4 to their proteins of origin. The growing sequence resource in M. truncatula allows identiﬁcation of proteins by their mass spectra, making proteomics an eﬀective approach for M. truncatula and proteomics approaches have become quite popular. A comprehensive review of considerations impor- tant in proteomics technology and applications in M. trun- catula and Arabidopsis was recently published [87, 88]. Be- cause small peptides have been shown to have roles in plant signaling, proteomics has been applied to identifying small protein/peptide components of certain M. truncatula tissues [89]. Proteomic approaches have also been applied to analy- ses of seed development [14, 16], pathogen interactions [90], symbiosome membranes [91], AM membranes [92], root microsomes [93], and other organ, tissue, and treatment- speciﬁc approaches [11, 94–100]. Most of the genes cloned thus far in the initial signal transduction pathway for nodulation are kinases [101]; sug- gesting global analysis of phosphoproteins is a way to identify important genes involved in signal transduction in M. trun- catula. Unfortunately, phosphoproteins involved in cellular signaling are generally present in low abundance, creating new challenges for proteomics. By making adjustments the basic proteomics procedures, such as adding an enrichment step, a proof of concept experiment in M. truncatula phos- phoproteomics, gives a taste of the potential of this approach [102]. 4.3. Metabolomics Alfalfa produces a number of secondary metabolites of great interest because of their contributions to human health and animal forage quality. The principle behind metabolomics is that metabolic proﬁling on a genomic scale oﬀers a view of the metabolic status of an organism, which can lend in- sight to the study gene function or whole plant biology [103]. Successful attempts to link proteomics, transcriptomics, and metabolomics for cell cultures in M. truncatula have emerged from these studies [104, 105]. Metabolomics is a new and evolving science, and requires specialized equipment and multifaceted technical strategies. The Nobel Foundation employs a strategy that utilizes se- quential or selective extraction followed by parallel analyses. The parallel analyses achieve a comprehensive view of the metabolome with high-performance liquid chromatography (HPLC), capillary electrophoresis (CE), gas chromatography (GC), mass spectrometry (MS), and various combinations of the above techniques such as GC/MS, LC/MS, and CE/MS. In addition to studying biological responses to biotic and abi- otic elicitors in M. truncatula cellcultures, these techniques are being applied to the study of natural variants in M. trun- catula, M. truncatula development, lignin biosynthesis, and legume-insect interactions. Perhaps the most daunting aspect of metabolomic exper- iments is the analysis of the data. Early on, it became obvious that metabolomics required a standard similar to MIAME (minimum information about a microarray experiment) to allow comparison of data. A framework for the description of plant metabolomic experiments and their results has recently International Journal of Plant Genomics been developed. ArMet (architecture for metabolomics) is published and in accepted use [106, 107]. 4.4. Phenomics As more and more researchers use M. truncatula as a model, the need for a standardized method of describing pheno- types becomes acute. Since the timing and structure of veg- etative and ﬂoral development in M. truncatula diﬀer from Arabidopsis, adoption of standards such as those used for Arabidopsis [108] is inappropriate. Additionally, M. truncat- ula symbioses with AM fungi and Sinorhizobium meliloti add another dimension to developmental processes that re- quire a standardized description of process stages and plant anatomy. To date, a few attempts have been made to develop a stan- dardized language for comparison. Vegetative growth param- eters were carefully measured to provide a benchmark in [109], but the use of a glasshouse environment rather than a controlled light and temperature regime rendered the data not universally applicable. Likewise, ﬂower development and response to vernalization have been documented in the same way [109], again in a glasshouse so the light intensity was uncontrolled. These experiments are progress toward a con- trolled standard for comparison of mutant phenotypes such as “late ﬂowering” or “increased internodal distance.” Pre- cision in phenotypic descriptions will be critical to genome scale mutant hunts. There is no plant structural GO ontology terms for nodulation or nodule structures in the Plant Ontology Consortium site as of the February 2007 release (http:// www.plantontology.org). The present plant ontology system provides terms for growth and developmental stages, as well as organs and tissues of Arabidopsis, maize and rice, but none of these plants nodulates, creating a problem for using GO annotation in M. truncatula. 4.5. Bioinformatics All of the “omics” scale tools discussed above necessi- tate strong bioinformatics infrastructure for the species. A good place to begin is the Medicago Consortium website: http://www.medicago.org. In addition to a handbook of pro- tocols for everything from growing and transforming M. truncatula to naming genes, links from this page lead to in- formatics tools such as ENSEMBL which allow a real time view of the annotation of the genome, tools allowing brows- ing of the genome for markers, genes, the location of BACs, the status of the sequencing project or the sequence status of any individual BAC. Users can also view the contigs assem- bled for sequencing, and make comparisons to other legumes through the legume information system [110] and the con- sensus legume database (www.legumes.org). Tools are also available through links from the medicago.org website for examining ESTs (TIGR, MtDB2, MENS), and in the future, examining microarray data. In silico approaches in M. trun- catula have led to important insights, such as the identiﬁ- cation of a large family of small legume-speciﬁc transcripts