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Deciphering the Plant Splicing Code: Experimental and Computational Approaches for Predicting Alternative Splicing and Splicing Regulatory Elements

Identifieur interne : 000704 ( Pmc/Corpus ); précédent : 000703; suivant : 000705

Deciphering the Plant Splicing Code: Experimental and Computational Approaches for Predicting Alternative Splicing and Splicing Regulatory Elements

Auteurs : Anireddy S. N. Reddy ; Mark F. Rogers ; Dale N. Richardson ; Michael Hamilton ; Asa Ben-Hur

Source :

RBID : PMC:3355732

Abstract

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 will discuss 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, we review the current state of research on splicing regulatory elements (SREs) and briefly discuss emerging experimental and computational tools to identify cis-elements involved in regulation of AS in plants. 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.


Url:
DOI: 10.3389/fpls.2012.00018
PubMed: 22645572
PubMed Central: 3355732

Links to Exploration step

PMC:3355732

Le document en format XML

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<p>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 will discuss 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
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</TEI>
<pmc article-type="review-article">
<pmc-dir>properties open_access</pmc-dir>
<front>
<journal-meta>
<journal-id journal-id-type="nlm-ta">Front Plant Sci</journal-id>
<journal-id journal-id-type="iso-abbrev">Front Plant Sci</journal-id>
<journal-id journal-id-type="publisher-id">Front. Plant Sci.</journal-id>
<journal-title-group>
<journal-title>Frontiers in plant science</journal-title>
</journal-title-group>
<issn pub-type="epub">1664-462X</issn>
<publisher>
<publisher-name>Frontiers Research Foundation</publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id pub-id-type="pmid">22645572</article-id>
<article-id pub-id-type="pmc">3355732</article-id>
<article-id pub-id-type="doi">10.3389/fpls.2012.00018</article-id>
<article-categories>
<subj-group subj-group-type="heading">
<subject>Plant Science</subject>
<subj-group>
<subject>Review Article</subject>
</subj-group>
</subj-group>
</article-categories>
<title-group>
<article-title>Deciphering the Plant Splicing Code: Experimental and Computational Approaches for Predicting Alternative Splicing and Splicing Regulatory Elements</article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname>Reddy</surname>
<given-names>Anireddy S. N.</given-names>
</name>
<xref ref-type="aff" rid="aff1">
<sup>1</sup>
</xref>
<xref ref-type="author-notes" rid="fn001">*</xref>
<xref ref-type="author-notes" rid="fn002">
<sup></sup>
</xref>
</contrib>
<contrib contrib-type="author">
<name>
<surname>Rogers</surname>
<given-names>Mark F.</given-names>
</name>
<xref ref-type="aff" rid="aff2">
<sup>2</sup>
</xref>
<xref ref-type="author-notes" rid="fn002">
<sup></sup>
</xref>
</contrib>
<contrib contrib-type="author">
<name>
<surname>Richardson</surname>
<given-names>Dale N.</given-names>
</name>
<xref ref-type="aff" rid="aff3">
<sup>3</sup>
</xref>
</contrib>
<contrib contrib-type="author">
<name>
<surname>Hamilton</surname>
<given-names>Michael</given-names>
</name>
<xref ref-type="aff" rid="aff2">
<sup>2</sup>
</xref>
</contrib>
<contrib contrib-type="author">
<name>
<surname>Ben-Hur</surname>
<given-names>Asa</given-names>
</name>
<xref ref-type="aff" rid="aff2">
<sup>2</sup>
</xref>
<xref ref-type="aff" rid="aff4">
<sup>4</sup>
</xref>
<xref ref-type="author-notes" rid="fn001">*</xref>
</contrib>
</contrib-group>
<aff id="aff1">
<sup>1</sup>
<institution>Program in Molecular Plant Biology, Department of Biology, Colorado State University</institution>
<country>Fort Collins, CO, USA</country>
</aff>
<aff id="aff2">
<sup>2</sup>
<institution>Department of Computer Science, Colorado State University</institution>
<country>Fort Collins, CO, USA</country>
</aff>
<aff id="aff3">
<sup>3</sup>
<institution>Centro de Investigação em Biodiversidade e Recursos Genéticos, University of Porto</institution>
<country>Vairão, Portugal</country>
</aff>
<aff id="aff4">
<sup>4</sup>
<institution>Program in Molecular Plant Biology, Colorado State University</institution>
<country>Fort Collins, CO, USA</country>
</aff>
<author-notes>
<fn fn-type="edited-by">
<p>Edited by: Richard A. Jorgensen, Laboratorio Nacional de Genómica para la Biodiversidad, Mexico</p>
</fn>
<fn fn-type="edited-by">
<p>Reviewed by: William Brad Barbazuk, University of Florida, USA; Steve M. Mount, University of Maryland, USA</p>
</fn>
<corresp id="fn001">*Correspondence: Anireddy S. N. Reddy, Program in Molecular Plant Biology, Department of Biology, Colorado State University, Fort Collins, CO 80523-1878, USA. e-mail:
<email>reddy@colostate.edu</email>
; Asa Ben-Hur, Department of Computer Science, Colorado State University, Fort Collins, CO 80523, USA. e-mail:
<email>asa@cs.colostate.edu</email>
</corresp>
<fn fn-type="other" id="fn002">
<p>
<sup></sup>
Anireddy S. N. Reddy and Mark F. Rogers are co-first authors.</p>
</fn>
<fn fn-type="other" id="fn003">
<p>This article was submitted to Frontiers in Plant Genetics and Genomics, a specialty of Frontiers in Plant Science.</p>
</fn>
</author-notes>
<pub-date pub-type="epub">
<day>07</day>
<month>2</month>
<year>2012</year>
</pub-date>
<pub-date pub-type="collection">
<year>2012</year>
</pub-date>
<volume>3</volume>
<elocation-id>18</elocation-id>
<history>
<date date-type="received">
<day>27</day>
<month>11</month>
<year>2011</year>
</date>
<date date-type="accepted">
<day>18</day>
<month>1</month>
<year>2012</year>
</date>
</history>
<permissions>
<copyright-statement>Copyright © 2012 Reddy, Rogers, Richardson, Hamilton and Ben-Hur.</copyright-statement>
<copyright-year>2012</copyright-year>
<license license-type="open-access" xlink:href="http://www.frontiersin.org/licenseagreement">
<license-p>This is an open-access article distributed under the terms of the
<uri xlink:type="simple" xlink:href="http://creativecommons.org/licenses/by-nc/3.0/">Creative Commons Attribution Non Commercial License</uri>
, which permits non-commercial use, distribution, and reproduction in other forums, provided the original authors and source are credited.</license-p>
</license>
</permissions>
<abstract>
<p>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 will discuss 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
<italic>cis</italic>
-elements involved in regulating AS, especially in exon skipping. However, few such studies have been carried out in plants. Here, we review the current state of research on splicing regulatory elements (SREs) and briefly discuss emerging experimental and computational tools to identify
<italic>cis</italic>
-elements involved in regulation of AS in plants. 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.</p>
</abstract>
<kwd-group>
<kwd>pre-mRNA splicing</kwd>
<kwd>alternative splicing</kwd>
<kwd>splicing regulators</kwd>
<kwd>splicing regulatory elements</kwd>
<kwd>plants</kwd>
<kwd>
<italic>Arabidopsis</italic>
</kwd>
<kwd>splicing code</kwd>
<kwd>RNA-Seq</kwd>
</kwd-group>
<counts>
<fig-count count="4"></fig-count>
<table-count count="1"></table-count>
<equation-count count="0"></equation-count>
<ref-count count="202"></ref-count>
<page-count count="15"></page-count>
<word-count count="15518"></word-count>
</counts>
</article-meta>
</front>
<body>
<sec>
<label>1</label>
<title>Introduction</title>
<p>Seminal discoveries in RNA biology in recent years have established a central role for RNAs in gene regulation at the transcriptional, post-transcriptional, and translational level in eukaryotes (reviewed in Chen,
<xref ref-type="bibr" rid="B30">2009</xref>
; Sharp,
<xref ref-type="bibr" rid="B149">2009</xref>
; Voinnet,
<xref ref-type="bibr" rid="B180">2009</xref>
; Licatalosi and Darnell,
<xref ref-type="bibr" rid="B96">2010</xref>
; Kalsotra and Cooper,
<xref ref-type="bibr" rid="B75">2011</xref>
; Staiger and Green,
<xref ref-type="bibr" rid="B160">2011</xref>
). In photosynthetic eukaryotes a vast majority of protein-coding genes (up to 90%) contain non-coding intronic sequences, hence the primary transcripts must undergo splicing to generate mature functional mRNAs (Reddy,
<xref ref-type="bibr" rid="B126">2007</xref>
; Barbazuk et al.,
<xref ref-type="bibr" rid="B10">2008</xref>
; Labadorf et al.,
<xref ref-type="bibr" rid="B83">2010</xref>
). Pre-mRNA splicing is carried out by the spliceosome, a large ribonucleoprotein complex. In plants, as in animals, there are two types of spliceosomes. The major type is called U2 type, which performs splicing of U2-dependent introns, whereas the minor U12 type is involved in splicing of rare U12-dependent introns (Simpson and Brown,
<xref ref-type="bibr" rid="B151">2008</xref>
). Both spliceosomes consist of five snRNAs (U1, U2, U4, U5, U6 in the major spliceosome and U11, U12, U4atac, U5, and U6atac in the minor spliceosome). The protein composition of the major spliceosome has been extensively studied in animals, revealing that it contains close to 200 proteins (Wahl et al.,
<xref ref-type="bibr" rid="B183">2009</xref>
; Valadkhan and Jaladat,
<xref ref-type="bibr" rid="B179">2010</xref>
). Computational analysis has revealed that plants have RNA and protein components of both spliceosomes (Ru et al.,
<xref ref-type="bibr" rid="B136">2008</xref>
; Simpson and Brown,
<xref ref-type="bibr" rid="B151">2008</xref>
).</p>
<p>Primary transcripts from intron-containing genes can be alternatively spliced by differential selection of splice sites, leading to production of multiple mature mRNAs from a single gene, which is considered a major source for proteome diversity (Black,
<xref ref-type="bibr" rid="B15">2003</xref>
; Reddy,
<xref ref-type="bibr" rid="B126">2007</xref>
; Pan et al.,
<xref ref-type="bibr" rid="B120">2008</xref>
; Ru et al.,
<xref ref-type="bibr" rid="B136">2008</xref>
; Kalsotra and Cooper,
<xref ref-type="bibr" rid="B75">2011</xref>
). Protein isoforms produced by splice variants may have altered functions (Black,
<xref ref-type="bibr" rid="B15">2003</xref>
; Stamm et al.,
<xref ref-type="bibr" rid="B161">2005</xref>
). In addition, AS plays an important role in gene regulation through regulated production of splice variants with a premature termination codon, which are degraded through nonsense-mediated decay and other RNA surveillance mechanisms (Chang et al.,
<xref ref-type="bibr" rid="B27">2007</xref>
; Kurihara et al.,
<xref ref-type="bibr" rid="B81">2009</xref>
; Barbazuk,
<xref ref-type="bibr" rid="B9">2010</xref>
; Palusa and Reddy,
<xref ref-type="bibr" rid="B119">2010</xref>
; Staiger and Green,
<xref ref-type="bibr" rid="B160">2011</xref>
