Toshiaki Katayama

Publications of Toshiaki Katayama

• Biogem: an effective tool-based approach for scaling up open source software development in bioinformatics.

  Authors: Raoul J P Bonnal, Jan Aerts, George Githinji, Naohisa Goto, Dan Maclean, Chase A Miller, Hiroyuki Mishima, Massimiliano Pagani, Ricardo Ramirez-Gonzalez, Geert Smant, Francesco Strozzi, Rob Syme, Rutger Vos, Trevor J Wennblom, Ben J Woodcroft, Toshiaki Katayama, Pjotr Prins

  Bioinformatics (Oxford, England). 02/2012; 28(7):1035-1037.

  SUMMARY: Biogem provides a software development environment for the Ruby programming language, which encourages community-based software development for bioinformatics while lowering the barrier toSUMMARY: Biogem provides a software development environment for the Ruby programming language, which encourages community-based software development for bioinformatics while lowering the barrier to entry and encouraging best practices. Biogem, with its targeted modular and decentralized approach, software generator, tools and tight web integration, is an improved general model for scaling up collaborative open source software development in bioinformatics. AVAILABILITY: Biogem and modules are free and are OSS. Biogem runs on all systems that support recent versions of Ruby, including Linux, Mac OS X and Windows. Further information at http://www.biogems.info. A tutorial is available at http://www.biogems.info/howto.html CONTACT: bonnal@ingm.org.

• Sharing programming resources between bio* projects through remote procedure call and native call stack strategies.

  Authors: Pjotr Prins, Naohisa Goto, Andrew Yates, Laurent Gautier, Scooter Willis, Christopher Fields, Toshiaki Katayama

  Methods in molecular biology (Clifton, N.J.). 01/2012; 856:513-27.

  Open-source software (OSS) encourages computer programmers to reuse software components written by others. In evolutionary bioinformatics, OSS comes in a broad range of programming languages,Open-source software (OSS) encourages computer programmers to reuse software components written by others. In evolutionary bioinformatics, OSS comes in a broad range of programming languages, including C/C++, Perl, Python, Ruby, Java, and R. To avoid writing the same functionality multiple times for different languages, it is possible to share components by bridging computer languages and Bio* projects, such as BioPerl, Biopython, BioRuby, BioJava, and R/Bioconductor. In this chapter, we compare the two principal approaches for sharing software between different programming languages: either by remote procedure call (RPC) or by sharing a local call stack. RPC provides a language-independent protocol over a network interface; examples are RSOAP and Rserve. The local call stack provides a between-language mapping not over the network interface, but directly in computer memory; examples are R bindings, RPy, and languages sharing the Java Virtual Machine stack. This functionality provides strategies for sharing of software between Bio* projects, which can be exploited more often. Here, we present cross-language examples for sequence translation, and measure throughput of the different options. We compare calling into R through native R, RSOAP, Rserve, and RPy interfaces, with the performance of native BioPerl, Biopython, BioJava, and BioRuby implementations, and with call stack bindings to BioJava and the European Molecular Biology Open Software Suite. In general, call stack approaches outperform native Bio* implementations and these, in turn, outperform RPC-based approaches. To test and compare strategies, we provide a downloadable BioNode image with all examples, tools, and libraries included. The BioNode image can be run on VirtualBox-supported operating systems, including Windows, OSX, and Linux.

• The 2nd DBCLS BioHackathon: interoperable bioinformatics Web services for integrated applications.

  Authors: Toshiaki Katayama, Mark D Wilkinson, Rutger Vos, Takeshi Kawashima, Shuichi Kawashima, Mitsuteru Nakao, Yasunori Yamamoto, Hong-Woo Chun, Atsuko Yamaguchi, Shin Kawano [......] Martin Senger, Jessica Severin, Yasumasa Shigemoto, Hideaki Sugawara, James Taylor, Oswaldo Trelles, Chisato Yamasaki, Riu Yamashita, Noriyuki Satoh, Toshihisa Takagi

  Journal of biomedical semantics. 08/2011; 2:4.

