Understanding of phenotypes and their genetic basis is a major focus in current plant biology. Large amounts of phenotype data are being generated, both for macroscopic phenotypes such as size or yield, and for molecular phenotypes such as expression levels and metabolite levels. More insight in the underlying genetic and molecular mechanisms that influence phenotypes will enable a better understanding of how various phenotypes are related to each other. This will be a major step forward in understanding plant biology, with immediate value for plant breeding and academic plant research. Currently the genetic basis of most phenotypes remains however to be discovered, and the relatedness of different traits is unclear. We here present a novel approach to connect phenotypes to underlying biological processes and molecular functions. These connections define similarities between different types of phenotypes. The approach starts by using Quantitative Trait Locus (QTL) data, which are abundantly available for many phenotypes of interest. Overrepresentation analysis of gene functions based on Gene Ontology term enrichment across multiple QTL regions for a given phenotype, be it macroscopic or molecular, results in a small set of biological processes and molecular functions for each phenotype. Subsequently, similarity between different phenotypes can be defined in terms of these gene functions. Using publicly available rice data as example, a close relationship with defined molecular phenotypes is demonstrated for many macroscopic phenotypes. This includes for example a link between 'leaf senescence' and 'aspartic acid', as well as between 'days to maturity' and 'choline'. Relationships between macroscopic and molecular phenotypes may result in more efficient marker-assisted breeding and are likely to direct future research aimed at a better understanding of plant phenotypes.

The Affymetrix GeneChip technology uses multiple probes per gene to measure its expression level. Individual probe signals can vary widely, which hampers proper interpretation. This variation can be caused by probes that do not properly match their target gene or that match multiple genes. To determine the accuracy of Affymetrix arrays, we developed an extensive verification protocol, for mouse arrays incorporating the NCBI RefSeq, NCBI UniGene Unique, NIA Mouse Gene Index, and UCSC mouse genome databases.

As part of large protein complexes, Snf2 family ATPases are responsible for energy supply during chromatin remodeling, but the precise mechanism of action of many of these proteins is largely unknown. They influence many processes in plants, such as the response to environmental stress. This analysis is the first comprehensive study of Snf2 family ATPases in plants. We here present a comparative analysis of 1159 candidate plant Snf2 genes in 33 complete and annotated plant genomes, including two green algae. The number of Snf2 ATPases shows considerable variation across plant genomes (17-63 genes). The DRD1, Rad5/16 and Snf2 subfamily members occur most often. Detailed analysis of the plant-specific DRD1 subfamily in related plant genomes shows the occurrence of a complex series of evolutionary events. Notably tomato carries unexpected gene expansions of DRD1 gene members. Most of these genes are expressed in tomato, although at low levels and with distinct tissue or organ specificity. In contrast, the Snf2 subfamily genes tend to be expressed constitutively in tomato. The results underpin and extend the Snf2 subfamily classification, which could help to determine the various functional roles of Snf2 ATPases and to target environmental stress tolerance and yield in future breeding.

Plants are sessile organisms that deal with their -sometimes adverse- environment in well-regulated ways. Chromatin remodeling involving SWI/SNF2-type ATPases is thought to be an important epigenetic mechanism for the regulation of gene expression in different developmental programs and for integrating these programs with the response to environmental signals. In this study, we report on the role of chromatin remodeling in Arabidopsis with respect to the variability of growth and gene expression in relationship to environmental conditions.

Solanum dulcamara (bittersweet, climbing nightshade) is one of the few species of the Solanaceae family native to Europe. As a common weed it is adapted to a wide range of ecological niches and it has long been recognized as one of the alternative hosts for pathogens and pests responsible for many important diseases in potato, such as Phytophthora. At the same time, it may represent an alternative source of resistance genes against these diseases. Despite its unique ecology and potential as a genetic resource, genomic research tools are lacking for S. dulcamara. We have taken advantage of next-generation sequencing to speed up research on and use of this non-model species.

MicroRNAs (miRNAs), short approximately 21-nucleotide RNA molecules, play an important role in post-transcriptional regulation of gene expression. The number of known miRNA hairpins registered in the miRBase database is rapidly increasing, but recent reports suggest that many miRNAs with restricted temporal or tissue-specific expression remain undiscovered. Various strategies for in silico miRNA identification have been proposed to facilitate miRNA discovery. Notably support vector machine (SVM) methods have recently gained popularity. However, a drawback of these methods is that they do not provide insight into the biological properties of miRNA sequences.

Small RNAs are important regulators of genome function, yet their prediction in genomes is still a major computational challenge. Statistical analyses of pre-miRNA sequences indicated that their 2D structure tends to have a minimal free energy (MFE) significantly lower than MFE values of equivalently randomized sequences with the same nucleotide composition, in contrast to other classes of non-coding RNA. The computation of many MFEs is, however, too intensive to allow for genome-wide screenings.