RNA editing is a generic term comprising a variety of processes that alter the DNA-encoded sequence of a transcribed RNA by inserting, deleting or modifying nucleotides in the transcript. These various processes have been observed sporadically throughout eukaryotes and in some viruses, although the mechanisms and outcomes of editing are generally lineage specific (1
). In plants, RNA editing affects mitochondrial and plastid transcripts of all major lineages of land plants (i.e. angiosperms, gymnosperms, ferns, lycophytes, hornworts, mosses and liverworts) and operates by the site-specific modification of cytidines to uridines and, in some groups, uridines to cytidines (2–4
). These C-to-U and U-to-C changes are generally found at codon positions that effect a change in the encoded amino acid (5
). Therefore, it is important to know where sites of RNA editing exist in the transcriptome in order to understand the proper structure and function of the translated proteins.
To discover the location of RNA edit sites in plant organellar transcriptomes, comprehensive experimental analyses have been carried out for several species. For chloroplasts, this list now includes over 10 angiosperms (e.g. 6–8), as well as a gymnosperm, a hornwort and a fern (9–11
). The chloroplasts of ferns and hornworts contain hundreds of C-to-U and U-to-C edit sites (10
), whereas angiosperm and gymnosperm chloroplasts harbor only a few dozen C-to-U sites and no U-to-C sites at all (6–9
). For plant mitochondria, four angiosperms have been examined and all of them have several hundred C-to-U sites but no U-to-C sites (5
Unfortunately, these systematic analyses of plant organellar transcriptomes have not kept pace with the rate of genome sequencing. There are now over 100 plastid and 20 mitochondrial genomes from land plants available in the sequence databases, and edit sites clearly abound in almost all of them. Of course, it is neither the aim of many of these genome sequencing projects to experimentally identify sites of RNA editing, nor is it always practical to do so for every newly sequenced genome. In recent years, several studies have taken various computational approaches to predict edit sites, with varying degrees of success (15–18
). Some methods attempt to predict sites using information in the immediate sequence context (15
), but these generally suffer from low specificity resulting in a large number of false positives. This is due to the low frequency of editing in angiosperm mitochondrial genes, where <10% of the cytidines in protein-coding genes are actually edited (5
). Other approaches utilize evolutionary information and have achieved better results. The predictive RNA editor for plant mitochondrial genes (PREP-Mt) identifies sites based on the principle that editing increases protein conservation among species (16
). The most successful program to date, CURE, relies on the shared ancestry of edit sites, only considering cytidine positions that are known to be edited in other species (18
All of the current methods have focused on the abundance of data from angiosperm mitochondria, so it is unclear whether they will be generally applicable for more divergent plant groups or for chloroplast editing. To address the need for a chloroplast predictor, the predictive RNA editor for plant chloroplast genes (PREP-Cp) was developed by adapting the PREP-Mt methodology. PREP-Cp behaves almost identically to PREP-Mt; the only difference is that PREP-Cp translates and aligns an input sequence to a pre-defined alignment of chloroplast homologs, whereas PREP-Mt aligns to a homologous mitochondrial alignment. And for times when the pre-defined alignments from PREP-Mt and PREP-Cp are not adequate, the predictive RNA editor for user-defined alignments (PREP-Aln) provides an alternative. PREP-Aln applies the PREP-Mt methodology to a custom alignment submitted by the user containing a mix of RNA sequences (with known edit sites) and DNA sequences (in which sites will be predicted). This flexibility allows the user to potentially increase prediction accuracy by taking advantage of newly published editing data or by increasing sampling from a targeted lineage of interest. This suite of web servers should greatly expand our ability to identify potential sites of RNA editing in plant organellar transcripts.