The Gene Ontology (GO) project (http://www. geneontology.org/) provides structured, controlled vocabularies and classifications that cover several domains of molecular and cellular biology and are freely available for community use in the annotation of genes, gene products and sequences. Many model organism databases and genome annotation groups use the GO and contribute their annotation sets to the GO resource. The GO database integrates the vocabularies and contributed annotations and provides full access to this information in several formats. Members of the GO Consortium continually work collectively, involving outside experts as needed, to expand and update the GO vocabularies. The GO Web resource also provides access to extensive documentation about the GO project and links to applications that use GO data for functional analyses.

The laboratory rat (Rattus norvegicus) is an indispensable tool in experimental medicine and drug development, having made inestimable contributions to human health. We report here the genome sequence of the Brown Norway (BN) rat strain. The sequence represents a high-quality 'draft' covering over 90% of the genome. The BN rat sequence is the third complete mammalian genome to be deciphered, and three-way comparisons with the human and mouse genomes resolve details of mammalian evolution. This first comprehensive analysis includes genes and proteins and their relation to human disease, repeated sequences, comparative genome-wide studies of mammalian orthologous chromosomal regions and rearrangement breakpoints, reconstruction of ancestral karyotypes and the events leading to existing species, rates of variation, and lineage-specific and lineage-independent evolutionary events such as expansion of gene families, orthology relations and protein evolution.

The human genome sequence defines our inherent biological potential; the realization of the biology encoded therein requires knowledge of the function of each gene. Currently, our knowledge in this area is still limited. Several lines of investigation have been used to elucidate the structure and function of the genes in the human genome. Even so, gene prediction remains a difficult task, as the varieties of transcripts of a gene may vary to a great extent. We thus performed an exhaustive integrative characterization of 41,118 full-length cDNAs that capture the gene transcripts as complete functional cassettes, providing an unequivocal report of structural and functional diversity at the gene level. Our international collaboration has validated 21,037 human gene candidates by analysis of high-quality full-length cDNA clones through curation using unified criteria. This led to the identification of 5,155 new gene candidates. It also manifested the most reliable way to control the quality of the cDNA clones. We have developed a human gene database, called the H-Invitational Database (H-InvDB; http://www.h-invitational.jp/). It provides the following: integrative annotation of human genes, description of gene structures, details of novel alternative splicing isoforms, non-protein-coding RNAs, functional domains, subcellular localizations, metabolic pathways, predictions of protein three-dimensional structure, mapping of known single nucleotide polymorphisms (SNPs), identification of polymorphic microsatellite repeats within human genes, and comparative results with mouse full-length cDNAs. The H-InvDB analysis has shown that up to 4% of the human genome sequence (National Center for Biotechnology Information build 34 assembly) may contain misassembled or missing regions. We found that 6.5% of the human gene candidates (1,377 loci) did not have a good protein-coding open reading frame, of which 296 loci are strong candidates for non-protein-coding RNA genes. In addition, among 72,027 uniquely mapped SNPs and insertions/deletions localized within human genes, 13,215 nonsynonymous SNPs, 315 nonsense SNPs, and 452 indels occurred in coding regions. Together with 25 polymorphic microsatellite repeats present in coding regions, they may alter protein structure, causing phenotypic effects or resulting in disease. The H-InvDB platform represents a substantial contribution to resources needed for the exploration of human biology and pathology.

The purpose of this study was to quantitatively estimate the relative contribution of arteriolar rarefaction (disappearance of microvessels) and arteriolar constriction to the increases in total peripheral resistance and changes in the patterns of flow distribution observed in hypertension. A mathematical model of the hamster cheek pouch intraluminal microcirculation was constructed based on data from the literature and observations from our own laboratory. Separate rarefaction and constriction of third-order (3A) and fourth-order (4A) arterioles were performed on the model, and the results were quantified based on the changes of the computed vascular resistance. The degree of increase in resistance depended both on the number and the order of vessels rarefied or constricted and also on the position of those vessels in the network. The maximum increases in resistance obtained in the model runs were 21% for rarefaction and 75% for constriction. Rarefaction, but not constriction, produced large increases in the degree of heterogeneity of blood flow in the various vessel orders. These results demonstrate that vessel rarefaction significantly influences tissue blood flow resistance to a degree comparable with vessel constriction; however, unlike constriction, microvascular rarefaction markedly altered blood flow distribution in our model of the hamster cheek pouch vascular bed. These findings conform with the hypothesis that a significant component of the increase in total peripheral resistance in hypertension may be due to vessel rarefaction.

With the draft sequence of the human genome available, there is a need to better define gene function in the context of systems biology. We studied 239 cardiovascular and renal phenotypes in 113 male rats derived from an F2 intercross and mapped 81 of these traits onto the genome. Aggregates of traits were identified on chromosomes 1, 2, 7, and 18. Systems biology was assessed by examining patterns of correlations ("physiological profiles") that can be used for gene hunting, mechanism-based physiological studies, and, with comparative genomics, translating these data to the human genome.