Large-scale detection and analysis of RNA editing in grape mtDNA by RNA deep-sequencing

Large-scale detection and analysis of RNA editing in grape mtDNA by RNA deep-sequencing
Ernesto Picardi1, David S. Horner2, Matteo Chiara2, Riccardo Schiavon3, Giorgio Valle3 and Graziano Pesole1,4,*

1Dipartimento di Biochimica e Biologia Molecolare ‘E. Quagliariello’, Università degli Studi di Bari, 70126 Bari, 2Dipartimento di Scienze Biomolecolari e Biotecnologie, Università degli Studi di Milano, 20133 Milano, 3CRIBI, Università degli Studi di Padova, viale G. Colombo 3, 35121 Padova and 4Istituto Tecnologie Biomediche del Consiglio Nazionale delle Ricerche, via Amendola 122/D, 70125 Bari, Italy

*To whom correspondence should be addressed. Tel: ; Fax: +39 080 544 3317; Email: graziano.pesole@biologia.uniba.it

Received September 7, 2009. Revised March 9, 2010. Accepted March 9, 2010.

RNA editing is a widespread post-transcriptional molecular phenomenon that can increase proteomic diversity, by modifying the sequence of completely or partially non-functional primary transcripts, through a variety of mechanistically and evolutionarily unrelated pathways. Editing by base substitution has been investigated in both animals and plants. However, conventional strategies based on directed Sanger sequencing are time-consuming and effectively preclude genome wide identification of RNA editing and assessment of partial and tissue-specific editing sites. In contrast, the high-throughput RNA-Seq approach allows the generation of a comprehensive landscape of RNA editing at the genome level. Short reads from Solexa/Illumina GA and ABI SOLiD platforms have been used to investigate the editing pattern in mitochondria of Vitis vinifera providing significant support for 401 C-to-U conversions in coding regions and an additional 44 modifications in non-coding RNAs. Moreover, 76% of all C-to-U conversions in coding genes represent partial RNA editing events and 28% of them were shown to be significantly tissue specific. Solexa/Illumina and SOLiD platforms showed different characteristics with respect to the specific issue of large-scale editing analysis, and the combined approach presented here reduces the false positive rate of discovery of editing events.