Gene expression patterns are correlated with genomic and genic structure in soybean.

Studies have indicated that exon and intron size and intergenic distance are correlated with gene expression levels and expression breadth. Previous reports on these correlations in plants and animals have been conflicting. In this study, next-generation sequence data, which has been shown to be more sensitive than previous expression profiling technologies, were generated and analyzed from 14 tissues. Our results revealed a novel dichotomy. At the low expression level, an increase in expression breadth correlated with an increase in transcript size because of an increase in the number of exons and introns. No significant changes in intron or exon sizes were noted. Conversely, genes expressed at the intermediate to high expression levels displayed a decrease in transcript size as their expression breadth increased. This was due to smaller exons, with no significant change in the number of exons. Taking advantage of the known gene space of soybean, we evaluated the positioning of genes and found significant clustering of similarly expressed genes. Identifying the correlations between the physical parameters of individual genes could lead to uncovering the role of regulation owing to nucleotide composition, which might have potential impacts in discerning the role of the noncoding regions.

RNA-Seq Atlas of Glycine max: a guide to the soybean transcriptome.

BACKGROUND: Next generation sequencing is transforming our understanding of transcriptomes. It can determine the expression level of transcripts with a dynamic range of over six orders of magnitude from multiple tissues, developmental stages or conditions. Patterns of gene expression provide insight into functions of genes with unknown annotation. RESULTS: The RNA Seq-Atlas presented here provides a record of high-resolution gene expression in a set of fourteen diverse tissues. Hierarchical clustering of transcriptional profiles for these tissues suggests three clades with similar profiles: aerial, underground and seed tissues. We also investigate the relationship between gene structure and gene expression and find a correlation between gene length and expression. Additionally, we find dramatic tissue-specific gene expression of both the most highly-expressed genes and the genes specific to legumes in seed development and nodule tissues. Analysis of the gene expression profiles of over 2,000 genes with preferential gene expression in seed suggests there are more than 177 genes with functional roles that are involved in the economically important seed filling process. Finally, the Seq-atlas also provides a means of evaluating existing gene model annotations for the Glycine max genome. CONCLUSIONS: This RNA-Seq atlas extends the analyses of previous gene expression atlases performed using Affymetrix GeneChip technology and provides an example of new methods to accommodate the increase in transcriptome data obtained from next generation sequencing. Data contained within this RNA-Seq atlas of Glycine max can be explored at http://www.soybase.org/soyseq.