Plant and Cell Physiology 2014 Online Database Issue Takeshi Obayashi1 and Kentaro Yano2,* 1
Graduate School of Information Sciences, Tohoku University, Sendai, Japan School of Agriculture, Meiji University, Kawasaki, Japan *Corresponding author: E-mail, [email protected]; Fax, +81-44-934-7046
2
characterized and documented. BrassiBase will close these various gaps and provide the full potential of research focusing on the adaptive characters and character trait evolution in the Brassicaceae. MEGANTE (https://megante.dna.affrc.go.jp/) is a web-based annotation system that makes plant genome annotation easy for non-bioinformaticians (Numa and Itoh 2014; see pp. 1–8 (e2)). Users can submit a sequence of up to 10 Mb in length and save up to 100 sequences on the server. The annotation is visualized with a genome browser and the results can be downloaded in a Microsoft Excel format. Yonemaru et al. (2014; see pp. 1–12 (e9)) introduce the HapRice database, which provides information about single nucleotide polymorphisms (SNPs) in different rice genomes. The SNP haplotypes were determined by the allele frequencies in two populations consisting of 3,334 SNPs within 76 world accessions and 3,252 SNPs within 177 Japanese accessions, and will aid marker-assisted breeding and provide useful additional markers for geneticists. The SNP information is available from the HapRice database (http://qtaro.abr.affrc.go.jp/). To allow a comprehensive understanding of the interactions between an organism’s metabolites and the chemical-level contribution of metabolites to human health, Nakamura et al. (2014; see pp. 1–9 (e7)) constructed a metabolite activity database known as the KNApSAcK Metabolite Activity DB (http://kanaya.naist.jp/MetaboliteActivity/top. jsp). The KNApSAcK Metabolite Activity DB is integrated within the KNApSAcK Family DBs (Afendi et al. 2012, Nakamura et al. 2013) to facilitate further systematized research in various omics fields, especially metabolomics, nutrigenomics and foodomics. The KNApSAcK Metabolite Activity DB could also be utilized for developing novel drugs and materials, as well as for identifying viable drug resources and other useful compounds. On pages 1–9 (e5), Fukami-Kobayashi et al. (2014) also describe the integration of omics information and resources in plant science. The original SABRE (Systematic consolidation of Arabidopsis and other Botanical Resources) database (Yamazaki et al. 2010), retrieved TAIR (The Arabidopsis Information Resource) gene models and their annotations, together with homologous gene clones from various species, and
Downloaded from http://pcp.oxfordjournals.org/ at University of Hawaii - Manoa on June 19, 2015
We are proud to present the fourth Database Issue of Plant and Cell Physiology (PCP) (available online at http://pcp.oxfordjour nals.org/). As we have introduced in previous years (Matsuoka and Yano 2010, Obayashi and Yano 2013), the ultimate goal of PCP’s annual Database Issue is to provide a useful online resource and forum for discussion of bioinformatics research, and in particular the development and maintenance of the infrastructure of web databases for plant science. Recently, largescale biological data sets, such as those for DNA polymorphisms and gene expression, have been obtained using next-generation sequencing technology. In addition, website and database construction has also become easier due to the many commercial and non-commercial types of software that have been recently released and widely distributed. This has resulted in the creation of many diverse web-based tools and databases that house and/or analyze large-scale data. However, it is not always easy for researchers to extract data efficiently from such databases, since their concept, contents and/or functions may sometimes be unclear. Therefore, to promote discussion and develop bioinformatics approaches and omics/knowledge information specifically for the plant sciences, databases ought to be designed with particular specifications in mind, for instance to include user-friendly interfaces, search functions and manuals. To ensure that a common consensus is reached in this and future Database Issues, PCP has updated its Instructions for Authors and implemented standard key features and descriptions of databases and online tools (see http://www.oxfordjournals.org/our_journals/pcp/pcp_2013_ call_for_paper.html), which will aid researchers in extracting relevant data. This year’s collection of Database articles promises to provide useful omics data to plant researchers, and comprise descriptions of nine sophisticated web databases. On pages 1–9 (e3), Kiefer et al. (2014) report on a new Brassica database—BrassiBase (http://brassibase.cos.uni-heidelberg.de/). The authors aim to develop an online database system of cross-referenced information and resources on Brassicaceae taxonomy, systematics, evolution, traits and germplasm resources. Biological material and resources, either collected directly in the wild or held in germplasm collections, are often taxonomically misidentified and are very rarely further
Plant Cell Physiol. 55(1): 1–2 (2014) doi:10.1093/pcp/pct193, available online at www.pcp.oxfordjournals.org ! The Author 2014. Published by Oxford University Press on behalf of Japanese Society of Plant Physiologists. All rights reserved. For permissions, please email: [email protected]
Plant Cell Physiol. 55(1): 1–2 (2014) doi:10.1093/pcp/pct193 ! The Author 2014.
1
2
feedback from PCP readers on how to improve future database issues and eagerly await the submission of more excellent papers describing novel databases, their features and any relevant updates, in time for the next PCP Database Issue, which will be published in early 2015.