) or contain target sequences for miRNA so that they are either degraded or not translated (Tan et al.,
<xref ref-type="bibr" rid="B166">2007</xref>
; Chen,
<xref ref-type="bibr" rid="B30">2009</xref>
). Hence, post-transcriptional regulation of gene expression by pre-mRNA splicing plays a crucial role in generating transcriptome and proteome diversity and provides novel ways to fine-tune gene regulation.</p>
<p>AS in plants was under-appreciated until recently, and it was considered rare as pre-mRNAs of only a few genes were known to undergo AS. For instance, in 2001 pre-mRNAs from only three dozen genes in plants were known to undergo AS (Reddy,
<xref ref-type="bibr" rid="B125">2001</xref>
). However, the completion of the
<italic>Arabidopsis</italic>
genome little over a decade ago, and other plant genomes more recently, as well as the availability of a massive amounts of transcribed sequence data in the form expressed sequence tags (ESTs)/cDNAs and limited RNA sequence data generated with next generation sequencing (NGS) technologies, have allowed the analysis of transcriptome-wide AS in several plants including
<italic>Arabidopsis</italic>
, rice, grape, and cucumber (Campbell et al.,
<xref ref-type="bibr" rid="B26">2006</xref>
; Wang and Brendel,
<xref ref-type="bibr" rid="B184">2006</xref>
; Reddy,
<xref ref-type="bibr" rid="B126">2007</xref>
; Baek et al.,
<xref ref-type="bibr" rid="B6">2008</xref>
; Barbazuk et al.,
<xref ref-type="bibr" rid="B10">2008</xref>
; Filichkin et al.,
<xref ref-type="bibr" rid="B43">2010</xref>
; Guo et al.,
<xref ref-type="bibr" rid="B56">2010</xref>
; Lu et al.,
<xref ref-type="bibr" rid="B101">2010</xref>
; Sanchez et al.,
<xref ref-type="bibr" rid="B139">2010</xref>
; Zenoni et al.,
<xref ref-type="bibr" rid="B201">2010</xref>
). A variety of splicing-sensitive microarrays such as splice junction arrays and tiling arrays, that are used extensively in animals (Hallegger et al.,
<xref ref-type="bibr" rid="B61">2010</xref>
) have not been widely used in plants to analyze AS globally (Love et al.,
<xref ref-type="bibr" rid="B100">2010</xref>
; Rehrauer et al.,
<xref ref-type="bibr" rid="B128">2010</xref>
; Zenoni et al.,
<xref ref-type="bibr" rid="B201">2010</xref>
). RNA sequencing (RNA-Seq) using NGS platforms has allowed the detection of rare transcripts, precise quantification of transcript levels and global analysis of AS (Pan et al.,
<xref ref-type="bibr" rid="B120">2008</xref>
; Wang et al.,
<xref ref-type="bibr" rid="B189">2009a</xref>
). A recent analysis of AS in plants using RNA-Seq has revealed that over 40% of intron-containing genes in
<italic>Arabidopsis</italic>
(Filichkin et al.,
<xref ref-type="bibr" rid="B43">2010</xref>
) and about 48% in rice (Lu et al.,
<xref ref-type="bibr" rid="B101">2010</xref>
) undergo AS, although it is not known how much of AS is due to noise in the splicing process (Melamud and Moult,
<xref ref-type="bibr" rid="B106">2009</xref>
) and how much is regulated AS with biological consequences. In humans, pre-mRNAs from almost every multi-exon gene are alternatively spliced and misregulation of splicing results in developmental abnormalities and disease (Pan et al.,
<xref ref-type="bibr" rid="B120">2008</xref>
; Ru et al.,
<xref ref-type="bibr" rid="B136">2008</xref>
; Sanford et al.,
<xref ref-type="bibr" rid="B140">2009</xref>
). As more RNA-Seq data from different cell types, tissues, developmental stages and under different biotic and abiotic stresses become available, the known repertoire of AS in plants is likely to increase. In addition, many splice variants are differentially expressed in a tissue- or development-specific manner or in response to developmental cues and stresses (Yoshimura et al.,
<xref ref-type="bibr" rid="B200">2002</xref>
; Iida et al.,
<xref ref-type="bibr" rid="B68">2004</xref>
; Palusa et al.,
<xref ref-type="bibr" rid="B118">2007</xref>
; Reddy,
<xref ref-type="bibr" rid="B126">2007</xref>
; Schindler et al.,
<xref ref-type="bibr" rid="B141">2008</xref>
; Simpson and Brown,
<xref ref-type="bibr" rid="B151">2008</xref>
; Filichkin et al.,
<xref ref-type="bibr" rid="B43">2010</xref>
; Simpson et al.,
<xref ref-type="bibr" rid="B154">2010</xref>
; Staiger and Green,
<xref ref-type="bibr" rid="B160">2011</xref>
).</p>
<p>In addition to AS events that include or delete long sequences in the pre-mRNA, other subtle AS events due to tandem acceptors (NAGNAG, N being any nucleotide) that result in gain or loss of three nucleotides in the spliced mRNA are common in land plants and animals (Iida et al.,
<xref ref-type="bibr" rid="B69">2008</xref>
; Schindler et al.,
<xref ref-type="bibr" rid="B141">2008</xref>
; Sinha et al.,
<xref ref-type="bibr" rid="B157">2010</xref>
). Primary transcripts of miRNAs also undergo AS (Hirsch et al.,
<xref ref-type="bibr" rid="B67">2006</xref>
; Szarzynska et al.,
<xref ref-type="bibr" rid="B165">2009</xref>
). In addition to
<italic>cis</italic>
-splicing, occurrence of
<italic>trans</italic>
-splicing, which produces chimeric transcripts by joining transcripts derived from two different nuclear protein-coding genes on the same or different chromosomes, has been reported in plants (Kawasaki et al.,
<xref ref-type="bibr" rid="B78">1999</xref>
; He et al.,
<xref ref-type="bibr" rid="B65">2008</xref>
; Guo et al.,
<xref ref-type="bibr" rid="B56">2010</xref>
). In rice, over 200 chimeric transcripts derived from
<italic>trans</italic>
-splicing were predicted from short-read sequence data and some of these were verified by RT-PCR (Zhang et al.,
<xref ref-type="bibr" rid="B202">2010</xref>
).</p>
<p>Comparative analysis of prevalence of different types of AS events in plants and animals has revealed that there are some fundamental differences between them. In plants a vast majority of splice variants (up to 56%) are due to intron retention, whereas it is not that prevalent in metazoans (5% in humans; Iida et al.,
<xref ref-type="bibr" rid="B68">2004</xref>
; Ner-Gaon et al.,
<xref ref-type="bibr" rid="B112">2004</xref>
; Wang and Brendel,
<xref ref-type="bibr" rid="B184">2006</xref>
; Baek et al.,
<xref ref-type="bibr" rid="B6">2008</xref>
; Filichkin et al.,
<xref ref-type="bibr" rid="B43">2010</xref>
; Labadorf et al.,
<xref ref-type="bibr" rid="B83">2010</xref>
). In animals, exon skipping is the most common form of AS (58% in humans) and it is less prevalent in plants (8% in
<italic>Arabidopsis</italic>
). The differences in the frequencies of different types of AS events between plants and metazoans are thought to reflect the differences in how plant and animal cells recognize exons and introns. However, it is not known why intron retention is prevalent in plants and what mechanisms contribute to it. Interestingly, transcriptome analysis of 18 accessions of
<italic>Arabidopsis thaliana</italic>
has revealed that intron retention events differed between accessions (Gan et al.,
<xref ref-type="bibr" rid="B48">2011</xref>
).</p>
<p>The high rate of occurrence of AS in plants and its regulation by stresses and developmental cues (Kalyna et al.,
<xref ref-type="bibr" rid="B77">2006</xref>
; Palusa et al.,
<xref ref-type="bibr" rid="B118">2007</xref>
; Reddy,
<xref ref-type="bibr" rid="B126">2007</xref>
; Barbazuk et al.,
<xref ref-type="bibr" rid="B10">2008</xref>
; Simpson et al.,
<xref ref-type="bibr" rid="B152">2008</xref>
,
<xref ref-type="bibr" rid="B154">2010</xref>
; Barbazuk,
<xref ref-type="bibr" rid="B9">2010</xref>
; Filichkin et al.,
<xref ref-type="bibr" rid="B43">2010</xref>
; Zenoni et al.,
<xref ref-type="bibr" rid="B201">2010</xref>
; Reddy and Ali,
<xref ref-type="bibr" rid="B127">2011</xref>
) has sparked a growing interest and led to further studies focused on revealing the full extent of AS in plants by deep sequencing. Such studies will aid in understanding the biological functions of splice variants and the mechanisms by which plant cells regulate AS. This review focuses on computational tools used in predicting AS and splicing regulatory elements (SREs) involved in the regulation of AS. We also briefly discuss some recent experimental approaches that have been used in animals to identify targets of RNA binding proteins (RBPs), which can be applied to plants to discover RNA sequences that bind splicing regulators.</p>
</sec>
<sec>
<label>2</label>
<title>Transcriptome-Wide Detection and Visualization of AS</title>
<p>Before the advent of NGS, large-scale studies of AS in plant and mammalian systems were carried out mostly using sequences of Expressed Sequence Tags (ESTs) and full-length cDNAs (Haas et al.,
<xref ref-type="bibr" rid="B58">2003</xref>
; Campbell et al.,
<xref ref-type="bibr" rid="B26">2006</xref>
; Wang and Brendel,
<xref ref-type="bibr" rid="B184">2006</xref>
; Chen et al.,
<xref ref-type="bibr" rid="B29">2007</xref>
; Gu and Guo,
<xref ref-type="bibr" rid="B54">2007</xref>
; Ner-Gaon et al.,
<xref ref-type="bibr" rid="B113">2007</xref>
; Wang et al.,
<xref ref-type="bibr" rid="B185">2008a</xref>
; Sablok et al.,
<xref ref-type="bibr" rid="B137">2011</xref>
). These studies have increased dramatically the estimated number of plant genes that exhibit AS, and identified intron retention events as the most common AS event (Reddy,
<xref ref-type="bibr" rid="B126">2007</xref>
; Wang et al.,
<xref ref-type="bibr" rid="B185">2008a</xref>
). The decline in the cost of sequencing using NGS platforms has made large-scale sequencing readily available, and transcriptome profiling using RNA-Seq has already been carried out in several plant species (Filichkin et al.,
<xref ref-type="bibr" rid="B43">2010</xref>
; Lu et al.,
<xref ref-type="bibr" rid="B101">2010</xref>
; Zenoni et al.,
<xref ref-type="bibr" rid="B201">2010</xref>
; Gan et al.,
<xref ref-type="bibr" rid="B48">2011</xref>
). However, analysis of these massive amounts of sequence data and the short length of sequence reads require novel computational tools, which has become a major bottleneck in mining the data to extract biologically relevant conclusions, especially for accurately predicting AS (Liang et al.,
<xref ref-type="bibr" rid="B95">2009</xref>
; Chodavarapu et al.,
<xref ref-type="bibr" rid="B31">2010</xref>
; Fiume et al.,
<xref ref-type="bibr" rid="B45">2010</xref>
; Marguerat and Bahler,
<xref ref-type="bibr" rid="B103">2010</xref>
).</p>
<sec>
<label>2.1</label>
<title>Transcripts vs splice graphs</title>
<p>Full-length cDNAs provide the best evidence for a gene’s splice forms – aligning such a sequence to the reference genome provides evidence for the exact exon-intron structure of a transcript. ESTs are shorter, but still usually cover several exons. NGS reads on the other hand, are short (around 100 bp in today’s technology), and only provide local evidence for transcript structure. This makes prediction of splice forms from RNA-Seq difficult, and most of the methods for transcriptome assembly first construct an object called a
