Formal description of plant phenotypes and standardized annotation of gene expression and protein localization data require uniform terminology that accurately describes plant anatomy and morphology. This facilitates cross species comparative studies and quantitative comparison of phenotypes and expression patterns. A major drawback is variable terminology that is used to describe plant anatomy and morphology in publications and genomic databases for different species. The same terms are sometimes applied to different plant structures in different taxonomic groups. Conversely, similar structures are named by their species-specific terms. To address this problem, we created the Plant Structure Ontology (PSO), the first generic ontological representation of anatomy and morphology of a flowering plant. The PSO is intended for a broad plant research community, including bench scientists, curators in genomic databases, and bioinformaticians. The initial releases of the PSO integrated existing ontologies for Arabidopsis (Arabidopsis thaliana), maize (Zea mays), and rice (Oryza sativa); more recent versions of the ontology encompass terms relevant to Fabaceae, Solanaceae, additional cereal crops, and poplar (Populus spp.). Databases such as The Arabidopsis Information Resource, Nottingham Arabidopsis Stock Centre, Gramene, MaizeGDB, and SOL Genomics Network are using the PSO to describe expression patterns of genes and phenotypes of mutants and natural variants and are regularly contributing new annotations to the Plant Ontology database. The PSO is also used in specialized public databases, such as BRENDA, GENEVESTIGATOR, NASCArrays, and others. Over 10,000 gene annotations and phenotype descriptions from participating databases can be queried and retrieved using the Plant Ontology browser. The PSO, as well as contributed gene associations, can be obtained at www.plantontology.org.

Plant metabolomics has matured over the past 8 years. Plant biologists routinely use comprehensive analyses of plant metabolites to discover new responses to genetic or environmental perturbation, or to validate initial hypotheses on the function and in vivo action of gene products. The wealth of scientific findings has increasingly provoked interest to share and review raw or processed data from plant metabolomics reports. We here suggest a minimum of parameters to be reported in order to define details of experimental study designs in plant metabolomics studies.

The Gene Ontology (GO) project (http://www.geneontology.org) provides a set of structured, controlled vocabularies for community use in annotating genes, gene products and sequences (also see http://www.sequenceontology.org/). The ontologies have been extended and refined for several biological areas, and improvements to the structure of the ontologies have been implemented. To improve the quantity and quality of gene product annotations available from its public repository, the GO Consortium has launched a focused effort to provide comprehensive and detailed annotation of orthologous genes across a number of reference genomes, including human and several key model organisms. Software developments include two releases of the ontology-editing tool OBO-Edit, and improvements to the AmiGO browser interface.

The Plant Ontology Consortium (POC, http://www.plantontology.org) is a collaborative effort among model plant genome databases and plant researchers that aims to create, maintain and facilitate the use of a controlled vocabulary (ontology) for plants. The ontology allows users to ascribe attributes of plant structure (anatomy and morphology) and developmental stages to data types, such as genes and phenotypes, to provide a semantic framework to make meaningful cross-species and database comparisons. The POC builds upon groundbreaking work by the Gene Ontology Consortium (GOC) by adopting and extending the GOCs principles, existing software and database structure. Over the past year, POC has added hundreds of ontology terms to associate with thousands of genes and gene products from Arabidopsis, rice and maize, which are available through a newly updated web-based browser (http://www.plantontology.org/amigo/go.cgi) for viewing, searching and querying. The Consortium has also implemented new functionalities to facilitate the application of PO in genomic research and updated the website to keep the contents current. In this report, we present a brief description of resources available from the website, changes to the interfaces, data updates, community activities and future enhancement.

Some plants like Arabidopsis thaliana increase in freezing tolerance when exposed to low nonfreezing temperatures, a process known as cold acclimation. Other plants including tomato, Solanum lycopersicum, are chilling sensitive and incur injury during prolonged low temperature exposure. A key initial event that occurs upon low temperature exposure is the induction of genes encoding the CBF transcription factors. In Arabidopsis three CBF genes, present in a tandemly-linked cluster, are induced by low temperatures. Tomato also harbors three tandemly-linked CBF genes, Sl-CBF3-CBF1-CBF2, but only one of these, Sl-CBF1, is low-temperature responsive. Here we report that Solanum species that are closely-allied to cultivated tomato essentially share this structural organization, but the locus is in a dynamic state of flux. Additional paralogs and in-frame deletions between adjacent genes occur, and the genomic regions flanking the CBF genes are dissimilar across Solanum species. Nevertheless, the CBF1 upstream region remains intact and highly conserved. This feature differed for CBF2 and CBF3, whose upstream regions were far less conserved. CBF1 was also the only low-temperature responsive gene in the cluster and its expression was greatly affected by a circadian clock. The tuber-bearing S. tuberosum and S. commersonii also harbored a fourth gene, CBF4, which was also low temperature responsive. CBF4 was physically linked to CBF5 in S. tuberosum, but CBF5 was absent from S. commersonii. Phylogenic analyses suggest that CBF5-CBF4 resulted from the duplication of the CBF3-CBF1-CBF2 cluster. DNA sequence motifs shared between the Solanum CBF1 and CBF4 upstream regions were identified, portions of which were also present in the Arabidopsis CBF1-3 upstream regions. These results suggest that much greater functional constraints are placed upon the Solanum CBF1 upstream regions over the other CBF upstream regions and that CBF4 has retained the capacity for low temperature responsiveness following the duplication event that gave rise to CBF4.

The Gene Ontology (GO) project is a collaboration among model organism databases to describe gene products from all organisms using a consistent and computable language. GO produces sets of explicitly defined, structured vocabularies that describe biological processes, molecular functions and cellular components of gene products in both a computer-and human-readable manner. Here we describe key aspects of GO, which, when overlooked, can cause erroneous results, and address how these pitfalls can be avoided.

Background: Microarray technology is a widely used approach for monitoring genome-wide gene expression. For Arabidopsis, there are over 1,800 microarray hybridizations representing many different experimental conditions on Affymetrix (TM) ATH1 gene chips alone. This huge amount of data offers a unique opportunity to infer the principles that govern the regulation of gene expression in plants.

In an era of increasingly complex biological datasets, one of the key steps in gene functional analysis comes from clustering genes based on co-expression. Biclustering algorithms can identify gene clusters with local co-expressed patterns, which are more likely to define genes functioning together than global clustering methods. However, these algorithms are not effective in uncovering gene regulatory networks because the mined biclusters lack genes that may be critical in the function but may not be co-expressed with the clustered genes. In this paper, we introduce a biclustering method called SKeleton Biclustering (SKB), which builds high quality biclusters from microarray data, creates relationships among the biclustered genes based on Gene Ontology annotations, and identifies genes that are missing in the biclusters. SKB thus defines inter-bicluster and intra-bicluster functional relationships. The delineation of functional relationships and incorporation of such missing genes may help biologists to discover biological processes that are important in a given study and provides clues for how the processes may be functioning together. Experimental results show that, with SKB, the biological significance of the biclusters is considerably improved.