  ABSTRACT: The interaction between biological researchers and the bioinformatics tools they use is still hampered by incomplete interoperability between such tools. To ensure interoperabilityABSTRACT: The interaction between biological researchers and the bioinformatics tools they use is still hampered by incomplete interoperability between such tools. To ensure interoperability initiatives are effectively deployed, end-user applications need to be aware of, and support, best practices and standards. Here, we report on an initiative in which software developers and genome biologists came together to explore and raise awareness of these issues: BioHackathon 2009. Developers in attendance came from diverse backgrounds, with experts in Web services, workflow tools, text mining and visualization. Genome biologists provided expertise and exemplar data from the domains of sequence and pathway analysis and glyco-informatics. One goal of the meeting was to evaluate the ability to address real world use cases in these domains using the tools that the developers represented. This resulted in i) a workflow to annotate 100,000 sequences from an invertebrate species; ii) an integrated system for analysis of the transcription factor binding sites (TFBSs) enriched based on differential gene expression data obtained from a microarray experiment; iii) a workflow to enumerate putative physical protein interactions among enzymes in a metabolic pathway using protein structure data; iv) a workflow to analyze glyco-gene-related diseases by searching for human homologs of glyco-genes in other species, such as fruit flies, and retrieving their phenotype-annotated SNPs. Beyond deriving prototype solutions for each use-case, a second major purpose of the BioHackathon was to highlight areas of insufficiency. We discuss the issues raised by our exploration of the problem/solution space, concluding that there are still problems with the way Web services are modeled and annotated, including: i) the absence of several useful data or analysis functions in the Web service "space"; ii) the lack of documentation of methods; iii) lack of compliance with the SOAP/WSDL specification among and between various programming-language libraries; and iv) incompatibility between various bioinformatics data formats. Although it was still difficult to solve real world problems posed to the developers by the biological researchers in attendance because of these problems, we note the promise of addressing these issues within a semantic framework.

• BioMart Central Portal: an open database network for the biological community.

  Authors: Jonathan M Guberman, J Ai, O Arnaiz, Joachim Baran, Andrew Blake, Richard Baldock, Claude Chelala, David Croft, Anthony Cros, Rosalind J Cutts [......] Jun Wang, Jianxin Wang, Brett Whitty, D T Wong, Marie Wong-Erasmus, L Yao, Ken Youens-Clark, Christina Yung, Junjun Zhang, Arek Kasprzyk

  Database : the journal of biological databases and curation. 01/2011; 2011:bar041.

  BioMart Central Portal is a first of its kind, community-driven effort to provide unified access to dozens of biological databases spanning genomics, proteomics, model organisms, cancer data,BioMart Central Portal is a first of its kind, community-driven effort to provide unified access to dozens of biological databases spanning genomics, proteomics, model organisms, cancer data, ontology information and more. Anybody can contribute an independently maintained resource to the Central Portal, allowing it to be exposed to and shared with the research community, and linking it with the other resources in the portal. Users can take advantage of the common interface to quickly utilize different sources without learning a new system for each. The system also simplifies cross-database searches that might otherwise require several complicated steps. Several integrated tools streamline common tasks, such as converting between ID formats and retrieving sequences. The combination of a wide variety of databases, an easy-to-use interface, robust programmatic access and the array of tools make Central Portal a one-stop shop for biological data querying. Here, we describe the structure of Central Portal and show example queries to demonstrate its capabilities.

• BioRuby: bioinformatics software for the Ruby programming language.

  Authors: Naohisa Goto, Pjotr Prins, Mitsuteru Nakao, Raoul Bonnal, Jan Aerts, Toshiaki Katayama

  Bioinformatics (Oxford, England). 10/2010; 26(20):2617-9.

  The BioRuby software toolkit contains a comprehensive set of free development tools and libraries for bioinformatics and molecular biology, written in the Ruby programming language. BioRuby hasThe BioRuby software toolkit contains a comprehensive set of free development tools and libraries for bioinformatics and molecular biology, written in the Ruby programming language. BioRuby has components for sequence analysis, pathway analysis, protein modelling and phylogenetic analysis; it supports many widely used data formats and provides easy access to databases, external programs and public web services, including BLAST, KEGG, GenBank, MEDLINE and GO. BioRuby comes with a tutorial, documentation and an interactive environment, which can be used in the shell, and in the web browser. BioRuby is free and open source software, made available under the Ruby license. BioRuby runs on all platforms that support Ruby, including Linux, Mac OS X and Windows. And, with JRuby, BioRuby runs on the Java Virtual Machine. The source code is available from http://www.bioruby.org/. katayama@bioruby.org

• TogoWS: integrated SOAP and REST APIs for interoperable bioinformatics Web services.

