SummaryMany mutants have been isolated from the model plant Arabidopsis thaliana, and recent important genetic resources, such as T-DNA knockout lines, facilitate the speed of identifying new mutants. However, present phenotypic analysis of mutant screens depends mainly on qualitative descriptions after visual observation of morphological traits. We propose a novel method of phenotypic analysis based on precise three-dimensional (3D) measurement by a laser range ®nder (LRF) and automatic data processing. We measured the 3D surfaces of young plants of two Arabidopsis ecotypes and successfully de®ned two new traits, the direction of the blade surface and epinasty of the blade, quantitatively. The proposed method enables us to obtain quantitative and precise descriptions of plant morphologies compared to conventional 2D measurement.The method will open a way to ®nd new traits from mutant pools or natural ecotypes based on 3D data.

PosMed (http://omicspace.riken.jp/) prioritizes candidate genes for positional cloning by employing our original database search engine GRASE, which uses an inferential process similar to an artificial neural network comprising documental neurons (or ‘documentrons’) that represent each document contained in databases such as MEDLINE and OMIM. Given a user-specified query, PosMed initially performs a full-text search of each documentron in the first-layer artificial neurons and then calculates the statistical significance of the connections between the hit documentrons and the second-layer artificial neurons representing each gene. When a chromosomal interval(s) is specified, PosMed explores the second-layer and third-layer artificial neurons representing genes within the chromosomal interval by evaluating the combined significance of the connections from the hit documentrons to the genes. PosMed is, therefore, a powerful tool that immediately ranks the candidate genes by connecting phenotypic keywords to the genes through connections representing not only gene–gene interactions but also other biological interactions (e.g. metabolite–gene, mutant mouse–gene, drug–gene, disease–gene and protein–protein interactions) and ortholog data. By utilizing orthologous connections, PosMed facilitates the ranking of human genes based on evidence found in other model species such as mouse. Currently, PosMed, an artificial superbrain that has learned a vast amount of biological knowledge ranging from genomes to phenomes (or ‘omic space’), supports the prioritization of positional candidate genes in humans, mouse, rat and Arabidopsis thaliana.

Recently, a number of collaborative large-scale mouse mutagenesis programs have been launched. These programs aim for a better understanding of the roles of all individual coding genes and the biological systems in which these genes participate. In international efforts to share phenotypic data among facilities/institutes, it is desirable to integrate information obtained from different phenotypic platforms reliably. Since the definitions of specific phenotypes often depend on a tacit understanding of concepts that tends to vary among different facilities, it is necessary to define phenotypes based on the explicit evidence of assay results. We have developed a website termed PhenoSITE (Phenome Semantics Information with Terminology of Experiments: http://www.gsc.riken.jp/Mouse/), in which we are trying to integrate phenotype-related information using an experimental-evidence-based approach. The site's features include (1) a baseline database for our phenotyping platform; (2) an ontology associating international phenotypic definitions with experimental terminologies used in our phenotyping platform; (3) a database for standardized operation procedures of the phenotyping platform; and (4) a database for mouse mutants using data produced from the large-scale mutagenesis program at RIKEN GSC. We have developed two types of integrated viewers to enhance the accessibility to mutant resource information. One viewer depicts a matrix view of the ontology-based classification and chromosomal location of each gene; the other depicts ontology-mediated integration of experimental protocols, baseline data, and mutant information. These approaches rely entirely upon experiment-based evidence, ensuring the reliability of the integrated data from different phenotyping platforms.

Molecular breeding of crops is an efficient way to upgrade plant functions useful to mankind. A key step is forward genetics or positional cloning to identify the genes that confer useful functions. In order to accelerate the whole research process, we have developed an integrated database system powered by an intelligent data-retrieval engine termed PosMed-plus (Positional Medline for plant upgrading science), allowing us to prioritize highly promising candidate genes in a given chromosomal interval(s) of Arabidopsis thaliana and rice, Oryza sativa. By inferentially integrating cross-species information resources including genomes, transcriptomes, proteomes, localizomes, phenomes and literature, the system compares a user's query, such as phenotypic or functional keywords, with the literature associated with the relevant genes located within the interval. By utilizing orthologous and paralogous correspondences, PosMed-plus efficiently integrates cross-species information to facilitate the ranking of rice candidate genes based on evidence from other model species such as Arabidopsis. PosMed-plus is a plant science version of the PosMed system widely used by mammalian researchers, and provides both a powerful integrative search function and a rich integrative display of the integrated databases. PosMed-plus is the first cross-species integrated database that inferentially prioritizes candidate genes for forward genetics approaches in plant science, and will be expanded for wider use in plant upgrading in many species.

The detection of phenotypic alterations of mutants and variants is one of the bottlenecks that hinder systematic gene functional studies of the model plant Arabidopsis. In an earlier study, we have addressed this problem by proposing a novel methodology for phenome analysis based on in silico analysis of polygon models that are acquired by 3-dimensional (3D) measurement and which precisely reconstruct the actual plant shape. However, 3D quantitative descriptions of morphological traits are rare, whereas conventional 2D descriptions have already been studied but may lack the necessary precision. In this report, we focus on six major leaf morphological traits, which are commonly used in the current manual mutant screens, and propose new 3D quantitative definitions that describe these traits. In experiments to extract the traits, we found significant differences between two variants of Arabidopsis with respect to blade roundness and blade epinasty. Remarkably, the detected difference between variants in the blade roundness trait was undetectable when using conventional 2D descriptions. Thus, the result of the experiment indicates that the proposed definitions with 3D description may lead to new discoveries of phenotypic alteration in gene functional studies that would not be possible using conventional 2D descriptions.