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Dive into the research topics where Akihiro Matsushima is active.

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Featured researches published by Akihiro Matsushima.


Proceedings of the National Academy of Sciences of the United States of America | 2009

Genome-wide suppression of aberrant mRNA-like noncoding RNAs by NMD in Arabidopsis

Yukio Kurihara; Akihiro Matsui; Kousuke Hanada; Makiko Kawashima; Junko Ishida; Taeko Morosawa; Maho Tanaka; Eli Kaminuma; Yoshiki Mochizuki; Akihiro Matsushima; Tetsuro Toyoda; Kazuo Shinozaki; Motoaki Seki

The nonsense-mediated mRNA decay (NMD) pathway is a well-known eukaryotic surveillance mechanism that eliminates aberrant mRNAs that contain a premature termination codon (PTC). The UP-Frameshift (UPF) proteins, UPF1, UPF2, and UPF3, are essential for normal NMD function. Several NMD substrates have been identified, but detailed information on NMD substrates is lacking. Here, we noticed that, in Arabidopsis, most of the mRNA-like nonprotein-coding RNAs (ncRNAs) have the features of an NMD substrate. We examined the expression profiles of 2 Arabidopsis mutants, upf1-1 and upf3-1, using a whole-genome tiling array. The results showed that expression of not only protein-coding transcripts but also many mRNA-like ncRNAs (mlncRNAs), including natural antisense transcript RNAs (nat-RNAs) transcribed from the opposite strands of the coding strands, were up-regulated in both mutants. The percentage of the up-regulated mlncRNAs to all expressed mlncRNAs was much higher than that of the up-regulated protein-coding transcripts to all expressed protein- coding transcripts. This finding demonstrates that one of the most important roles of NMD is the genome-wide suppression of the aberrant mlncRNAs including nat-RNAs.


Nucleic Acids Research | 2009

PosMed (Positional Medline): prioritizing genes with an artificial neural network comprising medical documents to accelerate positional cloning

Yuko Yoshida; Yuko Makita; Naohiko Heida; Satomi Asano; Akihiro Matsushima; Manabu Ishii; Yoshiki Mochizuki; Hiroshi Masuya; Shigeharu Wakana; Norio Kobayashi; Tetsuro Toyoda

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.


PLOS ONE | 2012

ECOMICS: A Web-Based Toolkit for Investigating the Biomolecular Web in Ecosystems Using a Trans-omics Approach

Yoshiyuki Ogata; Eisuke Chikayama; Yusuke Morioka; R. Craig Everroad; Amiu Shino; Akihiro Matsushima; Hideaki Haruna; Shigeharu Moriya; Tetsuro Toyoda; Jun Kikuchi

Ecosystems can be conceptually thought of as interconnected environmental and metabolic systems, in which small molecules to macro-molecules interact through diverse networks. State-of-the-art technologies in post-genomic science offer ways to inspect and analyze this biomolecular web using omics-based approaches. Exploring useful genes and enzymes, as well as biomass resources responsible for anabolism and catabolism within ecosystems will contribute to a better understanding of environmental functions and their application to biotechnology. Here we present ECOMICS, a suite of web-based tools for ECosystem trans-OMICS investigation that target metagenomic, metatranscriptomic, and meta-metabolomic systems, including biomacromolecular mixtures derived from biomass. ECOMICS is made of four integrated webtools. E-class allows for the sequence-based taxonomic classification of eukaryotic and prokaryotic ribosomal data and the functional classification of selected enzymes. FT2B allows for the digital processing of NMR spectra for downstream metabolic or chemical phenotyping. Bm-Char allows for statistical assignment of specific compounds found in lignocellulose-based biomass, and HetMap is a data matrix generator and correlation calculator that can be applied to trans-omics datasets as analyzed by these and other web tools. This web suite is unique in that it allows for the monitoring of biomass metabolism in a particular environment, i.e., from macromolecular complexes (FT2DB and Bm-Char) to microbial composition and degradation (E-class), and makes possible the understanding of relationships between molecular and microbial elements (HetMap). This website is available to the public domain at: https://database.riken.jp/ecomics/.