  Authors: Toshiaki Katayama, Mitsuteru Nakao, Toshihisa Takagi

  Nucleic acids research. 07/2010; 38(Web Server issue):W706-11.

  Web services have become widely used in bioinformatics analysis, but there exist incompatibilities in interfaces and data types, which prevent users from making full use of a combination of theseWeb services have become widely used in bioinformatics analysis, but there exist incompatibilities in interfaces and data types, which prevent users from making full use of a combination of these services. Therefore, we have developed the TogoWS service to provide an integrated interface with advanced features. In the TogoWS REST (REpresentative State Transfer) API (application programming interface), we introduce a unified access method for major database resources through intuitive URIs that can be used to search, retrieve, parse and convert the database entries. The TogoWS SOAP API resolves compatibility issues found on the server and client-side SOAP implementations. The TogoWS service is freely available at: http://togows.dbcls.jp/.

• The DBCLS BioHackathon: standardization and interoperability for bioinformatics web services and workflows. The DBCLS BioHackathon Consortium*.

  Authors: Toshiaki Katayama, Kazuharu Arakawa, Mitsuteru Nakao, Keiichiro Ono, Kiyoko F Aoki-Kinoshita, Yasunori Yamamoto, Atsuko Yamaguchi, Shuichi Kawashima, Hong-Woo Chun, Jan Aerts [......] Daron M Standley, Hideaki Sugawara, Toshiyuki Tashiro, Oswaldo Trelles, Rutger A Vos, Mark D Wilkinson, William York, Christian M Zmasek, Kiyoshi Asai, Toshihisa Takagi

  Journal of biomedical semantics. 01/2010; 1(1):8.

  Web services have become a key technology for bioinformatics, since life science databases are globally decentralized and the exponential increase in the amount of available data demands forWeb services have become a key technology for bioinformatics, since life science databases are globally decentralized and the exponential increase in the amount of available data demands for efficient systems without the need to transfer entire databases for every step of an analysis. However, various incompatibilities among database resources and analysis services make it difficult to connect and integrate these into interoperable workflows. To resolve this situation, we invited domain specialists from web service providers, client software developers, Open Bio* projects, the BioMoby project and researchers of emerging areas where a standard exchange data format is not well established, for an intensive collaboration entitled the BioHackathon 2008. The meeting was hosted by the Database Center for Life Science (DBCLS) and Computational Biology Research Center (CBRC) and was held in Tokyo from February 11th to 15th, 2008. In this report we highlight the work accomplished and the common issues arisen from this event, including the standardization of data exchange formats and services in the emerging fields of glycoinformatics, biological interaction networks, text mining, and phyloinformatics. In addition, common shared object development based on BioSQL, as well as technical challenges in large data management, asynchronous services, and security are discussed. Consequently, we improved interoperability of web services in several fields, however, further cooperation among major database centers and continued collaborative efforts between service providers and software developers are still necessary for an effective advance in bioinformatics web service technologies.

• Full-Malaria/Parasites and Full-Arthropods: databases of full-length cDNAs of parasites and arthropods, update 2009.

  Authors: Hiroyuki Wakaguri, Yutaka Suzuki, Toshiaki Katayama, Shuichi Kawashima, Eri Kibukawa, Kazushi Hiranuka, Masahide Sasaki, Sumio Sugano, Junichi Watanabe

  Nucleic acids research. 12/2008;

  Full-Malaria/Parasites is a database for transcriptome studies of apicomplexa and other parasites, which is based on our original full-length cDNA sequences and physical cDNA clone resources. In thisFull-Malaria/Parasites is a database for transcriptome studies of apicomplexa and other parasites, which is based on our original full-length cDNA sequences and physical cDNA clone resources. In this update, the database has been expanded to contain the shogun sequencing for the entire sequences of 14 818 non-redundant full-length cDNA clones from six apicomplexa parasites and 6.8 million of transcription start sites (TSS), both of which had been produced by novel protocols using the oligo-capping method and the Illumina GA sequencer. The former should be the ultimate data for exact annotation of the expressed genes, while the latter should be useful for ultra-deep expression analysis. Furthermore, we have launched Full-Arthropods, a full-length cDNA database for arthropods of medical importance. Full-Arthropods contains 50 343 one-pass sequences, 10 399 shotgun complete sequences and 22.4 million TSS tags in anopheles mosquitoes that transmit malaria, tsetse flies that transmit trypanosomiasis and dust mites that cause allergic dermatitis and bronchial asthma. By providing the largest integrated full-length cDNA data resources in the apicomplexa parasites as well as their vectors, Full-Malaria/Parasites and Full-Arthropods should help combat parasitic diseases. Full-Malaria/Parasites and Full-Arthropods are accessible from http://fullmal.hgc.jp/.