<italic>splice graph</italic>
(Heber et al.,
<xref ref-type="bibr" rid="B66">2002</xref>
). A splice graph is a compact graphical representation of a gene’s exon-intron structure that captures all the ways in which exons for a given gene may be assembled into a transcript (Heber et al.,
<xref ref-type="bibr" rid="B66">2002</xref>
; Xing et al.,
<xref ref-type="bibr" rid="B197">2004</xref>
; Harrington and Bork,
<xref ref-type="bibr" rid="B63">2008</xref>
; Sammeth et al.,
<xref ref-type="bibr" rid="B138">2008</xref>
; Bonizzoni et al.,
<xref ref-type="bibr" rid="B21">2009</xref>
; Labadorf et al.,
<xref ref-type="bibr" rid="B83">2010</xref>
; Richardson et al.,
<xref ref-type="bibr" rid="B130">2011</xref>
; Rogers et al.,
<xref ref-type="bibr" rid="B134">2012</xref>
). Figure
<xref ref-type="fig" rid="F1">1</xref>
illustrates the concept. The compact structure allows researchers to visualize a gene’s AS easily (Harrington and Bork,
<xref ref-type="bibr" rid="B63">2008</xref>
; Rogers et al.,
<xref ref-type="bibr" rid="B134">2012</xref>
), facilitates integration of ESTs into coherent models (Heber et al.,
<xref ref-type="bibr" rid="B66">2002</xref>
; Xing et al.,
<xref ref-type="bibr" rid="B197">2004</xref>
; Bonizzoni et al.,
<xref ref-type="bibr" rid="B21">2009</xref>
), aids statistical analysis of AS across a genome (Labadorf et al.,
<xref ref-type="bibr" rid="B83">2010</xref>
; Rogers et al.,
<xref ref-type="bibr" rid="B134">2012</xref>
), and facilitates comparisons between gene families (Richardson et al.,
<xref ref-type="bibr" rid="B130">2011</xref>
).</p>
<fig id="F1" position="float">
<label>Figure 1</label>
<caption>
<p>
<bold>Splice forms for the ING2 gene from
<italic>A. thaliana</italic>
shown as a set of transcripts (top) and as a splice graph (bottom)</bold>
. A splice graph is a compact representation that shows all the ways in which a gene’s exons may be combined. These plots, generated by
<italic>SpliceGrapher</italic>
, use color coding to highlight AS events.</p>
</caption>
<graphic xlink:href="fpls-03-00018-g001"></graphic>
</fig>
</sec>
<sec>
<label>2.2</label>
<title>AS prediction using RNA-Seq</title>
<p>Transcriptional activity and AS can be studied using RNA-Seq with and without a reference genome (see Figure
<xref ref-type="fig" rid="F2">2</xref>
for pipeline overview, and Table
<xref ref-type="table" rid="T1">1</xref>
for a list of tools). We begin our discussion with methods that require a reference genome. In this scenario, the first step is aligning the reads to the genome; we distinguish between two types of short-read mapping methods: those that allow only a limited number of gaps (usually a few bp at the most), and those that are able to map reads across splice junctions. Until a few years ago reads were short (32–36 bp) and most read mapping algorithms such as the Bowtie program (Trapnell and Salzberg,
<xref ref-type="bibr" rid="B172">2009</xref>
) performed ungapped alignment. As read length continues to increase (100 bp and higher using today’s technology), the number of reads that span splice junctions increases as well, and with it the number of programs that perform spliced alignment (see, e.g., Trapnell et al.,
<xref ref-type="bibr" rid="B171">2009</xref>
; Au et al.,
<xref ref-type="bibr" rid="B5">2010</xref>
; Jean et al.,
<xref ref-type="bibr" rid="B71">2010</xref>
; Wang et al.,
<xref ref-type="bibr" rid="B187">2010</xref>
). Once mapped, read coverage (the distribution of reads that align within a region of interest) then provides evidence for exons and splice junctions recapitulated in the RNA-Seq data (see Figure
<xref ref-type="fig" rid="F3">3</xref>
).</p>
<fig id="F2" position="float">
<label>Figure 2</label>
<caption>
<p>
<bold>Pipelines for prediction and quantification of splice forms from RNA-Seq data</bold>
. Methods such as trans-ABySS and Trinity perform
<italic>de novo</italic>
prediction, and do not require a reference genome (left); these methods do not rely on alignment programs to map reads to a genome. When a reference genome is available, methods such as Cufflinks first map the reads to the genome, followed by a step of assembly of splice forms or their quantification (right). Some of these methods require, or can use annotated isoforms to guide the process.</p>
</caption>
<graphic xlink:href="fpls-03-00018-g002"></graphic>
</fig>
<table-wrap id="T1" position="float">
<label>Table 1</label>
<caption>
<p>
<bold>Tools for predicting isoforms, their expression, and alternative splicing from RNA-Seq data</bold>
.</p>
</caption>
<table frame="hsides" rules="groups">
<thead>
<tr>
<th align="left" rowspan="1" colspan="1">Method</th>
<th align="left" rowspan="1" colspan="1">Task</th>
<th align="left" rowspan="1" colspan="1">Input data</th>
<th align="left" rowspan="1" colspan="1">Notes</th>
</tr>
</thead>
<tbody>
<tr>
<td align="left" rowspan="1" colspan="1">Trans-ABySS (Robertson et al.,
<xref ref-type="bibr" rid="B132">2010</xref>
)</td>
<td align="left" rowspan="1" colspan="1">IP, IE</td>
<td align="left" rowspan="1" colspan="1">
<italic>De novo</italic>
</td>
<td align="left" rowspan="1" colspan="1">Requires Abyss contigs</td>
</tr>
<tr>
<td align="left" rowspan="1" colspan="1">Trinity (Grabherr et al.,
<xref ref-type="bibr" rid="B52">2011</xref>
)</td>
<td align="left" rowspan="1" colspan="1">IP, IE</td>
<td align="left" rowspan="1" colspan="1">
<italic>De novo</italic>
</td>
<td align="left" rowspan="1" colspan="1"></td>
</tr>
<tr>
<td align="left" rowspan="1" colspan="1">Rnnotator (Martin et al.,
<xref ref-type="bibr" rid="B104">2010</xref>
)</td>
<td align="left" rowspan="1" colspan="1">IP</td>
<td align="left" rowspan="1" colspan="1">
<italic>De novo</italic>
</td>
<td align="left" rowspan="1" colspan="1"></td>
</tr>
<tr>
<td align="left" rowspan="1" colspan="1">Scripture (Guttman et al.,
<xref ref-type="bibr" rid="B57">2010</xref>
)</td>
<td align="left" rowspan="1" colspan="1">IP</td>
<td align="left" rowspan="1" colspan="1">G</td>
<td align="left" rowspan="1" colspan="1"></td>
</tr>
<tr>
<td align="left" rowspan="1" colspan="1">IsoLasso (Li et al.,
<xref ref-type="bibr" rid="B93">2011</xref>
)</td>
<td align="left" rowspan="1" colspan="1">IP, IE</td>
<td align="left" rowspan="1" colspan="1">G</td>
<td align="left" rowspan="1" colspan="1">Improved version of IsoInfer (Feng et al.,
<xref ref-type="bibr" rid="B42">2011</xref>
)</td>
</tr>
<tr>
<td align="left" rowspan="1" colspan="1">NSMAP (Xia et al.,
<xref ref-type="bibr" rid="B195">2011</xref>
)</td>
<td align="left" rowspan="1" colspan="1">IP, IE</td>
<td align="left" rowspan="1" colspan="1">G</td>
<td align="left" rowspan="1" colspan="1"></td>
</tr>
<tr>
<td align="left" rowspan="1" colspan="1">Cufflinks (Trapnell et al.,
<xref ref-type="bibr" rid="B173">2010</xref>
)</td>
<td align="left" rowspan="1" colspan="1">IP, IE</td>
<td align="left" rowspan="1" colspan="1">G, A</td>
<td align="left" rowspan="1" colspan="1">Annotated isoforms are optional</td>
</tr>
<tr>
<td align="left" rowspan="1" colspan="1">TAU (Filichkin et al.,
<xref ref-type="bibr" rid="B43">2010</xref>
)</td>
<td align="left" rowspan="1" colspan="1">IP</td>
<td align="left" rowspan="1" colspan="1">G,A</td>
<td align="left" rowspan="1" colspan="1">Annotated isoforms are optional; does not scale well with read length</td>
</tr>
<tr>
<td align="left" rowspan="1" colspan="1">SpliceGrapher (Rogers et al.,
<xref ref-type="bibr" rid="B134">2012</xref>
)</td>
<td align="left" rowspan="1" colspan="1">SG</td>
<td align="left" rowspan="1" colspan="1">G, A</td>
<td align="left" rowspan="1" colspan="1"></td>
</tr>
<tr>
<td align="left" rowspan="1" colspan="1">IsoEM (Nicolae et al.,
<xref ref-type="bibr" rid="B115">2010</xref>
)</td>
<td align="left" rowspan="1" colspan="1">IE</td>
<td align="left" rowspan="1" colspan="1">G, A</td>
<td align="left" rowspan="1" colspan="1"></td>
</tr>
<tr>
<td align="left" rowspan="1" colspan="1">IsoformEX (Kim et al.,
<xref ref-type="bibr" rid="B79">2011</xref>
)</td>
<td align="left" rowspan="1" colspan="1">IE</td>
<td align="left" rowspan="1" colspan="1">G, A</td>
<td align="left" rowspan="1" colspan="1"></td>
</tr>
<tr>
<td align="left" rowspan="1" colspan="1">SpliceTrap (Wu et al.,
<xref ref-type="bibr" rid="B193">2011</xref>
)</td>
<td align="left" rowspan="1" colspan="1">IE</td>
<td align="left" rowspan="1" colspan="1">G, A</td>
<td align="left" rowspan="1" colspan="1">Only handles exon skipping</td>
</tr>
<tr>
<td align="left" rowspan="1" colspan="1">NEUMA (Lee et al.,
<xref ref-type="bibr" rid="B88">2011</xref>
)</td>
<td align="left" rowspan="1" colspan="1">IE</td>
<td align="left" rowspan="1" colspan="1">G, A</td>
<td align="left" rowspan="1" colspan="1"></td>
</tr>
<tr>
<td align="left" rowspan="1" colspan="1">Solas (Richard et al.,
<xref ref-type="bibr" rid="B129">2010</xref>
)</td>
<td align="left" rowspan="1" colspan="1">IE</td>
<td align="left" rowspan="1" colspan="1">G, A</td>
<td align="left" rowspan="1" colspan="1"></td>
</tr>
<tr>
<td align="left" rowspan="1" colspan="1">rSeq (Jiang and Wong,
<xref ref-type="bibr" rid="B72">2009</xref>
)</td>
<td align="left" rowspan="1" colspan="1">IE</td>
<td align="left" rowspan="1" colspan="1">G, A</td>
<td align="left" rowspan="1" colspan="1"></td>
</tr>
<tr>
<td align="left" rowspan="1" colspan="1">RSEM (Li et al.,
<xref ref-type="bibr" rid="B92">2010</xref>
; Li and Dewey,
<xref ref-type="bibr" rid="B91">2011</xref>
)</td>
<td align="left" rowspan="1" colspan="1">IE</td>
<td align="left" rowspan="1" colspan="1">
<italic>De novo</italic>
</td>
<td align="left" rowspan="1" colspan="1">Requires a transcriptome assembler</td>
</tr>
</tbody>
</table>
<table-wrap-foot>
<p>
<italic>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)</italic>
.</p>
</table-wrap-foot>
</table-wrap>
<fig id="F3" position="float">
<label>Figure 3</label>
<caption>
<p>
<bold>Read coverage for the gene SCL33 in
<italic>A. thaliana</italic>
(data from Filichkin et al.,
<xref ref-type="bibr" rid="B43">2010</xref>
)</bold>
. The top panel shows the annotated gene model; the middle panel shows reads that map across splice junctions, with labels showing the number of reads that aligned across each junction and novel splice junctions highlighted in green. The bottom panel shows the distribution of reads across the gene. Here the read depth ranges from 1 to over 300, demonstrating that read coverage can be highly variable even across known exons (shaded regions on the graphs). This variability can make it difficult to distinguish between weakly expressed splice forms and background noise. This figure was generated by
