We have sequenced and annotated the genome of ®ssion yeast (Schizosaccharomyces pombe), which contains the smallest number of protein-coding genes yet recorded for a eukaryote: 4,824. The centromeres are between 35 and 110 kilobases (kb) and contain related repeats including a highly conserved 1.8-kb element. Regions upstream of genes are longer than in budding yeast (Saccharomyces cerevisiae), possibly re¯ecting more-extended control regions. Some 43% of the genes contain introns, of which there are 4,730. Fifty genes have signi®cant similarity with human disease genes; half of these are cancer related. We identify highly conserved genes important for eukaryotic cell organization including those required for the cytoskeleton, compartmentation, cell-cycle control, proteolysis, protein phosphorylation and RNA splicing. These genes may have originated with the appearance of eukaryotic life. Few similarly conserved genes that are important for multicellular organization were identi®ed, suggesting that the transition from prokaryotes to eukaryotes required more new genes than did the transition from unicellular to multicellular organization.We report here the completion of the fully annotated genome sequence of the simple eukaryote Schizosaccharomyces pombe, a ®ssion yeast. It becomes the sixth eukaryotic genome to be sequenced, following Saccharomyces cerevisiae 1 , Caenorhabditis elegans 2 , Drosophila melanogaster 3 , Arabidopsis thaliana 4 and Homo sapiens 5,6 . The entire sequence of the unique regions of the three chromosomes is complete, with gaps in the centromeric regions of about 40 kb, and about 260 kb in the telomeric regions. The completion of this sequence, the availability of sophisticated research methodologies, and the expanding community working on S. pombe, will accelerate the use of S. pombe for functional and comparative studies of eukaryotic cell processes.

Establishing transcriptional regulatory networks by analysis of gene expression data and promoter sequences shows great promise. We developed a novel promoter classification method using a Relevance Vector Machine (RVM) and Bayesian statistical principles to identify discriminatory features in the promoter sequences of genes that can correctly classify transcriptional responses. The method was applied to microarray data obtained from Arabidopsis seedlings treated with glucose or abscisic acid (ABA). Of those genes showing >2.5-fold changes in expression level, ~70% were correctly predicted as being up- or down-regulated (under 10-fold cross-validation), based on the presence or absence of a small set of discriminative promoter motifs. Many of these motifs have known regulatory functions in sugar- and ABA-mediated gene expression. One promoter motif that was not known to be involved in glucose-responsive gene expression was identified as the strongest classifier of glucose-up-regulated gene expression. We show it confers glucose-responsive gene expression in conjunction with another promoter motif, thus validating the classification method. We were able to establish a detailed model of glucose and ABA transcriptional regulatory networks and their interactions, which will help us to understand the mechanisms linking metabolism with growth in Arabidopsis. This study shows that machine learning strategies coupled to Bayesian statistical methods hold significant promise for identifying functionally significant promoter sequences

Adaptation of cells to environmental changes requires dynamic interactions between metabolic and regulatory networks, but studies typically address only one or a few layers of regulation. For nutritional shifts between two preferred carbon sources of Bacillus subtilis, we combined statistical and model-based data analyses of dynamic transcript, protein, and metabolite abundances and promoter activities. Adaptation to malate was rapid and primarily controlled posttranscriptionally compared with the slow, mainly transcriptionally controlled adaptation to glucose that entailed nearly half of the known transcription regulation network. Interactions across multiple levels of regulation were involved in adaptive changes that could also be achieved by controlling single genes. Our analysis suggests that global trade-offs and evolutionary constraints provide incentives to favor complex control programs.

Deep profiling of the transcriptome and proteome during leaf development reveals unexpected responses to water deficit, as well as a surprising lack of protein-level fluctuations during the day–night cycle, despite clear changes at the transcript level.

The study of the metabolite complement of biological samples, known as metabolomics, is creating large amounts of data, and support for handling these data sets is required to facilitate meaningful analyses that will answer biological questions. We present a data model for plant metabolomics known as ArMet (architecture for metabolomics). It encompasses the entire experimental time line from experiment definition and description of biological source material, through sample growth and preparation to the results of chemical analysis. Such formal data descriptions, which specify the full experimental context, enable principled comparison of data sets, allow proper interpretation of experimental results, permit the repetition of experiments and provide a basis for the design of systems for data storage and transmission. The current design and example implementations are freely available (http://www.armet.org/). We seek to advance discussion and community adoption of a standard for metabolomics, which would promote principled collection, storage and transmission of experiment data.