Transcript Profiling by 3′-Untranslated Region Sequencing Resolves Expression of Gene Families

Abstract

Differences in gene expression underlie central questions in plant biology extending from gene function to evolutionary mechanisms and quantitative traits. However, resolving expression of closely related genes (e.g. alleles and gene family members) is challenging on a genome-wide scale due to extensive sequence similarity and frequently incomplete genome sequence data. We present a new expression-profiling strategy that utilizes long-read, high-throughput sequencing to capture the information-rich 3#-untranslated region (UTR) of messenger RNAs (mRNAs). Resulting sequences resolve gene-specific transcripts independent of a sequenced genome. Analysis of approximately 229,000 3#-anchored sequences from maize (Zea mays) ovaries identified 14,822 unique transcripts represented by at least two sequence reads. Total RNA from ovaries of drought-stressed wild-type and viviparous-1 mutant plants was used to construct a multiplex cDNA library. Each sample was labeled by incorporating one of 16 unique three-base key codes into the 3#-cDNA fragments, and combined samples were sequenced using a GS 20 454 instrument. Transcript abundance was quantified by frequency of sequences identifying each unique mRNA. At least 202 unique transcripts showed highly significant differences in abundance between wild-type and mutant samples. For a subset of mRNAs, quantitative differences were validated by real-time reverse transcription-polymerase chain reaction. The 3#-UTR profile resolved 12 unique cellulose synthase (CesA) transcripts in maize ovaries and identified previously uncharacterized members of a histone H1 gene family. In addition, this method resolved nearly identical paralogs, as illustrated by two auxin-repressed, dormancyassociated (Arda) transcripts, which showed reciprocal mRNA abundance in wild-type and mutant samples. Our results demonstrate the potential of 3#-UTR profiling for resolving gene-and allele-specific transcripts.

Noncanonical transcript forms in yeast and their regulation during environmental stress

“…In many cases, inferring function for uncharacterized RNAs will require techniques that allow observation of low-abundance transcript forms and canonical mRNAs in the same experiment. Sequencingbased analysis of transcript ends can serve as a powerful method for the study of transcriptomes (Eveland et al 2008;Nagalakshmi et al 2008;Neil et al 2009;Ozsolak et al 2009) but can be hampered by relatively low throughput and/or the necessity for genetic perturbations. In this work, we report a 39-end RNA-seq protocol that provides the sequences and counts of diverse transcript forms with high sensitivity, enabling coverage of intergenic transcripts and alternative and canonical forms of mRNAs in libraries sequenced at standard depth.…”

Statistical Design and Analysis of RNA Sequencing Data

“…At present, there are three widely accepted commercially available NGS devices [Illumina's Genome Analyzer, Applied Biosystems' (Foster City, CA) SOLiD, and the 454 Genome Sequencer FLX] for RNA-Seq (Cloonan et al 2008;Eveland et al 2008;Marioni et al 2008). Across platforms, the RNA-Seq methodology is approximately the same.…”

“…RNA-Seq uses NGS technology to sequence, map, and quantify a population of transcripts (Mortazavi et al 2008;Morozova et al 2009). While RNA-Seq is a relatively new method, it has already provided unprecedented insights into the transcriptional complexities of a variety of organisms, including yeast (Nagalakshmi et al 2008), mice (Mortazavi et al 2008), Arabidopsis (Eveland et al 2008), and humans (Sultan et al 2008).…”

Bioinformatics Applied to Proteomics

“…These technologies allow us the large scale generation of ESTs efficiently and cost-effectively available at the National Centre Biotechnology Information database [NCBIdbEST] (http://www.ncbi.nlm.nih.gov/dbEST); Shendure et al, 2005). There are increasing studies in which 454 technologies, combined or not with Solexa/Illumina, are used to characterize transcriptomes in several plant and animal species (Emrich et al, 2007;Metzker, 2010;Eveland et al, 2008;Bellin et al, 2009). To give an idea of the potential implications of these sequencing technologies it is enough to know that the pyrosequencing delivers the microbial genome sequence in 1 hour, thus upsetting perspectives in basic research, phylogenetic analysis, diagnostics as in industrial applications (Clarke, 2005;Hamady et al, 2010;Yang et al, 2010;Claesson et al, 2009).…”