• KEGG Atlas mapping for global analysis of metabolic pathways.

  Authors: Shujiro Okuda, Takuji Yamada, Masami Hamajima, Masumi Itoh, Toshiaki Katayama, Peer Bork, Susumu Goto, Minoru Kanehisa

  Nucleic acids research. 08/2008; 36(Web Server issue):W423-6.

  KEGG Atlas is a new graphical interface to the KEGG suite of databases, especially to the systems information in the PATHWAY and BRITE databases. It currently consists of a single global map and anKEGG Atlas is a new graphical interface to the KEGG suite of databases, especially to the systems information in the PATHWAY and BRITE databases. It currently consists of a single global map and an associated viewer for metabolism, covering about 120 KEGG metabolic pathway maps and about 10 BRITE hierarchies. The viewer allows the user to navigate and zoom the global map under the Ajax technology. The mapping of high-throughput experimental data onto the global map is the main use of KEGG Atlas. In the global metabolism map, the node (circle) is a chemical compound and the edge (line) is a set of reactions linked to a set of KEGG Orthology (KO) entries for enzyme genes. Once gene identifiers in different organisms are converted to the K number identifiers in the KO system, corresponding line segments can be highlighted in the global map, allowing the user to view genome sequence data as organism-specific pathways, gene expression data as up- or down-regulated pathways, etc. Once chemical compounds are converted to the C number identifiers in KEGG, metabolomics data can also be displayed in the global map. KEGG Atlas is available at http://www.genome.jp/kegg/atlas/.

• AAindex: amino acid index database, progress report 2008.

  Authors: Shuichi Kawashima, Piotr Pokarowski, Maria Pokarowska, Andrzej Kolinski, Toshiaki Katayama, Minoru Kanehisa

  Nucleic acids research. 02/2008; 36(Database issue):D202-5.

  AAindex is a database of numerical indices representing various physicochemical and biochemical properties of amino acids and pairs of amino acids. We have added a collection of protein contactAAindex is a database of numerical indices representing various physicochemical and biochemical properties of amino acids and pairs of amino acids. We have added a collection of protein contact potentials to the AAindex as a new section. Accordingly AAindex consists of three sections now: AAindex1 for the amino acid index of 20 numerical values, AAindex2 for the amino acid substitution matrix and AAindex3 for the statistical protein contact potentials. All data are derived from published literature. The database can be accessed through the DBGET/LinkDB system at GenomeNet (http://www.genome.jp/dbget-bin/www_bfind?aaindex) or downloaded by anonymous FTP (ftp://ftp.genome.jp/pub/db/community/aaindex/).

• KEGG for linking genomes to life and the environment.

  Authors: Minoru Kanehisa, Michihiro Araki, Susumu Goto, Masahiro Hattori, Mika Hirakawa, Masumi Itoh, Toshiaki Katayama, Shuichi Kawashima, Shujiro Okuda, Toshiaki Tokimatsu, Yoshihiro Yamanishi

  Nucleic acids research. 02/2008; 36(Database issue):D480-4.

  KEGG (http://www.genome.jp/kegg/) is a database of biological systems that integrates genomic, chemical and systemic functional information. KEGG provides a reference knowledge base for linkingKEGG (http://www.genome.jp/kegg/) is a database of biological systems that integrates genomic, chemical and systemic functional information. KEGG provides a reference knowledge base for linking genomes to life through the process of PATHWAY mapping, which is to map, for example, a genomic or transcriptomic content of genes to KEGG reference pathways to infer systemic behaviors of the cell or the organism. In addition, KEGG provides a reference knowledge base for linking genomes to the environment, such as for the analysis of drug-target relationships, through the process of BRITE mapping. KEGG BRITE is an ontology database representing functional hierarchies of various biological objects, including molecules, cells, organisms, diseases and drugs, as well as relationships among them. KEGG PATHWAY is now supplemented with a new global map of metabolic pathways, which is essentially a combined map of about 120 existing pathway maps. In addition, smaller pathway modules are defined and stored in KEGG MODULE that also contains other functional units and complexes. The KEGG resource is being expanded to suit the needs for practical applications. KEGG DRUG contains all approved drugs in the US and Japan, and KEGG DISEASE is a new database linking disease genes, pathways, drugs and diagnostic markers.