Nucleic Acids Research | 2011

The RIKEN integrated database of mammals

Hiroshi Masuya; Yuko Makita; Norio Kobayashi; Koro Nishikata; Yuko Yoshida; Yoshiki Mochizuki; Koji Doi; Terue Takatsuki; Kazunori Waki; Nobuhiko Tanaka; Manabu Ishii; Akihiro Matsushima; Satoshi Takahashi; Atsushi Hijikata; Kouji Kozaki; Teiichi Furuichi; Hideya Kawaji; Shigeharu Wakana; Yukio Nakamura; Atsushi Yoshiki; Takehide Murata; Kaoru Fukami-Kobayashi; S. Sujatha Mohan; Osamu Ohara; Yoshihide Hayashizaki; Riichiro Mizoguchi; Yuichi Obata; Tetsuro Toyoda

The RIKEN integrated database of mammals (http://scinets.org/db/mammal) is the official undertaking to integrate its mammalian databases produced from multiple large-scale programs that have been promoted by the institute. The database integrates not only RIKEN’s original databases, such as FANTOM, the ENU mutagenesis program, the RIKEN Cerebellar Development Transcriptome Database and the Bioresource Database, but also imported data from public databases, such as Ensembl, MGI and biomedical ontologies. Our integrated database has been implemented on the infrastructure of publication medium for databases, termed SciNetS/SciNeS, or the Scientists’ Networking System, where the data and metadata are structured as a semantic web and are downloadable in various standardized formats. The top-level ontology-based implementation of mammal-related data directly integrates the representative knowledge and individual data records in existing databases to ensure advanced cross-database searches and reduced unevenness of the data management operations. Through the development of this database, we propose a novel methodology for the development of standardized comprehensive management of heterogeneous data sets in multiple databases to improve the sustainability, accessibility, utility and publicity of the data of biomedical information.


Plant and Cell Physiology | 2009

PosMed-plus: an intelligent search engine that inferentially integrates cross-species information resources for molecular breeding of plants.

Yuko Makita; Norio Kobayashi; Yoshiki Mochizuki; Yuko Yoshida; Satomi Asano; Naohiko Heida; Mrinalini Deshpande; Rinki Bhatia; Akihiro Matsushima; Manabu Ishii; Shuji Kawaguchi; Kei Iida; Kosuke Hanada; Takashi Kuromori; Motoaki Seki; Kazuo Shinozaki; Tetsuro Toyoda

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 users 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.


Nucleic Acids Research | 2013

PosMed: ranking genes and bioresources based on Semantic Web Association Study

Yuko Makita; Norio Kobayashi; Yuko Yoshida; Koji Doi; Yoshiki Mochizuki; Koro Nishikata; Akihiro Matsushima; Satoshi Takahashi; Manabu Ishii; Terue Takatsuki; Rinki Bhatia; Zolzaya Khadbaatar; Hajime Watabe; Hiroshi Masuya; Tetsuro Toyoda

Positional MEDLINE (PosMed; http://biolod.org/PosMed) is a powerful Semantic Web Association Study engine that ranks biomedical resources such as genes, metabolites, diseases and drugs, based on the statistical significance of associations between user-specified phenotypic keywords and resources connected directly or inferentially through a Semantic Web of biological databases such as MEDLINE, OMIM, pathways, co-expressions, molecular interactions and ontology terms. Since 2005, PosMed has long been used for in silico positional cloning studies to infer candidate disease-responsible genes existing within chromosomal intervals. PosMed is redesigned as a workbench to discover possible functional interpretations for numerous genetic variants found from exome sequencing of human disease samples. We also show that the association search engine enhances the value of mouse bioresources because most knockout mouse resources have no phenotypic annotation, but can be associated inferentially to phenotypes via genes and biomedical documents. For this purpose, we established text-mining rules to the biomedical documents by careful human curation work, and created a huge amount of correct linking between genes and documents. PosMed associates any phenotypic keyword to mouse resources with 20 public databases and four original data sets as of May 2013.