<italic>SpliceGrapher</italic>
.</p>
</caption>
<graphic xlink:href="fpls-03-00018-g003"></graphic>
</fig>
<p>The first studies that predicted AS from RNA-Seq data were performed in mammals and focused on detection of exon skipping, the most common form of AS in these systems (see, e.g., Mortazavi et al.,
<xref ref-type="bibr" rid="B109">2008</xref>
; Pan et al.,
<xref ref-type="bibr" rid="B120">2008</xref>
; Sultan et al.,
<xref ref-type="bibr" rid="B164">2008</xref>
; Wang et al.,
<xref ref-type="bibr" rid="B186">2008b</xref>
; Tang et al.,
<xref ref-type="bibr" rid="B167">2009</xref>
); detection of exon skipping is relatively simple, requiring the detection of reads that span a splice junction that skips a known exon. Several programs are now available for assembling transcripts using ungapped and spliced alignments among them Cufflinks (Trapnell et al.,
<xref ref-type="bibr" rid="B171">2009</xref>
), and Scripture (Guttman et al.,
<xref ref-type="bibr" rid="B57">2010</xref>
). Whereas these programs were designed for mammalian genomes, TAU (Filichkin et al.,
<xref ref-type="bibr" rid="B43">2010</xref>
) was designed to predict transcripts for the model plant
<italic>A. thaliana</italic>
. The program predicted an assortment of non-canonical splice junctions associated with splice forms that were later validated via RT-PCR (Filichkin et al.,
<xref ref-type="bibr" rid="B43">2010</xref>
). However, its spliced alignment component has memory requirements that make it prohibitive with increasing read length.</p>
<p>Some methods simultaneously predict transcripts and their abundance, using read depth as information that can be used to untangle transcripts from each other. They are based on the idea that read depth can be expressed as a weighted sum across the transcripts that are represented in a sample. These include IsoLasso (Li et al.,
<xref ref-type="bibr" rid="B93">2011</xref>
) and NSMAP (Xia et al.,
<xref ref-type="bibr" rid="B195">2011</xref>
).</p>
<p>RNA-Seq data alone may not be sufficient to resolve splice forms unambiguously (Lacroix et al.,
<xref ref-type="bibr" rid="B84">2008</xref>
; Rogers et al.,
<xref ref-type="bibr" rid="B134">2012</xref>
). Accordingly, Cufflinks was recently enhanced to incorporate information from gene models into its transcript prediction method (Roberts et al.,
<xref ref-type="bibr" rid="B131">2011</xref>
); the TAU program is also able to leverage gene annotations. We developed the SpliceGrapher tool, which uses gene models to establish a context for interpreting evidence from EST or RNA-Seq alignments, and to predict novel AS events only when the evidence for them is strong (Rogers et al.,
<xref ref-type="bibr" rid="B134">2012</xref>
). Furthermore, our tool leaves the predictions in the form of splice graphs when transcripts cannot be unambiguously resolved. Our work shows the strength of this approach and the tradeoffs associated with using gene annotations to augment TAU or Cufflinks predictions.</p>
<p>Accurate splice junction identification is crucial for making accurate AS predictions, but the short length of NGS reads makes spliced alignment especially challenging. A splice junction may occur anywhere within a read, so the read may have just a few bases on one side of a junction. Therefore, methods that use simple heuristics such as the existence of canonical splice site dimers and acceptable intron lengths can lead to many false-positive splice junctions (Rogers et al.,
<xref ref-type="bibr" rid="B134">2012</xref>
). This highlights the importance of modeling splice junction sequence characteristics as implemented in MapSplice (Wang et al.,
<xref ref-type="bibr" rid="B187">2010</xref>
), for example, or the sophisticated sequence-based splice site models used by SpliceGrapher (Rogers et al.,
<xref ref-type="bibr" rid="B134">2012</xref>
) and PALMapper (Jean et al.,
<xref ref-type="bibr" rid="B71">2010</xref>
).</p>
<p>Not all plants have a reference genome available. In its absence,
<italic>de novo</italic>
transcriptome assembly packages can be used. These methods construct transcripts based on overlapping k-mers. One such program is ABySS (Simpson et al.,
<xref ref-type="bibr" rid="B156">2009</xref>
), a
<italic>de novo</italic>
genome assembler that has been used to predict AS patterns such as exon skipping, intron retention, and alternative 5′ splice sites (Birol et al.,
<xref ref-type="bibr" rid="B14">2009</xref>
). The recently developed Trinity suite (Grabherr et al.,
<xref ref-type="bibr" rid="B52">2011</xref>
) assembles reads into splice graphs, and predicts splice forms by tracing paths through the graph.</p>
<p>Tiling and exon-junction arrays are an alternative platform for studying AS (Clark et al.,
<xref ref-type="bibr" rid="B32">2002</xref>
; Johnson et al.,
<xref ref-type="bibr" rid="B74">2003</xref>
; Mockler and Ecker,
<xref ref-type="bibr" rid="B108">2005</xref>
; Cuperlovic-Culf et al.,
<xref ref-type="bibr" rid="B35">2006</xref>
; Hallegger et al.,
<xref ref-type="bibr" rid="B61">2010</xref>
). Tiling arrays provide an unbiased view of a genome using probes evenly spaced across its entire length, thus permitting discovery of novel AS events (Ner-Gaon and Fluhr,
<xref ref-type="bibr" rid="B111">2006</xref>
; Hazen et al.,
<xref ref-type="bibr" rid="B64">2009</xref>
). Ner-Gaon and Fluhr (
<xref ref-type="bibr" rid="B111">2006</xref>
) found that whole-genome tiling arrays identified as much AS activity in
<italic>A. thaliana</italic>
as the most comprehensive EST-based studies. However, microarrays suffer from several limitations such as cross-hybridization and varying probe binding affinities that increase hybridization signal noise, and poor sensitivity for detecting isoforms that differ only in a few nucleotides, and also in cases where minor isoforms are expressed at low levels (Mockler and Ecker,
<xref ref-type="bibr" rid="B108">2005</xref>
; Hallegger et al.,
<xref ref-type="bibr" rid="B61">2010</xref>
). These factors, combined with the success of RNA-Seq, have impeded adoption of this technology by plant researchers.</p>
<p>Visualization of NGS data is now supported by several genome browsers, including Stein et al. (
<xref ref-type="bibr" rid="B163">2002</xref>
), the Integrated Genome Browser (IGB; Nicol et al.,
<xref ref-type="bibr" rid="B114">2009</xref>
), the Integrative Genomics Viewer (Robinson et al.,
<xref ref-type="bibr" rid="B133">2011</xref>
), and GenomeView (Abeel,
<xref ref-type="bibr" rid="B1">2011</xref>
), that can display short-read coverage graphs and transcripts. SpliceGrapher (Rogers et al.,
<xref ref-type="bibr" rid="B134">2012</xref>
), designed specifically for AS analysis, includes a plotting tool that displays splice graphs, gene models, and short-read coverage graphs, and highlights AS events. It generates static plots that cannot be manipulated the way a genome browser visualization can, but we have found them to be very useful for viewing of plant transcriptome data, because of the relatively short intron length.</p>
</sec>
</sec>
<sec>
<label>3</label>
<title>Differential AS</title>
<p>Detection of differentially expressed genes is perhaps the most common analysis task performed on microarray data, and many methods are available (Grant et al.,
<xref ref-type="bibr" rid="B53">2007</xref>
; Durinck,
<xref ref-type="bibr" rid="B38">2008</xref>
). NGS technology and the availability of microarrays that allow a distinction between splice forms (e.g., exon-junction and tiling arrays) are opening the possibility of extending this idea to detection of differential AS: genes that show differences in the patterns of AS under different conditions or developmental stages. Several studies have revealed regulated AS in plants (Yoshimura et al.,
<xref ref-type="bibr" rid="B200">2002</xref>
; Iida et al.,
<xref ref-type="bibr" rid="B68">2004</xref>
; Palusa et al.,
<xref ref-type="bibr" rid="B118">2007</xref>
; Reddy,
<xref ref-type="bibr" rid="B126">2007</xref>
; Schindler et al.,
<xref ref-type="bibr" rid="B141">2008</xref>
; Simpson and Brown,
<xref ref-type="bibr" rid="B151">2008</xref>
; Filichkin et al.,
<xref ref-type="bibr" rid="B43">2010</xref>
; Simpson et al.,
<xref ref-type="bibr" rid="B154">2010</xref>
; Kumar et al.,
<xref ref-type="bibr" rid="B80">2011</xref>
; Staiger and Green,
<xref ref-type="bibr" rid="B160">2011</xref>
), and detection of differential AS by high-throughput methods will further enhance our understanding of the role of AS, and help in developing a plant condition-dependent splicing code.</p>
<p>A number of studies have used microarrays to estimate isoform expression levels (Mockler and Ecker,
<xref ref-type="bibr" rid="B108">2005</xref>
; Blencowe et al.,
<xref ref-type="bibr" rid="B18">2009</xref>
; Hallegger et al.,
<xref ref-type="bibr" rid="B61">2010</xref>
), and methods have been proposed to detect splice form expression levels from exon arrays (Purdom et al.,
<xref ref-type="bibr" rid="B123">2008</xref>
; Xing et al.,
<xref ref-type="bibr" rid="B198">2008</xref>
). These methods have been used to study AS in mammals (Xing and Lee,
<xref ref-type="bibr" rid="B196">2008</xref>
; Warzecha et al.,
<xref ref-type="bibr" rid="B192">2009</xref>
), but have not been widely adopted in plants (Love et al.,
<xref ref-type="bibr" rid="B100">2010</xref>
; Rehrauer et al.,
<xref ref-type="bibr" rid="B128">2010</xref>
). Microarrays have several limitations in this context including a large number of false-positive AS predictions (Gaidatzis et al.,
<xref ref-type="bibr" rid="B47">2009</xref>
).</p>
<p>NGS data address many of the limitations of microarrays – they are more sensitive to weakly expressed isoforms and have a broader dynamic range (Roy et al.,
<xref ref-type="bibr" rid="B135">2011</xref>
) – but they also introduce considerable challenges. For example, it may not be possible to find a unique mapping between reads and the reference genome (Lacroix et al.,
<xref ref-type="bibr" rid="B84">2008</xref>
). Additionally, read coverage can be highly variable due to a variety of factors such as library preparation methods, flow cell characteristics and reads that align to multiple locations (Mortazavi et al.,
<xref ref-type="bibr" rid="B109">2008</xref>
; Fang and Cui,
<xref ref-type="bibr" rid="B41">2011</xref>
).</p>
<p>Detection of differential AS requires establishing accurate models for the splice forms represented in the data (discussed earlier), quantifying splice form expression in a way that allows detection of weakly expressed splice forms, and performing statistical tests to differentiate between the relative expression of splice forms across samples. Several measures are used to report expression levels based on short-read coverage. A common metric is RPKM (reads per kilobase per million reads), developed in (Mortazavi et al.,