• From genomics to chemical genomics: new developments in KEGG.

  Authors: Minoru Kanehisa, Susumu Goto, Masahiro Hattori, Kiyoko F Aoki-Kinoshita, Masumi Itoh, Shuichi Kawashima, Toshiaki Katayama, Michihiro Araki, Mika Hirakawa

  Nucleic acids research. 02/2006; 34(Database issue):D354-7.

  The increasing amount of genomic and molecular information is the basis for understanding higher-order biological systems, such as the cell and the organism, and their interactions with theThe increasing amount of genomic and molecular information is the basis for understanding higher-order biological systems, such as the cell and the organism, and their interactions with the environment, as well as for medical, industrial and other practical applications. The KEGG resource (http://www.genome.jp/kegg/) provides a reference knowledge base for linking genomes to biological systems, categorized as building blocks in the genomic space (KEGG GENES) and the chemical space (KEGG LIGAND), and wiring diagrams of interaction networks and reaction networks (KEGG PATHWAY). A fourth component, KEGG BRITE, has been formally added to the KEGG suite of databases. This reflects our attempt to computerize functional interpretations as part of the pathway reconstruction process based on the hierarchically structured knowledge about the genomic, chemical and network spaces. In accordance with the new chemical genomics initiatives, the scope of KEGG LIGAND has been significantly expanded to cover both endogenous and exogenous molecules. Specifically, RPAIR contains curated chemical structure transformation patterns extracted from known enzymatic reactions, which would enable analysis of genome-environment interactions, such as the prediction of new reactions and new enzyme genes that would degrade new environmental compounds. Additionally, drug information is now stored separately and linked to new KEGG DRUG structure maps.

• ODB: a database of operons accumulating known operons across multiple genomes.

  Authors: Shujiro Okuda, Toshiaki Katayama, Shuichi Kawashima, Susumu Goto, Minoru Kanehisa

  Nucleic acids research. 02/2006; 34(Database issue):D358-62.

  Operon structures play an important role in co-regulation in prokaryotes. Although over 200 complete genome sequences are now available, databases providing genome-wide operon information have beenOperon structures play an important role in co-regulation in prokaryotes. Although over 200 complete genome sequences are now available, databases providing genome-wide operon information have been limited to certain specific genomes. Thus, we have developed an ODB (Operon DataBase), which provides a data retrieval system of known operons among the many complete genomes. Additionally, putative operons that are conserved in terms of known operons are also provided. The current version of our database contains about 2000 known operon information in more than 50 genomes and about 13 000 putative operons in more than 200 genomes. This system integrates four types of associations: genome context, gene co-expression obtained from microarray data, functional links in biological pathways and the conservation of gene order across the genomes. These associations are indicators of the genes that organize an operon, and the combination of these indicators allows us to predict more reliable operons. Furthermore, our system validates these predictions using known operon information obtained from the literature. This database integrates known literature-based information and genomic data. In addition, it provides an operon prediction tool, which make the system useful for both bioinformatics researchers and experimental biologists. Our database is accessible at http://odb.kuicr.kyoto-u.ac.jp/.

• Utilizing evolutionary information and gene expression data for estimating gene networks with bayesian network models.

  Authors: Yoshinori Tamada, Hideo Bannai, Seiya Imoto, Toshiaki Katayama, Minoru Kanehisa, Satoru Miyano

  Journal of bioinformatics and computational biology. 01/2006; 3(6):1295-313.

  Since microarray gene expression data do not contain sufficient information for estimating accurate gene networks, other biological information has been considered to improve the estimated networks.Since microarray gene expression data do not contain sufficient information for estimating accurate gene networks, other biological information has been considered to improve the estimated networks. Recent studies have revealed that highly conserved proteins that exhibit similar expression patterns in different organisms, have almost the same function in each organism. Such conserved proteins are also known to play similar roles in terms of the regulation of genes. Therefore, this evolutionary information can be used to refine regulatory relationships among genes, which are estimated from gene expression data. We propose a statistical method for estimating gene networks from gene expression data by utilizing evolutionarily conserved relationships between genes. Our method simultaneously estimates two gene networks of two distinct organisms, with a Bayesian network model utilizing the evolutionary information so that gene expression data of one organism helps to estimate the gene network of the other. We show the effectiveness of the method through the analysis on Saccharomyces cerevisiae and Homo sapiens cell cycle gene expression data. Our method was successful in estimating gene networks that capture many known relationships as well as several unknown relationships which are likely to be novel. Supplementary information is available at http://bonsai.ims.u-tokyo.ac.jp/~tamada/bayesnet/.

