Mining small RNA sequencing data: a new approach to identify small nucleolar RNAs in Arabidopsis |
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| Authors: | Ho-Ming Chen and Shu-Hsing Wu |
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| Affiliation: | 1.Institute of Plant and Microbial Biology, Academia Sinica, 2.Molecular and Biological Agricultural Sciences Program, Taiwan International Graduate Program, National Chung-Hsing University and Academia Sinica, Taipei, 11529 and 3.Graduate Institute of Biotechnology and Department of Life Sciences, National Chung-Hsing University, Taichung, 402, Taiwan |
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| Abstract: | Small nucleolar RNAs (snoRNAs) are noncoding RNAs that direct 2′-O-methylation or pseudouridylation on ribosomal RNAs or spliceosomal small nuclear RNAs. These modifications are needed to modulate the activity of ribosomes and spliceosomes. A comprehensive repertoire of snoRNAs is needed to expand the knowledge of these modifications. The sequences corresponding to snoRNAs in 18–26-nt small RNA sequencing data have been rarely explored and remain as a hidden treasure for snoRNA annotation. Here, we showed the enrichment of small RNAs at Arabidopsis snoRNA termini and developed a computational approach to identify snoRNAs on the basis of this characteristic. The approach successfully uncovered the full-length sequences of 144 known Arabidopsis snoRNA genes, including some snoRNAs with improved 5′- or 3′-end annotation. In addition, we identified 27 and 17 candidates for novel box C/D and box H/ACA snoRNAs, respectively. Northern blot analysis and sequencing data from parallel analysis of RNA ends confirmed the expression and the termini of the newly predicted snoRNAs. Our study especially expanded on the current knowledge of box H/ACA snoRNAs and snoRNA species targeting snRNAs. In this study, we demonstrated that the use of small RNA sequencing data can increase the complexity and the accuracy of snoRNA annotation. |
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