Over the last 100 years, the atmospheric concentration of carbon dioxide has dramatically increased, in major part due to the burning of fossil fuels, recent rapid industrialization, and land use changes. The predicted effects of continued climate change are complex but include effects on air and surface temperature, with coincident effects on water availability.  Soil temperature can influence root growth, cell elongation, root length and extension, initiation of new lateral roots and root hairs, and root branching. These effects are likely manifestations of the variety of physiological effects brought about by temperature on plant roots; including changes in root respiration, nutrient uptake, as well as physicochemical effects on the soil environment (e.g., changes in nitrogen mineralization). Ambient temperature changes also affects other parts of the plant (e.g., photosynthetic rates), which also affects below ground growth and physiology. When we include in this discussion issues of plant genetic variation, as well as the effects of temperature on water availability, the full complexity of the effects of climate change on the plant root environment becomes clear.

A hallmark of modern biology is large-scale -omics data. These data are massive and often very complex in nature; thereby generating the need for extensive storage, detailed computational analyses, fast retrieval and efficient integration, for better understanding of the data and hypothesis generation for the underlying biological system. Our efforts to study the systems biology of the soybean root hair cell have generated very large datasets. In addition, other laboratories are now applying these methods to soybean to address a variety of biological questions. To address the need for web resources capable of handling the complex task of integrating soybean -omics data and to provide data annotation (e.g. the pathway information), we developed the Soybean Knowledge Base. .

The Soybean Knowledge Base (SoyKB) is a comprehensive all-inclusive web resource for soybean. SoyKB is designed to handle the storage and integration of the genomics, microarray, transcriptomics, proteomics and metabolomics data along with the function and pathway information. It has four modules including the main mySQL database module at the back end that incorporates and integrates all the soybean genomics and -omics data from various sources. It is designed to contain information on four different entities namely genes, miRNAs, metabolites and SNPs. The other three front-end modules are web interface, genome browser and pathway integration.

SoyKB has four tiers of registration, which control the access to the public and private experimental datasets. Users can add comments, download data for multiple genes as well as submit their own datasets. Tools like protein 3D-structure and pathway viewers, gene family browsers and BLAST sequence similarity tool are all part of key features of SoyKB.

SoyKB can be accessed at http://soykb.org/.

Proteomics is the study of the structure, function and interaction of proteins. Proteins play a critical role in the life of any organism by being involved in many processes within the cell. The proteome, which is the complete collection of proteins in an organism, is much more complicated to study than the genome or transcriptome. An organism harboring 20 000 genes can have more than 1 million different proteins due to alternative splicing and post-translational modifications. Furthermore, the level of transcription of a gene often does not reflect the level of the encoded protein. An mRNA can be present in numerous copies in a cell but the mRNA stability (half life) and efficiency of translation will affect the level of the corresponding protein. A protein can also be present in a cell but inactive until subjected to post-translational modifications like phosphorylation. Finally, many proteins can interact with other proteins and only the resulting complex may have biological function. The measurement of mRNA levels via transcriptomics (e.g., via the use DNA microarrays, see discussion of this subject on our website) is complimentary to proteomics.