• An interactive gene network for securin-separase, condensin, cohesin, Dis1/Mtc1 and histones constructed by mass transformation.

  Authors: Tatsuro Yuasa, Takeshi Hayashi, Nobuyasu Ikai, Toshiaki Katayama, Keita Aoki, Takayuki Obara, Yusuke Toyoda, Takeshi Maruyama, Daisuke Kitagawa, Kohta Takahashi, Koji Nagao, Yukinobu Nakaseko, Mitsuhiro Yanagida

  Genes to cells : devoted to molecular & cellular mechanisms. 12/2004; 9(11):1069-82.

  The small genome of fission yeast Schizosaccharomyces pombe contains 4824 predicted genes and gene disruption suggests that approximately 850 are essential for viability. To obtain information onThe small genome of fission yeast Schizosaccharomyces pombe contains 4824 predicted genes and gene disruption suggests that approximately 850 are essential for viability. To obtain information on interactions among genes required for chromosome segregation, an approach called Strategy B was taken using mass transformation of the 1015 temperature-sensitive (ts) mutants that were made by random mutagenesis and transformed by plasmids carrying the genes for securin, separase, condensin, cohesin, kinetochore microtubule-binding proteins Dis1/Mtc1 or histones. Mutant strains whose phenotypes were either suppressed or inhibited by plasmids were selected. Each plasmid interacted positively or negatively with the average 14 strains. Identification of the mutant gene products by cloning revealed many hitherto unknown interactions. The interactive networks of segregation therefore may consist of genes with a variety of functions. For example, separase/Cut1 interacts with Cdc48/p97/VCP, which stabilizes securin and separase. Surprisingly, S. pombe cdc48 mutants displayed the mitotic phenotype highly similar to separase/cut1 mutants. This approach also provides a novel way of mutant isolation, resulting in two histone H2B strains and a cohesion mutant with a new phenotype.

• Genome Informatics 14: 629--630 (2003) 629 BioRuby: Open-Source Bioinformatics Library

  Authors: Mitsuteru C. Nakao, Shiuichi Kawashima, Toshiaki Katayama, Minoru Kanehisa

  07/2004;

  Introduction BioRuby [1] is an open-source project which aims to provide a reusable library for biological tasks for the Ruby language [4]. Ruby is an interpreted object-oriented scripting languageIntroduction BioRuby [1] is an open-source project which aims to provide a reusable library for biological tasks for the Ruby language [4]. Ruby is an interpreted object-oriented scripting language with a simple and powerful syntax and native object-oriented programming support. Ruby is developed by a Japanese author and is now accepted not only by Japanese but also by many professional programmers around the world as a highly productive language. Ruby has many advantageous features to process text files and for system management tasks, which are frequently needed for bioinformatics tools. Compared to other languages, it has native support for object-oriented programming with a simple but powerful syntax, with which we can easily describe and manipulate complicated biological data structures e#ciently. These are the main reason why we decided to implement a bioinformatics library in Ruby, even though BioPerl [2], BioJava, and BioPython were developed previously. BioRuby is available

• Development of Community Annotation Databases for Linking Genomes to Cellular Functions

  Authors: Miho Furumichi, Yoko Sato, Toshiaki Katayama, Shuichi Kawashima, Minoru Kanehisa

  07/2004;

  Introduction The roles of biological databases are changing in two important aspects. First, the data content about molecular structures and molecular functions is certainly not su#cient for theIntroduction The roles of biological databases are changing in two important aspects. First, the data content about molecular structures and molecular functions is certainly not su#cient for the emerging field of systems biology or the Genomes to Life initiatives. It is necessary to somehow capture higher-level structures and functions, such as molecular interaction networks and cellular processes, and create new databases that integrate both molecular and higher-level information. Secondly, although the current form of data submissions by individual authors works well for the sequence and 3D structure databases, it is not appropriate for collecting higher-level biological knowledge from the research community. For the first aspect we have been developing KEGG [2], which integrates cellular functions with genomes and chemistry. For the second aspects we are promoting 'community database' where individual researchers in the community not only obtain information from the database, but a

