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Background: Alfalfa (Medicago sativa L.) is the primary forage legume crop species in the United States and plays essential economic and ecological roles in agricultural systems across the country. Modern alfalfa is the result of hybridization between tetraploid M. sativa ssp. sativa and M. sativa ssp. falcata. Due to its large and complex genome, there are few genomic resources available for alfalfa improvement., Results: A de novo transcriptome assembly from two alfalfa subspecies, M. sativa ssp. sativa (B47) and M. sativa ssp. falcata (F56) was developed using Illumina RNA-seq technology. Transcripts from roots, nitrogen-fixing root nodules, leaves, flowers, elongating stem internodes, and post-elongation stem internodes were assembled into the Medicago sativa Gene Index 1.2 (MSGI 1.2) representing 112,626 unique transcript sequences. Nodule-specific and transcripts involved in cell wall biosynthesis were identified. Statistical analyses identified 20,447 transcripts differentially expressed between the two subspecies. Pair-wise comparisons of each tissue combination identified 58,932 sequences differentially expressed in B47 and 69,143 sequences differentially expressed in F56. Comparing transcript abundance in floral tissues of B47 and F56 identified expression differences in sequences involved in anthocyanin and carotenoid synthesis, which determine flower pigmentation. Single nucleotide polymorphisms (SNPs) unique to each M. sativa subspecies (110,241) were identified., Conclusions: The Medicago sativa Gene Index 1.2 increases the expressed sequence data available for alfalfa by ninefold and can be expanded as additional experiments are performed. The MSGI 1.2 transcriptome sequences, annotations, expression profiles, and SNPs were assembled into the Alfalfa Gene Index and Expression Database (AGED) at http://plantgrn.noble.org/AGED/ , a publicly available genomic resource for alfalfa improvement and legume research.

Background: Common bean (Phaseolus vulgaris) is grown throughout the world and comprises roughly 50% of the grain legumes consumed worldwide. Despite this, genetic resources for common beans have been lacking. Next generation sequencing, has facilitated our investigation of the gene expression profiles associated with biologically important traits in common bean. An increased understanding of gene expression in common bean will improve our understanding of gene expression patterns in other legume species., Results: Combining recently developed genomic resources for Phaseolus vulgaris, including predicted gene calls, with RNA-Seq technology, we measured the gene expression patterns from 24 samples collected from seven tissues at developmentally important stages and from three nitrogen treatments. Gene expression patterns throughout the plant were analyzed to better understand changes due to nodulation, seed development, and nitrogen utilization. We have identified 11,010 genes differentially expressed with a fold change ≥ 2 and a P-value < 0.05 between different tissues at the same time point, 15,752 genes differentially expressed within a tissue due to changes in development, and 2,315 genes expressed only in a single tissue. These analyses identified 2,970 genes with expression patterns that appear to be directly dependent on the source of available nitrogen. Finally, we have assembled this data in a publicly available database, The Phaseolus vulgaris Gene Expression Atlas (Pv GEA), http://plantgrn.noble.org/PvGEA/ . Using the website, researchers can query gene expression profiles of their gene of interest, search for genes expressed in different tissues, or download the dataset in a tabular form., Conclusions: These data provide the basis for a gene expression atlas, which will facilitate functional genomic studies in common bean. Analysis of this dataset has identified genes important in regulating seed composition and has increased our understanding of nodulation and impact of the nitrogen source on assimilation and distribution throughout the plant.

Background: Single nucleotide polymorphisms (SNPs) are the most common type of sequence variation among plants and are often functionally important. We describe the use of 454 technology and high resolution melting analysis (HRM) for high throughput SNP discovery in tetraploid alfalfa (Medicago sativa L.), a species with high economic value but limited genomic resources., Results: The alfalfa genotypes selected from M. sativa subsp. sativa var. 'Chilean' and M. sativa subsp. falcata var. 'Wisfal', which differ in water stress sensitivity, were used to prepare cDNA from tissue of clonally-propagated plants grown under either well-watered or water-stressed conditions, and then pooled for 454 sequencing. Based on 125.2 Mb of raw sequence, a total of 54,216 unique sequences were obtained including 24,144 tentative consensus (TCs) sequences and 30,072 singletons, ranging from 100 bp to 6,662 bp in length, with an average length of 541 bp. We identified 40,661 candidate SNPs distributed throughout the genome. A sample of candidate SNPs were evaluated and validated using high resolution melting (HRM) analysis. A total of 3,491 TCs harboring 20,270 candidate SNPs were located on the M. truncatula (MT 3.5.1) chromosomes. Gene Ontology assignments indicate that sequences obtained cover a broad range of GO categories., Conclusions: We describe an efficient method to identify thousands of SNPs distributed throughout the alfalfa genome covering a broad range of GO categories. Validated SNPs represent valuable molecular marker resources that can be used to enhance marker density in linkage maps, identify potential factors involved in heterosis and genetic variation, and as tools for association mapping and genomic selection in alfalfa.