<xref ref-type="bibr" rid="B109">2008</xref>
). Many methods report expression levels in RPKM (Mortazavi et al.,
<xref ref-type="bibr" rid="B109">2008</xref>
; Jiang and Wong,
<xref ref-type="bibr" rid="B72">2009</xref>
; Guttman et al.,
<xref ref-type="bibr" rid="B57">2010</xref>
; Feng et al.,
<xref ref-type="bibr" rid="B42">2011</xref>
; Kim et al.,
<xref ref-type="bibr" rid="B79">2011</xref>
; Xia et al.,
<xref ref-type="bibr" rid="B195">2011</xref>
) or an equivalent measure such as FPKM (fragments per kilobase per million reads; Nicolae et al.,
<xref ref-type="bibr" rid="B115">2010</xref>
; Trapnell et al.,
<xref ref-type="bibr" rid="B173">2010</xref>
). When comparing expression levels between genes or isoforms, RPKM may bias estimates in favor of longer sequences (Costa et al.,
<xref ref-type="bibr" rid="B33">2010</xref>
), so other measures have been proposed (see, e.g., Lee et al.,
<xref ref-type="bibr" rid="B88">2011</xref>
). Comparisons with microarrays have confirmed that RNA-Seq data can provide at least as much sensitivity as microarrays (Mortazavi et al.,
<xref ref-type="bibr" rid="B109">2008</xref>
), but without the limitations previously mentioned. Several studies have validated RNA-Seq expression estimates using qRT-PCR and these have demonstrated that the two methods produce estimates that are consistent with one another (see, e.g., Wang et al.,
<xref ref-type="bibr" rid="B186">2008b</xref>
; Nicolae et al.,
<xref ref-type="bibr" rid="B115">2010</xref>
; Richard et al.,
<xref ref-type="bibr" rid="B129">2010</xref>
; Lee et al.,
<xref ref-type="bibr" rid="B88">2011</xref>
).</p>
<p>Recently software has been developed to measure differential AS from RNA-Seq. For example, Cuffdiff, an extension of the Cufflinks package, compares transcript expression levels on the basis of read coverage in two experiments (Trapnell et al.,
<xref ref-type="bibr" rid="B173">2010</xref>
). The transcripts may be generated from Cufflinks or may come from annotated gene models. Cuffdiff generates statistical test results for fold changes at either the transcript or gene level. Studies in mammals have used these tools to compare differentially expressed splice forms between different conditions. The authors of the Cufflinks software used it to study transcriptional changes in mouse myoblast cell lines. They identified 70 genes in which the prevalent splice forms changed as cells transitioned from myocyte production to myotube fusion (Trapnell et al.,
<xref ref-type="bibr" rid="B173">2010</xref>
). In another study, Twine et al. (
<xref ref-type="bibr" rid="B174">2011</xref>
) found statistically significant differences in the expression of alternative splice forms between normal and diseased brain cells that yielded insights into the progression of Alzheimer’s disease.</p>
<p>Few plant studies have applied differential AS tools. Differential AS in response to stresses has been reported in
<italic>A. thaliana</italic>
(Filichkin et al.,
<xref ref-type="bibr" rid="B43">2010</xref>
). In addition, Jiao and Meyerowitz (
<xref ref-type="bibr" rid="B73">2010</xref>
) investigated flower development in
<italic>A. thaliana</italic>
, in part using the program rSeq (Jiang and Wong,
<xref ref-type="bibr" rid="B72">2009</xref>
) to estimate isoform expression levels. Their analysis revealed differentially expressed splice forms for three genes (
<italic>APETALA1</italic>
,
<italic>APETALA3</italic>
, and
<italic>AGAMOUS</italic>
) between two flowering stages. Analysis of intron retention events showed that they occurred more frequently with the U12 spliceosomes than with the U2 spliceosomes (Jiao and Meyerowitz,
<xref ref-type="bibr" rid="B73">2010</xref>
).</p>
<p>These results underscore the potential for using RNA-Seq to probe AS under changing conditions such as organism development or stresses. With NGS, researchers are no longer restricted by the cost of ESTs or a dearth of plant-specific microarrays. In addition, a growing number of software tools are helping to automate the analysis of transcript expression from NGS data. The availability of some NGS analysis pipelines through the iPlant cyber infrastructure is going to help plant researchers who do not have the expertise or the required computational infrastructure to analyze their RNA-Seq data (Goff et al.,
<xref ref-type="bibr" rid="B50">2011</xref>
). NGS data analysis is a focus area for iPlant, and several methods are available, including Cufflinks, and several read mapping algorithms; efforts are underway to add the SpliceGrapher package to the iPlant infrastructure. These factors combined, provide unprecedented opportunity for researchers to explore the role of AS across all plant species.</p>
</sec>
<sec>
<label>4</label>
<title>Regulation of Splicing</title>
<p>An important question in AS is regulation of splice site choice. The splicing code, i.e., the set of biological rules for determining the splicing outcomes in both constitutive and alternative splicing, is only beginning to be addressed in animals (Barash et al.,
<xref ref-type="bibr" rid="B7">2010a</xref>
) and very little is known about it in plants.</p>
<sec>
<label>4.1</label>
<title>Gene architecture and composition in pre-mRNA splicing</title>
<p>Comparative genomics studies on gene structure have revealed major differences in the architecture of plant and animal genes (Reddy,
<xref ref-type="bibr" rid="B126">2007</xref>
). Plant genes are generally shorter than animal genes with fewer exons and significantly shorter introns. In animals, the average size of exons is about 140 nucleotides, whereas introns are several thousands of nucleotides long (average 3000 nucleotides in humans). In contrast, plant exons and introns are about the same length (in
<italic>Arabidopsis</italic>
for example, the average length of exons, and introns is 173 and 172 nucleotides, respectively) or about twice the size of exons in rice (e.g., exons 193 and introns 433 nucleotides; Reddy,
<xref ref-type="bibr" rid="B126">2007</xref>
; Baek et al.,
<xref ref-type="bibr" rid="B6">2008</xref>
). Based on the smaller size of plant introns relative to animal introns and the high number of intron retention events, it is thought that the primary mode of splicing in plants is accomplished via an intron definition mechanism. However, there are plants whose introns are longer, but still have a high rate of intron retention (e.g., grape, with intron length of around 970, computed using the current genome annotations), and animals with short introns and exon skipping rates characteristic of mammalian systems (e.g.,
<italic>Tetraodon nigroviridis</italic>
(puffer fish) which has introns of length 600 bp on average).</p>
<p>Plant introns are rich in U and UA nucleotides and exons are G-rich (Goodall and Filipowicz,
<xref ref-type="bibr" rid="B51">1989</xref>
). Several studies indicate that
<italic>cis</italic>
-elements involved in intron recognition in plants are likely to be different from those in yeast and animals (Reviewed in Reddy,
<xref ref-type="bibr" rid="B125">2001</xref>
; Schuler,
<xref ref-type="bibr" rid="B145">2008</xref>
). For instance, animal introns are not accurately spliced in plants and vice versa. High U or UA content in introns was found to be important for splice site recognition and efficient splicing of U2 type and U12 type introns (Goodall and Filipowicz,
<xref ref-type="bibr" rid="B51">1989</xref>
; Lambermon et al.,
<xref ref-type="bibr" rid="B87">2000</xref>
; Lorkovic et al.,
<xref ref-type="bibr" rid="B99">2000</xref>
; Reddy,
<xref ref-type="bibr" rid="B125">2001</xref>
; Lewandowska et al.,
<xref ref-type="bibr" rid="B89">2004</xref>
; Simpson et al.,
<xref ref-type="bibr" rid="B153">2004</xref>
), suggesting the presence of proteins that interact with U or UA rich elements. In tobacco, three proteins (UBP1, RBP45, RBP47) that bind to intronic sequences have been characterized. UBP1 has been shown to be necessary for efficient splicing of pre-mRNA as well as mRNA accumulation (Goodall and Filipowicz,
<xref ref-type="bibr" rid="B51">1989</xref>
; Lambermon et al.,
<xref ref-type="bibr" rid="B87">2000</xref>
). An analysis of AS in
<italic>Medicago</italic>
, poplar,
<italic>Arabidopsis</italic>
and rice, revealed that except for rice, AS was most prevalent for introns with decreased UA content (Baek et al.,
<xref ref-type="bibr" rid="B6">2008</xref>
). In addition to intronic sequences, exonic purine-rich sequences have been shown to influence splice site choice (Egoavil et al.,
<xref ref-type="bibr" rid="B39">1997</xref>
; McCullough and Schuler,
<xref ref-type="bibr" rid="B105">1997</xref>
; Lewandowska et al.,
<xref ref-type="bibr" rid="B89">2004</xref>
; Schuler,
<xref ref-type="bibr" rid="B145">2008</xref>
). However, aside from broad surveys of gene architecture, composition, and mutational analysis of splice sites, there has been little work performed to uncover the putative
<italic>cis-</italic>
elements that would provide substantial computational evidence for the intron definition model as the primary means by which intron-retaining transcripts are generated.</p>
</sec>
<sec>
<label>4.2</label>
<title>
<italic>Cis</italic>
-elements in pre-mRNAs that control splicing</title>
<p>In metazoans, four core sequence elements at the exon/intron boundaries and the intron/exon boundaries are necessary for splice site recognition by the spliceosome. These include (i) a motif at the 5′ splice site (SS) or donor site with a conserved GU dinucleotide, (ii) another motif at the 3′ SS or acceptor site with a conserved AG dinucleotide, (iii) a stretch of pyrimidines (polypyrimidine tract) upstream of the 3′ SS and (iv) a branch point 17–40 nucleotides upstream of the polypyrimidine tract. Components of the spliceosome recognize these core signals. U1 snRNP recognizes the 5′ SS, U2AF35 and U2AF65 recognize the 3′ SS and polypyrimidine tract, respectively and U2 snRNP recognizes the branch point. The 5′ and 3′ SSs are very similar between plants and animals, and the polypyrimidine tract in plants is rich in Us (Reddy,
<xref ref-type="bibr" rid="B126">2007</xref>
). Several mutants in which splice sites are affected have been isolated and consequences of these mutations have been reviewed extensively (Brown,
<xref ref-type="bibr" rid="B22">1996</xref>
; Schuler,
<xref ref-type="bibr" rid="B145">2008</xref>