• Genome Informatics 14: 673--674 (2003) 673 KEGG API: A Web Service Using SOAP/WSDL to

  Authors: Shuichi Kawashima, Toshiaki Katayama, Yoko Sato, Minoru Kanehisa

  07/2004;

  Introduction We have been making the KEGG system available at GenomeNet [2]. KEGG is a suite of databases including GENES, SSDB, PATHWAY, LIGAND, LinkDB, etc. for genome research and related researchIntroduction We have been making the KEGG system available at GenomeNet [2]. KEGG is a suite of databases including GENES, SSDB, PATHWAY, LIGAND, LinkDB, etc. for genome research and related research areas in molecular and cellular biology. These databases and associated computation services are available via WWW and the user interfaces are built on web browsers. Thus, the interfaces are designed to be accessed by humans, not by machines, which means that it is troublesome for the researchers who want to use KEGG in an automated manner. Besides, from the database developer's side, it is impossible to prepare all the CGI programs that satisfy a variety of users' needs. In recent years, the Internet technology for application-to-application communication referred to as the web service is improving at a rapid rate. For example, Google, a popular Internet search engine, provides the web service called the Google Web API [1]. The service enables users to develop software that accesses and

• Genome Informatics 14: 368--369 (2003) Metabolic Pathway Reconstruction for Malaria Parasite

  Authors: Plasmodium Falciparum, Vachiranee Limviphuvadh, Yasushi Okuno, Toshiaki Katayama, Akiyasu C Yoshizawa, Minoru Kanehisa

  07/2004;

  Introduction Human malaria is caused by infection with four species of the intracellular parasitic protozoan genus Plasmodium that are transmitted by Anopheles mosquitoes. Of these four species,Introduction Human malaria is caused by infection with four species of the intracellular parasitic protozoan genus Plasmodium that are transmitted by Anopheles mosquitoes. Of these four species, Plasmodium falciparum is the most lethal. At present, at least 300 million people are a#ected by malaria globally and accounts for 0.7-2.7 million annual deaths. The development of resistance in the parasite to antimalarial drugs, the lack of any licensed malaria vaccine and the fundamental complexity inherent in the malaria parasite, mean there is an urgent need to better understand the function of P. falciparum genes and their biological role to support the development of e#ective antimalarial strategies. P. falciparum genome indicates the presence of 5,432 genes spread across 14 chromosomes, a mitochondrial genome and a circular plastid genome. Notably, more than 60% are hypothetical proteins [1]. This fact emphasizes the need to elucidate gene function by somehow, new strategies. In thi

• Genome Informatics 14: 438--439 (2003) Extraction of Organism Groups from Whole Genome

  Authors: Yoshihiro Yamanishi, Akiyasu C Yoshizawa, Masumi Itoh, Toshiaki Katayama, Minoru Kanehisa

  07/2004;

  this paper is to conduct a whole genome scale phylogenetic analysis in order to extract major organism groups from the comparison of fully sequenced genomes using the independent component analysis,this paper is to conduct a whole genome scale phylogenetic analysis in order to extract major organism groups from the comparison of fully sequenced genomes using the independent component analysis, at the same, to identify genes that are characteristic to a certain organism group. In the experiment, we show that 24 major organism groups are extracted from phylogenetic profiles composed of orthologous gene clusters, and our genome tree corresponds to the simplified classification of KEGG organism groups and suggests that two prokaryotic kingdoms, Archaea and Bacteria, are closer to each other than to Eukaryotes on the whole genome level. We also show a possibility for predicting biological functions of hypothetical genes by examining the correspondence between the extent of their a#liation and specific properties of certain organism groups. 2 Material and Method Ortholog clusters (OCs) are sets of orthologous gene clusters, which are currently developed in the KEGG database [1]. The gene clusters which are identified as cliques in the graph of SSDB (sequence similarity database) are assigned unique identifiers called OC numbers. In this study, we focus on 126 organisms with fully and partially sequenced genomes (including 11 eukaryotes, 99 bacteria and 16 archaea) in the KEGG/GENES and KEGG/SSDB databases as of June 2003. Similarly as phylogenetic profiles, OC profiles are constructed from 17286 OCs (whose gene members exist in at least two organisms) in the 126 organisms. Each OC profile consists of a string of bits, where the presence and absence of an OC are coded as 1 and 0 across organisms