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1.  RNA-Seq Atlas of Glycine max: A guide to the soybean transcriptome 
BMC Plant Biology  2010;10:160.
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.
doi:10.1186/1471-2229-10-160
PMCID: PMC3017786  PMID: 20687943
2.  Transcript profiling of common bean (Phaseolus vulgaris L.) using the GeneChip® Soybean Genome Array: optimizing analysis by masking biased probes 
BMC Plant Biology  2010;10:85.
Background
Common bean (Phaseolus vulgaris L.) and soybean (Glycine max) both belong to the Phaseoleae tribe and share significant coding sequence homology. This suggests that the GeneChip® Soybean Genome Array (soybean GeneChip) may be used for gene expression studies using common bean.
Results
To evaluate the utility of the soybean GeneChip for transcript profiling of common bean, we hybridized cRNAs purified from nodule, leaf, and root of common bean and soybean in triplicate to the soybean GeneChip. Initial data analysis showed a decreased sensitivity and accuracy of measuring differential gene expression in common bean cross-species hybridization (CSH) GeneChip data compared to that of soybean. We employed a method that masked putative probes targeting inter-species variable (ISV) regions between common bean and soybean. A masking signal intensity threshold was selected that optimized both sensitivity and accuracy of measuring differential gene expression. After masking for ISV regions, the number of differentially-expressed genes identified in common bean was increased by 2.8-fold reflecting increased sensitivity. Quantitative RT-PCR (qRT-PCR) analysis of 20 randomly selected genes and purine-ureide pathway genes demonstrated an increased accuracy of measuring differential gene expression after masking for ISV regions. We also evaluated masked probe frequency per probe set to gain insight into the sequence divergence pattern between common bean and soybean. The sequence divergence pattern analysis suggested that the genes for basic cellular functions and metabolism were highly conserved between soybean and common bean. Additionally, our results show that some classes of genes, particularly those associated with environmental adaptation, are highly divergent.
Conclusions
The soybean GeneChip is a suitable cross-species platform for transcript profiling in common bean when used in combination with the masking protocol described. In addition to transcript profiling, CSH of the GeneChip in combination with masking probes in the ISV regions can be used for comparative ecological and/or evolutionary genomics studies.
doi:10.1186/1471-2229-10-85
PMCID: PMC3017814  PMID: 20459672
3.  Complementary genetic and genomic approaches help characterize the linkage group I seed protein QTL in soybean 
BMC Plant Biology  2010;10:41.
Background
The nutritional and economic value of many crops is effectively a function of seed protein and oil content. Insight into the genetic and molecular control mechanisms involved in the deposition of these constituents in the developing seed is needed to guide crop improvement. A quantitative trait locus (QTL) on Linkage Group I (LG I) of soybean (Glycine max (L.) Merrill) has a striking effect on seed protein content.
Results
A soybean near-isogenic line (NIL) pair contrasting in seed protein and differing in an introgressed genomic segment containing the LG I protein QTL was used as a resource to demarcate the QTL region and to study variation in transcript abundance in developing seed. The LG I QTL region was delineated to less than 8.4 Mbp of genomic sequence on chromosome 20. Using Affymetrix® Soy GeneChip and high-throughput Illumina® whole transcriptome sequencing platforms, 13 genes displaying significant seed transcript accumulation differences between NILs were identified that mapped to the 8.4 Mbp LG I protein QTL region.
Conclusions
This study identifies gene candidates at the LG I protein QTL for potential involvement in the regulation of protein content in the soybean seed. The results demonstrate the power of complementary approaches to characterize contrasting NILs and provide genome-wide transcriptome insight towards understanding seed biology and the soybean genome.
doi:10.1186/1471-2229-10-41
PMCID: PMC2848761  PMID: 20199683

Results 1-3 (3)