Genomic landscape of the soybean (Glycine max) genome

The genomic landscape of plants, while slowly being charted, is still composed primarily of unknown territory. The landscape can be related to chromatin domains, transposable element neighborhoods, gene organization, epigenetic modifications of the genome and more. Certain patterns of expression, tissue specific versus constitutive, or high expression versus low expression, are often associated with physical attributes of the gene and genome. We have known for a while that expression is not controlled solely by the promoter but is modulated by transcription factors, small RNAs, parachromatin, as well as by all of the components that make up epigenetics (Jorgensen, 2011). Characterizing and identifying the internal cues that regulate transcription and translation within the genome can help us decipher the form, function and evolution of living organisms. Recently, with advances in technology, a correlation between the transcriptional profile of the gene and the physical size of the gene has been observed. The focus of my research project has been to better understand the internal genomic regulations not contributed to known elements (promoters, small RNAs, transcription factors). Coupling next-generation transcription data with the recently published soybean genome has allowed us to get a fuller understanding of the relationship between the structural parameters of the gene, transcriptional demands and genomic neighborhoods

Gene Expression: Sizing It All Up

Genomic architecture appears to be a largely unexplored component of gene expression. That architecture can be related to chromatin domains, transposable element neighborhoods, epigenetic modifications of the genome, and more. Although surely not the end of the story, we are learning that when it comes to gene expression, size is also important. We have been surprised to find that certain patterns of expression, tissue specific versus constitutive, or high expression versus low expression, are often associated with physical attributes of the gene and genome. Multiple studies have shown an inverse relationship between gene expression patterns and various physical parameters of the genome such as intron size, exon size, intron number, and size of intergenic regions. An increase in expression level and breadth often correlates with a decrease in the size of physical attributes of the gene. Three models have been proposed to explain these relationships. Contradictory results were found in several organisms when expression level and expression breadth were analyzed independently. However, when both factors were combined in a single study a novel relationship was revealed. At low levels of expression, an increase in expression breadth correlated with an increase in genic, intergenic, and intragenic sizes. Contrastingly, at high levels of expression, an increase in expression breadth inversely correlated with the size of the gene. In this article we explore the several hypotheses regarding genome physical parameters and gene expression

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

<p>Abstract</p> <p>Background</p> <p>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.</p> <p>Results</p> <p>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 <it>Glycine max </it>genome.</p> <p>Conclusions</p> <p>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 <it>Glycine max </it>can be explored at <url>http://www.soybase.org/soyseq</url>.</p

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

Genomic landscape of the soybean (Glycine max) genome

The genomic landscape of plants, while slowly being charted, is still composed primarily of unknown territory. The landscape can be related to chromatin domains, transposable element neighborhoods, gene organization, epigenetic modifications of the genome and more. Certain patterns of expression, tissue specific versus constitutive, or high expression versus low expression, are often associated with physical attributes of the gene and genome. We have known for a while that expression is not controlled solely by the promoter but is modulated by transcription factors, small RNAs, parachromatin, as well as by all of the components that make up epigenetics (Jorgensen, 2011). Characterizing and identifying the internal cues that regulate transcription and translation within the genome can help us decipher the form, function and evolution of living organisms. Recently, with advances in technology, a correlation between the transcriptional profile of the gene and the physical size of the gene has been observed. The focus of my research project has been to better understand the internal genomic regulations not contributed to known elements (promoters, small RNAs, transcription factors). Coupling next-generation transcription data with the recently published soybean genome has allowed us to get a fuller understanding of the relationship between the structural parameters of the gene, transcriptional demands and genomic neighborhoods.</p

RNA-Seq Atlas of \u3ci\u3eGlycine max\u3c/i\u3e: 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

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.This article is from BMC Plant Biology 10 (2010): 160, doi:10.1186/1471-2229-10-160. Posted with permission.</p

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.This article is from Genome, 2011, 54(1): 10-18, 10.1139/G10-090.</p