Nucleic Acids Research | 2009

OmicBrowse: a Flash-based high-performance graphics interface for genomic resources

Akihiro Matsushima; Norio Kobayashi; Yoshiki Mochizuki; Manabu Ishii; Shuji Kawaguchi; Takaho A. Endo; Ryo Umetsu; Yuko Makita; Tetsuro Toyoda

OmicBrowse is a genome browser designed as a scalable system for maintaining numerous genome annotation datasets. It is an open source tool capable of regulating multiple user data access to each dataset to allow multiple users to have their own integrative view of both their unpublished and published datasets, so that the maintenance costs related to supplying each collaborator exclusively with their own private data are significantly reduced. OmicBrowse supports DAS1 imports and exports of annotations to Internet site servers worldwide. We also provide a data-download named OmicDownload server that interactively selects datasets and filters the data on the selected datasets. Our OmicBrowse server has been freely available at http://omicspace.riken.jp/ since its launch in 2003. The OmicBrowse source code is downloadable from http://sourceforge.net/projects/omicbrowse/.


Nature Precedings | 2010

Logical Operation Based Literature Association with Genes and its application, PosMed.

Yuko Makita; Rinki Bhatia; Mrinalini Deshpande; Akihiro Matsushima; Manabu Ishii; Yoshiki Mochizuki; Yuko Yoshida; Norio Kobayashi; Testuro Toyoda

Makita Y, Kobayashi N, Mochizuki Y, Yoshida Y, Asano S, Heida N, Deshpande M, Bhatia R, Matsushima A, Ishii M, Kawaguchi S, Iida K, Hanada K, Kuromori T, Seki M, Shinozaki K, Toyoda T. PosMed-plus: an intelligent search engine that inferentially integrates cross-species information resources for molecular breeding of plants. Plant Cell Physiol. 2009 50(7):1249-59. PMID: 19528193 Yoshida Y, Makita Y, Heida N, Asano S, Matsushima A, Ishii M, Mochizuki Y, Masuya H, Wakana S, Kobayashi N, Toyoda T. PosMed (Positional Medline): prioritizing genes with an artificial neural network comprising medical documents to accelerate positional cloning. Nucleic Acids Res. 2009 37(Web Server issue):W147-52. PMID: 19468046 PosMed 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 (Yoshida, et al. 2009). PosMed immediately ranks the candidate genes by connecting phenotypic keywords to the genes through connections representing gene–gene interactions other biological relationships, such as metabolite–gene, mutant mouse– gene, drug–gene, disease–gene, and protein–protein interactions, ortholog data, and gene–literature connections. To make proper relationships between genes and literature, we manually curate queries, which are defined by logical operation rules, against MEDLINE. For example, to detect a set of MEDLINE documents for the AT1G03880 gene in A. thaliana, we applied the following logical query: (‘AT1G03880’ OR ‘CRU2’ OR ‘CRB’ OR ‘CRUCIFERIN 2’ OR ‘CRUCIFERIN B’) AND (‘Arabidopsis’) NOT (‘chloroplast RNA binding’). Curators refined these queries in mouse, rice and A. thaliana. For human and rat genes, we use mouse curation results via ortholog genes in PosMed.


Nucleic Acids Research | 2011

Semantic-JSON: a lightweight web service interface for Semantic Web contents integrating multiple life science databases

Norio Kobayashi; Manabu Ishii; Satoshi Takahashi; Yoshiki Mochizuki; Akihiro Matsushima; Tetsuro Toyoda


Nature Precedings | 2010

SciNetS Search : Inference Search over an Integrated Life-sciences Database Based on the Semantic Web

Norio Kobayashi; Manabu Ishii; Yuko Yoshida; Yuko Makita; Akihiro Matsushima; Yoshiki Mochizuki; Tetsuro Toyoda

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Yoshiki Mochizuki

RIKEN Brain Science Institute

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Manabu Ishii

RIKEN Brain Science Institute

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Norio Kobayashi

RIKEN Brain Science Institute

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Yuko Yoshida

RIKEN Brain Science Institute

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Hiroshi Masuya

National Institute of Genetics

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Koro Nishikata

Yokohama City University

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