Biomedical subjects

Naohiko Heida

Publications and source records attributed to Naohiko Heida.

6 recordsLinked to original sources

PubMed2006

OmicBrowse: a browser of multidimensional omics annotations.

UNLABELLED: OmicBrowse is a browser to explore multiple datasets coordinated in the multidimensional omic space integrating omics knowledge ranging from genomes to phenomes and connecting evolutional correspondences among multiple species. OmicBrowse integrates multiple data servers into a single omic space through secure peer-to-peer server communications, so that a user can easily obtain an integrated view of distributed data servers, e.g. an integrated view of numerous whole-genome tiling-array data retrieved from a user's in-house private-data server, along with various genomic annotations from public internet servers. OmicBrowse is especially appropriate for positional-cloning purposes. It displays both genetic maps and genomic annotations within wide chromosomal intervals and assists a user to select candidate genes by filtering their annotations or associated documents against user-specified keywords or ontology terms. We also show that an omic-space chart effectively represents schemes for integrating multiple datasets of multiple species. AVAILABILITY: OmicBrowse is developed by the Genome-Phenome Superbrain Project and is released as free open-source software under the GNU General Public License at http://omicspace.riken.jp.

Chromosome Mapping↗

PubMed2006

A flexible representation of omic knowledge for thorough analysis of microarray data.

BACKGROUND: In order to understand microarray data reasonably in the context of other existing biological knowledge, it is necessary to conduct a thorough examination of the data utilizing every aspect of available omic knowledge libraries. So far, a number of bioinformatics tools have been developed. However, each of them is restricted to deal with one type of omic knowledge, e.g., pathways, interactions or gene ontology. Now that the varieties of omic knowledge are expanding, analysis tools need a way to deal with any type of omic knowledge. Hence, we have designed the Omic Space Markup Language (OSML) that can represent a wide range of omic knowledge, and also, we have developed a tool named GSCope3, which can statistically analyze microarray data in comparison with the OSML-formatted omic knowledge data. RESULTS: In order to test the applicability of OSML to represent a variety of omic knowledge specifically useful for analysis of Arabidopsis thaliana microarray data, we have constructed a Biological Knowledge Library (BiKLi) by converting eight different types of omic knowledge into OSML-formatted datasets. We applied GSCope3 and BiKLi to previously reported A. thaliana microarray data, so as to extract any additional insights from the data. As a result, we have discovered a new insight that lignin formation resists drought stress and activates transcription of many water channel genes to oppose drought stress; and most of the 20S proteasome subunit genes show similar expression profiles under drought stress. In addition to this novel discovery, similar findings previously reported were also quickly confirmed using GSCope3 and BiKLi. CONCLUSION: GSCope3 can statistically analyze microarray data in the context of any OSML-represented omic knowledge. OSML is not restricted to a specific data type structure, but it can represent a wide range of omic knowledge. It allows us to convert new types of omic knowledge into datasets that can be used for microarray data analysis with GSCope3. In addition to BiKLi, by collecting various types of omic knowledge as OSML libraries, it becomes possible for us to conduct detailed thorough analysis from various biological viewpoints. GSCope3 and BiKLi are available for academic users at our web site http://omicspace.riken.jp.

Journal Article↗

PubMed2005

Three-dimensional definition of leaf morphological traits of Arabidopsis in silico phenotypic analysis.

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.

Algorithms↗

PubMed2005

In silico phenotypic screening method of mutants based on statistical modeling of genetically mixed samples.

In comprehensive functional genomics projects, systematic analysis of phenotypes is vital. However, conventional phenotypic screening is done mainly by imprecise visual observation of qualitative traits, and, therefore, in silico screening techniques for quantitative traits are required. In this report, we propose in silico phenotypic screening method that utilizes a Gaussian mixture model for the trait distribution in the offspring of a mutagenized line and the likelihood ratio test between the estimated Gaussian mixture model and the wild-type single Gaussian model. In order to evaluate the proposed method, we performed a screening experiment using real trait data of Arabidopsis. In this experiment, the proposed screening method properly distinguished the mutant line from the wild-type line. Furthermore, we conducted power analysis of the proposed method and two conventional methods under various simulated conditions of sample size and distribution of trait frequency. The result of the power analysis confirmed the effectiveness of the proposed method compared to the conventional methods.

Algorithms↗

PubMed2004

TraitMap: an XML-based genetic-map database combining multigenic loci and biomolecular networks.

MOTIVATION: Most ordinary traits are well described by multiple measurable parameters. Thus, in the course of elucidating the genes responsible for a given trait, it is necessary to conduct and integrate the genetic mapping of each parameter. However, the integration of multiple mapping results from different publications is prevented by the fact that they are conventionally published and accumulated in printed forms or graphics which are difficult for computers to reuse for further analyses. RESULTS: We have defined an XML-based schema as a container of genetic mapping results, and created a database named TraitMap containing curator-checked data records based on published papers of mapping results in Homosapiens, Mus musculus, and Arabidopsis thaliana. TraitMap is the first database of mapping charts in genetics, and is integrated in a web-based retrieval framework: termed Genome <--> Phenome Superhighway (GPS) system, where it is possible to combine and visualize multiple mapping records in a two-dimensional display. Since most traits are regulated by multiple genes, the system associates every combination of genetic loci to biomolecular networks, and thus helps us to estimate molecular-level candidate networks responsible for a given trait. It is demonstrated that a combined analysis of two diabetes-related traits (susceptibility to insulin resistance and non-HDL cholesterol level) suggests that molecular-level relationships such as the interaction among leptin receptor (Lepr), peroxisome proliferators-activated receptor-gamma (Pparg) and insulin receptor substrate 1 (Irs1), are candidate causal networks affecting the traits in a multigenic manner. AVAILABILITY: TraitMap database and GPS are accessible at http://omicspace.riken.jp/gps/

Chromosome Mapping↗

PubMed2004

Automatic quantification of morphological traits via three-dimensional measurement of Arabidopsis.

Many 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 finder (LRF) and automatic data processing. We measured the 3D surfaces of young plants of two Arabidopsis ecotypes and successfully defined 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 find new traits from mutant pools or natural ecotypes based on 3D data.

Algorithms↗