). Although these core elements are conserved across species, they are very short and they alone, are not sufficient to define exons and introns and recruit the splicing machinery. Additional sequences in exons and introns, which are collectively referred to as splicing regulatory elements (SREs), are important for constitutive splicing as well as AS. The SREs function as either splicing enhancers or suppressors and affect splice site choice. The efficiency by which the spliceosome recognizes exons and introns is in part determined by numerous protein factors that recognize these SREs. Depending on the location of SREs and their effect on splicing, they are grouped into four classes: exonic splicing enhancers (ESEs), exonic splicing silencers (ESSs), intronic splicing enhancers (ISEs), and intronic splicing silencers (ISSs; Black,
<xref ref-type="bibr" rid="B15">2003</xref>
; Chasin,
<xref ref-type="bibr" rid="B28">2007</xref>
; Wang and Burge,
<xref ref-type="bibr" rid="B188">2008</xref>
; Wang et al.,
<xref ref-type="bibr" rid="B190">2009b</xref>
).</p>
<sec>
<label>4.2.1</label>
<title>Computational studies to predict SREs</title>
<p>In animals systems, many studies have been performed to predict SREs. Most of these studies focused on exon skipping as this is the most prevalent AS event (Fairbrother et al.,
<xref ref-type="bibr" rid="B40">2002</xref>
; Sorek et al.,
<xref ref-type="bibr" rid="B158">2004</xref>
; Dror et al.,
<xref ref-type="bibr" rid="B37">2005</xref>
; Ohler et al.,
<xref ref-type="bibr" rid="B117">2005</xref>
; Ratsch et al.,
<xref ref-type="bibr" rid="B124">2005</xref>
; Yeo et al.,
<xref ref-type="bibr" rid="B199">2005</xref>
; Barash et al.,
<xref ref-type="bibr" rid="B7">2010a</xref>
,
<xref ref-type="bibr" rid="B8">b</xref>
) and some on tandem 3′ splice sites (Akerman and Mandel-Gutfreund,
<xref ref-type="bibr" rid="B2">2006</xref>
; Xia et al.,
<xref ref-type="bibr" rid="B194">2006</xref>
). Computational tools to predict ESEs such as RESCUE-ESE (Relative Enhancer and Silencer Classification by Unanimous Enrichment) have been used to predict hexameric ESEs in animals (Fairbrother et al.,
<xref ref-type="bibr" rid="B40">2002</xref>
). Several other programs have been developed to identify splicing elements or to simulate the splicing of primary transcripts (Schwartz et al.,
<xref ref-type="bibr" rid="B146">2009a</xref>
). These methods are based on the rationale that alternatively spliced exons may contain sequence elements that are absent in constitutively spliced exons and introns or vice versa. Analysis of sequences that are over or under represented in alternatively spliced exons compared to constitutively spliced exons has led to prediction of sequences that could contribute to AS. Experimental validation of some of these has led to identification of many
<italic>cis</italic>
regulatory elements involved in regulating splice site choice either by enhancing or silencing the AS of a particular exon. These studies have shown that
<italic>cis</italic>
-elements involved in splicing can exist predominantly in exons and also in introns in some cases (Blencowe,
<xref ref-type="bibr" rid="B16">2000</xref>
; Chasin,
<xref ref-type="bibr" rid="B28">2007</xref>
). The theme that emerged from these analyses is that most SREs often share common features: they are short (about 6–10 nucleotides) and loosely conserved between different targets of the same protein, suggesting a flexible nature of the RNA-protein interaction. These motifs, that are individually weak but present in multiple copies, act as binding sites for competing trans factors (Ladd and Cooper,
<xref ref-type="bibr" rid="B85">2002</xref>
). The most commonly studied elements are ESEs and the SR family of splicing regulators that bind them. It is now clear that many, if not all exons, constitutively or alternatively spliced, harbor ESEs that bind to SR proteins (Black,
<xref ref-type="bibr" rid="B15">2003</xref>
; Chasin,
<xref ref-type="bibr" rid="B28">2007</xref>
; Barash et al.,
<xref ref-type="bibr" rid="B7">2010a</xref>
). By contrast, ESS elements are usually bound by hnRNP proteins and can repress interactions across exons and introns that participate in spliceosome assembly (Blencowe,
<xref ref-type="bibr" rid="B17">2006</xref>
; Chasin,
<xref ref-type="bibr" rid="B28">2007</xref>
).</p>
<p>A recent compendium of
<italic>cis</italic>
-elements was used in the development of a mammalian “splicing code.” It included 171 known motifs and 326 new motifs that were used to predict splicing patterns of exons (Barash et al.,
<xref ref-type="bibr" rid="B7">2010a</xref>
). While most research has focused on discovering these elements within mammalian species, some progress has been made in identifying ESE motifs in
<italic>Arabidopsis</italic>
(Pertea et al.,
<xref ref-type="bibr" rid="B122">2007</xref>
). The goal of the study was to improve splice site prediction accuracy in
<italic>Arabidopsis</italic>
by incorporating potential ESE motifs into splice site recognition programs. The authors constructed a dataset (ESEAra) comprised of around 4000 high quality
<italic>Arabidopsis</italic>
gene models containing close to 17,500 coding exons. They extracted 50 bp regions flanking the boundaries of internal and terminal exons in the ESEAra dataset, established frequency distributions of hexamers near weak and strong splice sites, and used a scoring threshold to identify 84 putative ESE hexamers in these regions. Of these 84 ESE hexamers, 35 (12 at the 5′ end, 6 at the 3′ end, and 17 at both ends) had experimental evidence for ESE activity in
<italic>Arabidopsis</italic>
(Pertea et al.,
<xref ref-type="bibr" rid="B122">2007</xref>
). Several of the detected motifs exhibit the GAAGAA hexamer, which is a part of a recognized human ESE.</p>
<p>Aside from the above study, our catalog of plant
<italic>cis</italic>
regulatory elements involved in AS is limited. Over two decades ago, it was shown that AU-rich sequence elements in plant introns are required for plant pre-mRNA splicing (Goodall and Filipowicz,
<xref ref-type="bibr" rid="B51">1989</xref>
; Filipowicz et al.,
<xref ref-type="bibr" rid="B44">1995</xref>
). Since then, our knowledge of trans factor binding sites has been constrained to putative or few experimentally defined regulatory motifs. Yoshimura et al. (
<xref ref-type="bibr" rid="B200">2002</xref>
) experimentally identified an AU-rich splicing regulatory element (GU[G|C|A]UUGC[C|U]UAUUUGAAUUGCAG) located in an exon/intron boundary responsible for tissue-specific AS of tobacco chloroplast ascorbate peroxidase (chlAPX) pre-mRNA. Four isoforms are produced via AS of the 3′-terminal region of chlAPX pre-mRNA (tAPX-I, sAPX-I, -II, and -III) due to alternative excision of intron 11 and intron 12. Subsequent site directed mutagenesis of the
<italic>cis</italic>
regulatory element and RNA gel mobility shift assays confirmed interaction with a particular trans factor expressed in leaves but not roots. The identity of this trans factor remains unknown. Using two 9G8 (one of the mammalian SR proteins) binding sequences in gel shift assays, it was shown that AtRSZ22 also binds to those sequences (Lopato et al.,
<xref ref-type="bibr" rid="B97">1999</xref>
). AtGRP7 (
<italic>Arabidopsis thaliana</italic>
glycine-rich RNA binding protein) regulates AS of its own pre-mRNA as well as its paralog, AtGRP8, pre-mRNA by producing a PTC-containing splice variant when its levels increase. Similarly, AtGRP8 autoregulates its AS and AtGRP7 pre-mRNA AS (Schoning et al.,
<xref ref-type="bibr" rid="B143">2007</xref>
,
<xref ref-type="bibr" rid="B142">2008</xref>
). This regulation involves binding of AtGRP7 to its pre-mRNA and the pre-mRNA of AtGRP8 and vice versa (Schoning et al.,
<xref ref-type="bibr" rid="B143">2007</xref>
,
<xref ref-type="bibr" rid="B142">2008</xref>
). The sequences in these pre-mRNAs that bind to
<italic>AtGRP7</italic>
and
<italic>AtGRP8</italic>
have been identified (Schoning et al.,
<xref ref-type="bibr" rid="B143">2007</xref>
,
<xref ref-type="bibr" rid="B142">2008</xref>
). Although there are several other instances of auto- and cross-regulation of AS of several spliceosomal proteins,
<italic>cis</italic>
-elements involved in regulation of AS are not known (Kalyna et al.,
<xref ref-type="bibr" rid="B76">2003</xref>
; Reddy,
<xref ref-type="bibr" rid="B126">2007</xref>
; Barta et al.,
<xref ref-type="bibr" rid="B12">2008</xref>
; Stauffer et al.,
<xref ref-type="bibr" rid="B162">2010</xref>
; Reddy and Ali,
<xref ref-type="bibr" rid="B127">2011</xref>
).</p>
<p>Because intron retention is so pervasive in plants, uncovering the sequence elements that lead to this splice form would be a valuable breakthrough, as there are fundamental implications for post-transcriptional regulation of gene expression either by NMD/RUST (Lewis et al.,
<xref ref-type="bibr" rid="B90">2003</xref>
), or by generating new target sites for miRNA binding. There is evidence that generation of premature termination codon in plants by AS leads to degradation of mRNAs (Arciga-Reyes et al.,
<xref ref-type="bibr" rid="B4">2006</xref>
; Schoning et al.,
<xref ref-type="bibr" rid="B142">2008</xref>
; Kurihara et al.,
<xref ref-type="bibr" rid="B81">2009</xref>
; Barbazuk,
<xref ref-type="bibr" rid="B9">2010</xref>
; Palusa and Reddy,
<xref ref-type="bibr" rid="B119">2010</xref>
; Staiger and Green,
<xref ref-type="bibr" rid="B160">2011</xref>
). In humans, it has been predicted that retained introns in the 3′ UTR can create and/or increase putative miRNA targets (Tan et al.,
<xref ref-type="bibr" rid="B166">2007</xref>
). Analysis of all known retained introns and their flanking exons can be performed computationally to determine what sequence elements contribute to intron retention. Building a set of potential regulatory sequences would lend predictive power to discern the likelihood that a given gene will produce an intron-retaining transcript.</p>
</sec>
<sec>
<label>4.2.2</label>
<title>Validation of computationally predicted SREs</title>
<p>In animals,
<italic>in vitro</italic>
and
<italic>in vivo</italic>
splicing assays with pre-mRNAs containing wild type and mutated putative SREs have been used to validate predictions. Unfortunately,
<italic>in vitro</italic>
methods that employ S100 or nuclear extracts for splicing assays for identifying SREs cannot be applied to plants, as there is no plant-derived
<italic>in vitro</italic>
splicing assay system. However, the validity of computationally predicted plant
<italic>cis</italic>
-elements in splicing regulation can be tested using two different approaches. In one approach, one can generate mutations in the predicted
<italic>cis</italic>
-element and compare the splicing of wild type to mutated gene in a transient or stable expression system, which will allow analysis of splicing in its natural exon/intron context. Alteration of predicted
<italic>cis</italic>
