Article open access publication

Characterization of viral RNA splicing using whole-transcriptome datasets from host species

Scientific Reports, Springer Nature, ISSN 2045-2322

Volume 8, 1, 2018

DOI:10.1038/s41598-018-21190-7, Dimensions: pub.1101019989, PMC: PMC5818608, PMID: 29459752,


Zhou, Chengran (1) (2)
Liu, Shanlin (1) (3)
Yang, Hua (2)
Gao, Shan (5)
Wang, Jian (1) (6)
Yang, Huan-Ming (1) (6)
Zhao, Yun * (2)
Wang, Hui * (1) (7)
Zhou, Xin * (4)

* Corresponding author



  1. (1) Beijing Genomics Institute, grid.21155.32
  2. (2) Sichuan University, grid.13291.38
  3. (3) University of Copenhagen, grid.5254.6, KU
  4. (4) China Agricultural University, grid.22935.3f
  5. (5) CAS Key Laboratory of Biomedical & Diagnostic Technology, CAS/Suzhou Institute of Biomedical Engineering and Technology, 215163, Suzhou, China
  6. (6) James D. Watson Institute of Genome Sciences, 310058, Hangzhou, China
  7. (7) University of Oxford, grid.4991.5



United Kingdom






RNA alternative splicing (AS) is an important post-transcriptional mechanism enabling single genes to produce multiple proteins. It has been well demonstrated that viruses deploy host AS machinery for viral protein productions. However, knowledge on viral AS is limited to a few disease-causing viruses in model species. Here we report a novel approach to characterizing viral AS using whole transcriptome dataset from host species. Two insect transcriptomes (Acheta domesticus and Planococcus citri) generated in the 1,000 Insect Transcriptome Evolution (1KITE) project were used as a proof of concept using the new pipeline. Two closely related densoviruses (Acheta domesticus densovirus, AdDNV, and Planococcus citri densovirus, PcDNV, Ambidensovirus, Densovirinae, Parvoviridae) were detected and analyzed for AS patterns. The results suggested that although the two viruses shared major AS features, dramatic AS divergences were observed. Detailed analysis of the splicing junctions showed clusters of AS events occurred in two regions of the virus genome, demonstrating that transcriptome analysis could gain valuable insights into viral splicing. When applied to large-scale transcriptomics projects with diverse taxonomic sampling, our new method is expected to rapidly expand our knowledge on RNA splicing mechanisms for a wide range of viruses.


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