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

Andrew Severin(Iowa State University), Jenna Lynn Woody(Iowa State University), Yung‐Tsi Bolon(United States Department of Agriculture), Bindu Joseph(Iowa State University), Brian W. Diers(University of Illinois Urbana-Champaign), Andrew Farmer(National Center for Genome Resources), Gary J. Muehlbauer(University of Minnesota), Rex T. Nelson(Agricultural Research Service), David Grant(Agricultural Research Service), James E. Specht(University of Nebraska–Lincoln), Michelle A. Graham(United States Department of Agriculture), Steven B. Cannon(Iowa State University), Gregory D. May(National Center for Genome Resources), Carroll P. Vance(United States Department of Agriculture), Randy C. Shoemaker(Iowa State University)
BMC Plant Biology
January 1, 2010
Cited by 736Open Access
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Abstract

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.


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