reveals high complexity of the rice transcriptome. Genome Research. https://doi.org/10.1101/gr.100677.109
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