Deciphering the plant splicing code

Extensive alternative splicing (AS) of precursor mRNAs (pre-mRNAs) in multicellular eukaryotes increases the protein-coding capacity of a genome and allows novel ways to regulate gene expression. In flowering plants, up to 48% of intron-containing genes exhibit AS. However, the full extent of AS in plants is not yet known, as only a few high-throughput RNA-Seq studies have been performed.  As the cost of obtaining RNA-Seq reads continues to fall, it is anticipated that huge amounts of plant sequence data will accumulate and help in obtaining a more complete picture of AS in plants. Although it is not an onerous task to obtain hundreds of millions of reads using high-throughput sequencing technologies, computational tools to accurately predict and visualize AS are still being developed and refined.

This review discusses the tools to predict and visualize transcriptome-wide AS in plants using short-reads and highlight their limitations. Comparative studies of AS events between plants and animals have revealed that there are major differences in the most prevalent types of AS events, suggesting that plants and animals differ in the way they recognize exons and introns. Extensive studies have been performed in animals to identify cis-elements involved in regulating AS, especially in exon skipping. However, few such studies have been carried out in plants. Here, the current state of research on splicing regulatory elements (SREs) is reviewed and emerging experimental and computational tools to identify cis-elements involved in regulation of AS in plants are discussed.

The availability of curated alternative splice forms in plants makes it possible to use computational tools to predict SREs involved in AS regulation, which can then be verified experimentally. Such studies will permit identification of plant-specific features involved in AS regulation and contribute to deciphering the splicing code in plants.

Tools for predicting isoforms, their expression, and alternative splicing from RNA-Seq data.

Method Task Input data
Trans-ABySS (Robertson et al., 2010) IP, IE De novo
Trinity (Grabherr et al., 2011) IP, IE De novo
Rnnotator (Martin et al., 2010) IP De novo
Scripture (Guttman et al., 2010) IP G
IsoLasso (Li et al., 2011) IP, IE G
NSMAP (Xia et al., 2011) IP, IE G
Cufflinks (Trapnell et al., 2010) IP, IE G, A
TAU (Filichkin et al., 2010) IP G,A
SpliceGrapher (Rogers et al., 2012) SG G, A
IsoEM (Nicolae et al., 2010) IE G, A
IsoformEX (Kim et al., 2011) IE G, A
SpliceTrap (Wu et al., 2011) IE G, A
NEUMA (Lee et al., 2011) IE G, A
Solas (Richard et al., 2010) IE G, A
rSeq (Jiang and Wong, 2009) IE G, A
RSEM (Li et al., 2010; Li and Dewey, 2011) IE De novo


The tools vary in the specific task they address; we distinguish between several tasks: isoform prediction (IP), isoform expression (IE) and splice graph prediction (SG). The tools also vary in the input data they require: de novo (no input required except for the RNA-Seq data), a reference genome (G) or annotated isoforms (A).

  • Reddy AS, Rogers MF, Richardson DN, Hamilton M, Ben-Hur A. (2012) Deciphering the plant splicing code: experimental and computational approaches for predicting alternative splicing and splicing regulatory elements. Front Plant Sci 3, 18. [article]
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