-elements can be done in a high-throughput manner using the strategy described in Figure
<xref ref-type="fig" rid="F4">4</xref>
. Splicing of the wild type and mutated gene can be first tested in protoplasts by RT-PCR using a forward primer corresponding to the tag and a reverse gene-specific primer. If the candidate gene is not expressed in mesophyll cells then it can be analyzed in transgenic lines. If there is an SRE then one can test the importance of individual bases in that element by site directed mutagenesis. If there are two
<italic>cis</italic>
-elements in a gene that are complementary to each other and have the potential to form a stem/stem-loop structure, then a change in the sequence of one of the two elements or both to disrupt base pairing should affect splicing. To confirm that the base pairing in the two
<italic>cis</italic>
-elements, not the sequence itself, is necessary, one can mutate both elements in such a way that the sequence is changed but the two elements are complementary to each other. If this still shows regulated splicing then it is likely that the base pairing is involved in regulated splicing.</p>
<fig id="F4" position="float">
<label>Figure 4</label>
<caption>
<p>
<bold>Approach to mutate a predicted
<italic>cis</italic>
-element: Generation of a construct in which a predicted
<italic>cis</italic>
-element (shown in red) is changed (shown in blue) involves two rounds of PCR</bold>
. In the first PCR the target gene with two primers sets (F1/R1 and F2/R2). The F1 and R1 primer set amplifies the gene from the initiation codon to the predicted
<italic>cis</italic>
-element and F2 and R2 will amplify from the predicted
<italic>cis</italic>
-element to the stop codon. Primers F1 and R2, in addition to gene-specific sequence (shown in green), will be tailed with sequences complementary to Gateway vector primers (shown in dark yellow). Similarly, primers R1 and F2, in addition to gene-specific sequence, will be tailed with the changed sequence in the predicted
<italic>cis</italic>
-element (shown in blue). In the second PCR, the two gene fragments from the first PCR will be mixed. This overlapping template will be amplified using primers complementary to primers F1 and R2 tailed with the attB1 and attB2 Gateway sequences, which can then be cloned into a Gateway donor vector and into a plant transformation vector with a tag as a fusion to the N-terminus. The wild type gene will be cloned in a similar fashion except that only one PCR will be done with the F1/R2 primer set containing the entire attB1 and attB2 Gateway sequences.</p>
</caption>
<graphic xlink:href="fpls-03-00018-g004"></graphic>
</fig>
<p>A second approach for validating
<italic>cis</italic>
-elements in an intron is to use a reporter gene (GFP) that is interrupted by a test intron that contains predicted
<italic>cis</italic>
-elements. By following the gene’s splicing pattern, one can determine if the signals are present exclusively in the inserted part of the gene. A similar approach could be used to study
<italic>cis</italic>
-elements in other regions of a gene. Identified SREs can then be further analyzed by using site directed mutagenesis.</p>
<p>
<italic>In vivo</italic>
analysis of pre-mRNA splicing by expressing splicing reporters in transient assays (e.g., protoplasts or leaf transfections) have not been used widely (Gniadkowski et al.,
<xref ref-type="bibr" rid="B49">1996</xref>
; Lambermon et al.,
<xref ref-type="bibr" rid="B86">2002</xref>
; Lewandowska et al.,
<xref ref-type="bibr" rid="B89">2004</xref>
; Isshiki et al.,
<xref ref-type="bibr" rid="B70">2006</xref>
; Schuler,
<xref ref-type="bibr" rid="B145">2008</xref>
; Stauffer et al.,
<xref ref-type="bibr" rid="B162">2010</xref>
). The lack of a plant-derived
<italic>in vitro</italic>
splicing system makes transient assay systems very attractive for studying the constitutive splicing and AS of pre-mRNAs for genes that are expressed in leaves.</p>
</sec>
<sec>
<label>4.2.3</label>
<title>Experimental approaches for transcriptome-wide identification of SREs</title>
<p>RNA binding proteins (RBPs) with characteristic RNA binding motifs such as the RNA recognition motif (RRM) and the K-homology (KH) domain that interact with specific RNAs, profoundly impact gene expression at various levels (transcription, capping, splicing, polyadenylation, biogenesis of miRNAs and siRNAs, RNA transport, localization and degradation, small RNA regulated gene expression) and have been shown to play important roles in development and disease in animals (Licatalosi and Darnell,
<xref ref-type="bibr" rid="B96">2010</xref>
). In plants, a large repertoire of RNA binding proteins have been identified using bioinformatic tools. In
<italic>Arabidopsis</italic>
there are more than two hundred RNA binding proteins (RBPs), including 200 RRM containing RBPs and 30 KH domain proteins (Lorkovic,
<xref ref-type="bibr" rid="B98">2009</xref>
). Many plant RBPs are unique to plants, suggesting that they are likely to have novel RNA targets and perform plant-specific functions. A large fraction of RBPs are implicated in pre-mRNA splicing (Reddy,
<xref ref-type="bibr" rid="B126">2007</xref>
; Simpson et al.,
<xref ref-type="bibr" rid="B152">2008</xref>
; Lorkovic,
<xref ref-type="bibr" rid="B98">2009</xref>
; Reddy and Ali,
<xref ref-type="bibr" rid="B127">2011</xref>
; Staiger and Green,
<xref ref-type="bibr" rid="B160">2011</xref>
). Interestingly, many pre-mRNAs encodings RBPs (e.g., pre-mRNAs of SR and SR-like proteins, glycine-rich RNA binding proteins, and other spliceosomal proteins) are extensively alternatively spliced in plants and there is tight regulation of AS of these genes (Isshiki et al.,
<xref ref-type="bibr" rid="B70">2006</xref>
; Palusa et al.,
<xref ref-type="bibr" rid="B118">2007</xref>
; Barta et al.,
<xref ref-type="bibr" rid="B12">2008</xref>
; Simpson et al.,
<xref ref-type="bibr" rid="B154">2010</xref>
; Stauffer et al.,
<xref ref-type="bibr" rid="B162">2010</xref>
; Staiger and Green,
<xref ref-type="bibr" rid="B160">2011</xref>
). Protein-RNA interactions are crucial in many aspects of RNA metabolism. These interactions are dependent on RNA sequence elements in RNAs that interact specifically with proteins. Although the
<italic>in vivo</italic>
RNA targets of most plant RBPs are unknown, genetic studies with some of the RBPs indicate a crucial role for these in different developmental processes (flowering time, flower development, circadian responses) and various biotic and abiotic stress-signaling pathways (Reddy,
<xref ref-type="bibr" rid="B126">2007</xref>
; Simpson et al.,
<xref ref-type="bibr" rid="B152">2008</xref>
; Lorkovic,
<xref ref-type="bibr" rid="B98">2009</xref>
; Staiger and Green,
<xref ref-type="bibr" rid="B160">2011</xref>
).</p>
<p>RNA sequences that bind to RBPs are generally analyzed using various methods. These include RNA immunoprecipitation (RIP), which involves co-immunoprecipitation of RNAs with RBPs without prior cross-linking to the RNA using an antibody to a specific RBP followed by sequencing of the precipitated RNAs (RIP-seq) or probing microarrays with precipitated RNA (RIP-chip; Brown et al.,
<xref ref-type="bibr" rid="B23">2001</xref>
; Tenenbaum et al.,
<xref ref-type="bibr" rid="B168">2002</xref>
; Barkan,
<xref ref-type="bibr" rid="B11">2009</xref>
; Wang et al.,
<xref ref-type="bibr" rid="B191">2009c</xref>
). The caveats of this approach include loss of true interactions because the transient and dynamic nature of RNA-protein interactions, thereby precluding co-IP of some RNAs, and at the same time, high noise levels due to the sticky nature of RNAs (Mili and Steitz,
<xref ref-type="bibr" rid="B107">2004</xref>
). Moreover, the binding RNA sequences represent both direct and indirect targets as most RBPs interact with other proteins and form complexes (Reddy,
<xref ref-type="bibr" rid="B126">2007</xref>
). Advances in efficient ways to UV crosslink RNAs with RBPs
<italic>in vivo</italic>
followed by immunoprecipitation of complexes with an RBP specific antibody (CLIP, Cross-Linking ImmunoPrecipitation) under stringent conditions and sequencing of RNA using high-throughput RNA sequencing (HITS-CLIP) have paved the way to map, in an unbiased manner, transcriptome-wide direct RNA targets of an RBP (Ule et al.,
<xref ref-type="bibr" rid="B177">2003</xref>
,
<xref ref-type="bibr" rid="B176">2005</xref>
,
<xref ref-type="bibr" rid="B178">2006</xref>
; Wang et al.,
<xref ref-type="bibr" rid="B190">2009b</xref>
; Darnell,
<xref ref-type="bibr" rid="B36">2010</xref>
; Licatalosi and Darnell,
<xref ref-type="bibr" rid="B96">2010</xref>
). In HITS-CLIP, cells/tissues are exposed to ultraviolet (UV) radiation, which generates covalent bonds between RNA and proteins that are in close contact (Ule et al.,
<xref ref-type="bibr" rid="B177">2003</xref>
,
<xref ref-type="bibr" rid="B176">2005</xref>
). Because of the covalent bonds, RNA-protein complexes can be purified under stringent conditions. A modified version of HITS-CLIP called Photoactivatable-Ribonucleoside-Enhanced Cross-linking and Immunoprecipitation (PAR-CLIP) enhances cross-linking efficiency and allows identification of the locations in the RNA that are involved in interacting with RBP (Hafner et al.,
<xref ref-type="bibr" rid="B59">2010a</xref>
). In PAR-CLIP, a photoreactive ribonucleoside analog, 4-thiouridine, is incorporated into nascent RNAs, which are then cross-linked to RBPs by exposing cells to UV radiation. RNAs that are complexed with a particular RBP are then immunoprecipitated using an antibody to that RBP. RNAs in immunoprecipitated samples are then converted into cDNA and sequenced using NGS platforms. Since the cross-linked RNA region shows T to C mutations in the sequenced cDNA, the binding region in RNA can be precisely identified (Hafner et al.,
<xref ref-type="bibr" rid="B59">2010a</xref>
).</p>
<p>An in-depth discussion on advantages and disadvantages of these methods is available in recent reviews (Barkan,
<xref ref-type="bibr" rid="B11">2009</xref>
; Darnell,
<xref ref-type="bibr" rid="B36">2010</xref>
; Hafner et al.,
<xref ref-type="bibr" rid="B60">2010b</xref>
). These methods could be applied to plants to create a transcriptome-wide map of RNA binding sequences that bind to a given RBP. These tools, developed with animal systems, are fairly recent and have the potential to uncover plant SREs, especially those involved in AS events that are prevalent in plants as compared to animals. These procedures can identify the binding site landscape or “RNA map” for plant SR proteins, one of the key regulators of pre-mRNA splicing and other RBPs. Incorporating the results from these studies into computational predictions should lead to a better understanding of the splicing code in plants.</p>
</sec>
<sec>
<label>4.2.4</label>
<title>RNA secondary structure and other properties that affect AS</title>
<p>In addition to the presence of SREs, there are several properties of pre-mRNA transcripts that were shown to affect AS. These include intron length, GC-content, splice site strength, and pre-mRNA secondary structure (Ladd and Cooper,
<xref ref-type="bibr" rid="B85">2002</xref>
; Chasin,
<xref ref-type="bibr" rid="B28">2007</xref>
; Shepard and Hertel,
<xref ref-type="bibr" rid="B150">2008</xref>
; Schwartz et al.,
<xref ref-type="bibr" rid="B147">2009b</xref>
; Labadorf et al.,
<xref ref-type="bibr" rid="B83">2010</xref>
). A comparison of retained and constitutively spliced introns identified using
<italic>Arabidopsis</italic>
tiling arrays revealed that retained introns tended to be shorter and to have higher GC-content than constitutively spliced introns (Ner-Gaon and Fluhr,
<xref ref-type="bibr" rid="B111">2006</xref>
). Similar characteristics were also observed for introns in rice (Zhang et al.,
<xref ref-type="bibr" rid="B202">2010</xref>
).</p>
<p>Sequence signals surrounding the 5′ and 3′ splice site junctions, polypyrimidine tract (PPT), and branch sites also impact splice site selection. For instance, in humans and plants it has been shown that splice sites flanking retained introns contain weaker signals than those flanking constitutively spliced introns (Kurmangaliyev and Gelfand,
<xref ref-type="bibr" rid="B82">2008</xref>
; Labadorf et al.,
<xref ref-type="bibr" rid="B83">2010</xref>
). Studies on PPT and branch sites (Brown,
<xref ref-type="bibr" rid="B22">1996</xref>
; Tolstrup et al.,
<xref ref-type="bibr" rid="B170">1997</xref>
) in plants suggest that their metazoan equivalents are more pronounced. However, their importance in correctly splicing pre-mRNA was shown by Simpson et al. (
<xref ref-type="bibr" rid="B155">2002</xref>
). This evidence suggests a need to generate a catalog for all AS variants centered at these signal-rich regions.</p>
<p>
<italic>Cis</italic>
regulatory elements in pre-mRNA secondary structure are known to regulate AS by sometimes affecting the recruitment of SR proteins (Buratti and Baralle,
<xref ref-type="bibr" rid="B24">2004</xref>
; Buratti et al.,
<xref ref-type="bibr" rid="B25">2004</xref>
). Formation of secondary structure between complementary repeat-pair sequences in two introns flanking a cassette exon is proposed to mask splice sites and cause exon skipping events (Lian and Garner,
<xref ref-type="bibr" rid="B94">2005</xref>
). A similar mechanism where complementary sequences in two exons can base pair across an intron may lead to intron retention. There are only a few examples in plants where RNA secondary structure has been shown to regulate splicing. In photosynthetic eukaryotes, AS of pre-mRNAs from genes encoding proteins involved in thiamine metabolism (THIC and THI4 in
<italic>Chlamydomonas</italic>
and THIC in
<italic>Arabidopsis</italic>
) is regulated by a thiamin pyrophosphate (TPP) binding riboswitch (Bocobza et al.,
<xref ref-type="bibr" rid="B19">2007</xref>
; Croft et al.,
<xref ref-type="bibr" rid="B34">2007</xref>
; Wachter et al.,
<xref ref-type="bibr" rid="B182">2007</xref>
). High levels of TPP result in its binding to an RNA aptamer in the pre-mRNA, which leads to production of splice variants with a PTC or long 3′UTR that are unstable (Bocobza and Aharoni,
<xref ref-type="bibr" rid="B20">2008</xref>
; Wachter,
<xref ref-type="bibr" rid="B181">2010</xref>
). In plants, the transcription factor IIIA (TFIIIA), which is necessary for transcription of 5S RNA, is alternatively spliced by inclusion or exclusion of its third exon; the splice variant with the included exon contains a PTC and is a target of NMD (Barbazuk,
<xref ref-type="bibr" rid="B9">2010</xref>
). This exon is thought to be derived by exonization of 5S RNA and is conserved from moss to flowering plants, but not in green algae and metazoans (Fu et al.,
<xref ref-type="bibr" rid="B46">2009</xref>
; Hammond et al.,
<xref ref-type="bibr" rid="B62">2009</xref>
). The 5S RNA-like element assumes a 5S RNA structure, which regulates AS of its own pre-mRNA. The 5S RNA-like structure binds ribosomal protein L5, which in turn promotes exon skipping and generation of a functional transcript, whereas in the absence of L5, this exon is included, and results in the production of a PTC-containing unstable transcript (Hammond et al.,
<xref ref-type="bibr" rid="B62">2009</xref>
), suggesting a tight regulation of functional TFIIIA mRNA by the L5 protein.</p>
</sec>
</sec>
</sec>
<sec>
<label>5</label>
<title>Regulation of Alternative Splicing by Chromatin Organization</title>
<p>Apart from the features mentioned above, several recent studies point to epigenetic regulation of AS. Access to DNA may be affected by chromatin organization or methylation, which could impact the rate at which a gene is transcribed thereby affecting AS (Luco et al.,
<xref ref-type="bibr" rid="B102">2011</xref>
). In animals, it is now well established that the recruitment of splicing factors to pre-mRNAs occurs co-transcriptionally by the RNA Pol II carboxy-terminal domain, but the completion of splicing may occur co-transcriptionally or post-transcriptionally (Pandya-Jones and Black,
<xref ref-type="bibr" rid="B121">2009</xref>
; Luco et al.,
<xref ref-type="bibr" rid="B102">2011</xref>
). Furthermore, the rate of elongation of transcription regulates AS, where rapid elongation favors recruitment of the splicing machinery to strong splice sites, thereby including exons with strong splice sites; whereas slow elongation allows recruitment of spliceosome to weak sites (Luco et al.,
<xref ref-type="bibr" rid="B102">2011</xref>
).</p>
<p>Some studies implicate a more direct role for chromatin remodeling enzymes. For instance, histone deacetylases in yeast and humans interact directly with U2 snRNP (Gunderson and Johnson,
<xref ref-type="bibr" rid="B55">2009</xref>
) and the histone methyltransferase with U1 snRNP-specific protein U1C (Ohkura et al.,
<xref ref-type="bibr" rid="B116">2005</xref>
) and alter splicing patterns. Furthermore, chromatin remodelers SWI/SNF in yeast and humans (e.g., Braham) also regulate AS by interacting with spliceosomal proteins and recruiting snRNPs (Batsche et al.,
<xref ref-type="bibr" rid="B13">2006</xref>
; Tyagi et al.,
<xref ref-type="bibr" rid="B175">2009</xref>
). In
<italic>Arabidopsis</italic>
, mutations in protein arginine methyl transferase 5 (PRMT5), which methylates arginine residues in histones and Sm spliceosomal proteins, impair the circadian rhythm (Sanchez et al.,
<xref ref-type="bibr" rid="B139">2010</xref>
). It has been shown that AS of core-clock regulated genes (e.g., pseudo response regulator 9) and other pre-mRNAs is altered in the
<italic>prmt5</italic>
mutants (Sanchez et al.,
<xref ref-type="bibr" rid="B139">2010</xref>
). Whether the observed effects in
<italic>prmt5</italic>
mutants are due to altered methylation of Sm proteins and/or due to epigenetic defects remains to be studied.</p>
<p>Genome-wide nucleosome positioning and methylation studies in
<italic>Arabidopsis</italic>
and humans revealed that DNA associated with nucleosomes is more highly methylated than the flanking DNA (Nahkuri et al.,
<xref ref-type="bibr" rid="B110">2009</xref>
; Chodavarapu et al.,
<xref ref-type="bibr" rid="B31">2010</xref>
). Furthermore, nucleosomes are enriched in exons, particularly at exon-intron and intron-exon boundaries. Included exons and exons with weak splice sites are highly enriched in nucleosomes (Schwartz et al.,
<xref ref-type="bibr" rid="B148">2009c</xref>
; Spies et al.,
<xref ref-type="bibr" rid="B159">2009</xref>
; Tilgner et al.,
<xref ref-type="bibr" rid="B169">2009</xref>
). These studies suggest a role for nucleosome positioning in defining exons as well as regulating splicing (Nahkuri et al.,
<xref ref-type="bibr" rid="B110">2009</xref>
; Schwartz et al.,
<xref ref-type="bibr" rid="B148">2009c</xref>
; Spies et al.,
<xref ref-type="bibr" rid="B159">2009</xref>
; Tilgner et al.,
<xref ref-type="bibr" rid="B169">2009</xref>
; Chodavarapu et al.,
<xref ref-type="bibr" rid="B31">2010</xref>
). Small interfering RNAs (siRNAs) trigger transcriptional gene silencing by inducing heterochromatin formation in a sequence specific manner. Targeting of siRNA to intronic or exonic sequences that are close to an alternative exon has been shown to regulate the splicing of that exon (Allo et al.,
<xref ref-type="bibr" rid="B3">2009</xref>
), suggesting a role for siRNAs in AS.</p>
</sec>
<sec>
<title>Conclusion</title>
<p>Elucidation of multiple layers of gene regulation is critical to understand how plants grow, differentiate and respond appropriately to their environment. Regulated pre-mRNA splicing is emerging as an important layer in gene regulation. Extensive studies aimed at identifying features that control splicing in animal cells suggest that the combination of multiple characteristics in pre-mRNAs, including loosely conserved
<italic>cis</italic>
-elements and/or secondary structure(s) in transcripts, chromatin modification, and the rate of transcription regulate splicing (Barash et al.,
<xref ref-type="bibr" rid="B7">2010a</xref>
; Schor et al.,
<xref ref-type="bibr" rid="B144">2010</xref>
; Luco et al.,
<xref ref-type="bibr" rid="B102">2011</xref>
). Thus far, our knowledge of AS regulation in plants is limited to a few experimentally determined ESE motifs in plants. A prerequisite for elucidating the splicing code for plants is to identify candidate SREs. New technological advances such as deep sequencing of RNA, together with
<italic>in vivo</italic>
RNA-RBP cross-linking methods (HITS-CLIP, PAR-CLIP) have paved the way to find SREs globally in animals. Although these methods are applicable to plants, they have yet to be employed. Determining the splicing code in plants requires analysis of entire transcriptomes across tissues and conditions, and relating the observed patterns of AS to a variety of sequence signals, including SREs, nucleosome positioning signals, and possibly other as-yet unknown factors that affect splice site choice. Addressing these challenges and integrating the results to formulate the plant splicing code is a daunting task that will require a significant effort from the plant research community.</p>
</sec>
<sec>
<title>Conflict of Interest Statement</title>
<p>The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.</p>
</sec>
</body>
<back>
<ack>
<p>Pre-mRNA research in our laboratories is funded by a grant from the National Science Foundation. We thank Julie Thomas for her comments on the manuscript.</p>
</ack>
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