References Afendi, F.M., Okada, T., Yamazaki, M., Hirai-Morita, A., Nakamura, Y., Nakamura, K. et al. (2012) KNApSAcK family databases: integrated metabolite–plant species databases for multifaceted plant research. Plant Cell Physiol. 53: e1. Akiyama, K., Kurotani, A., Iida, K., Kuromori, T., Shinozaki, K. and Sakurai, T. (2014) RARGE II: an integrated phenotype database of Arabidopsis mutant traits using a controlled vocabulary. Plant Cell Physiol. 55: e4. Asamizu, E., Ichihara, H., Nakaya, A., Nakamura, Y., Hirakawa, H., Ishii, T. et al. (2014) Plant Genome DataBase Japan (PGDBj): a portal website for the integration of plant genome-related databases. Plant Cell Physiol 55: e8. Fukami-Kobayashi, K., Nakamura, Y., Tamura, T. and Kobayashi, M. (2014) SABRE2: a database connecting plant EST/full-length cDNA clones with Arabidopsis information. Plant Cell Physiol 55: e5. Kiefer, M., Schmickl, R., German, D.A., Manda´kova´, T., Lysak, M.A., AlShehbaz, I.A. et al. (2014) BrassiBase: introduction to a novel knowledge database on Brassicaeae evolution. Plant Cell Physiol. 55: e3. Mano, S., Miwa, T., Nishikawa, S., Mimura, T. and Nishimura, M. (2011) Plant Organelles Database 2 (PODB2). Plant Cell Physiol. 52: 244–253. Mano, S., Nakamura, T., Kondo, M., Miwa, T., Nishikawa, S.-i., Mimura, T. et al. (2014) The Plant Organelles Database 3 (PODB3) update 2014: integrating electron micrographs and new options for plant organelle research. Plant Cell Physiol. 55: e1. Matsuoka, M. and Yano, K. (2010) Editorial. Plant Cell Physiol. 51: 1247. Nakamura, K., Shimura, N., Otabe, Y., Hirai-Morita, A., Nakamura, Y. and Ono, N. (2013) KNApSAcK-3D: a three-dimensional structure database of plant metabolites. Plant Cell Physiol. 54: e4. Nakamura, Y., Mochamad Afendi, F., Kawsar Parvin, A., Ono, N., Tanaka, K., Hirai Morita, A. et al. (2014) KNApSAcKMetabolite activity database for retrieving the relationships between metabolites and biological activities. Plant Cell Physiol. 55: e7. Numa, H. and Itoh, T. (2014) MEGANTE: a web-based system for integrated plant genome annotation. Plant Cell Physiol. 55: e2. Obayashi, T., Okamura, Y., Ito, S., Tadaka, S., Aoki, Y., Shirota, M. et al. (2014) ATTED-II in 2014: evaluation of gene coexpression in agriculturally important plants. Plant Cell Physiol 55: e6. Obayashi, T. and Yano, K. (2013) The 2013 Plant and Cell Physiology Database Issue. Plant Cell Physiol 54: 169–170. Yamazaki, Y., Akashi, R., Banno, Y., Endo, T., Ezura, H., FukamiKobayashi, K. et al. (2010) NBRP databases: databases of biological resources in Japan. Nucleic Acids Res. 38: D26–D32. Yonemaru, J.-i., Ebana, K. and Yano, M. (2014) HapRice, a SNP haplotype database and a web tool for rice. Plant Cell Physiol. 55: e9.
Plant Cell Physiol. 55(1): 1–2 (2014) doi:10.1093/pcp/pct193 ! The Author 2014.
Downloaded from http://pcp.oxfordjournals.org/ at University of Hawaii - Manoa on June 19, 2015
thus facilitated using TAIR annotations of Arabidopsis genes for research on homologous genes from other model plants. SABRE has recently been upgraded to version SABRE2 (http://sabre. epd.brc.riken.jp/SABRE2.html), which now stores information on >1.5 million plant expressed sequence tag (EST)/cDNA clones of the National BioResource Project (NBRP) of Japan. All clones are actual experimental resources from 14 plant species, including the major crops such as barley, soybean, tomato and wheat, and are available from the core facilities at the NBRP. Asamizu et al. (2014; see pp. 1–7 (e8)) introduce an integrated database, the Plant Genome DataBase Japan (PGDBj), which provides a comprehensive coverage of plant genomerelated databases in Japan (http://pgfbj.jp). The PGDBj comprises three component databases (DBs): Ortholog DB, Plant Resource DB and DNA Marker DB. Users are able to locate their desired information from the component DBs as well as various types of linked external DBs by performing searches though a cross-search window. Also included in this issue are three papers describing recent updates to already existing, publically available databases. Mano et al. (2011) developed the Plant Organelles Database 2 (PODB2), a specialized database to facilitate plant organellar research. The original database contained image and movie data and protocols, and has now been updated to version PODB3 (http://podb.nibb.ac.jp/Organellome/), to deal also with electron micrographs and information on organelle dynamics in response to external stimuli (Mano et al. 2014). In addition, the user interface for access and the redesign of pages have been enhanced in PODB3. All improvements and additional novel features of PODB3 with respect to version PODB2 are described fully in the article (see pp. 1–9 (e1)). Obayashi et al. (2014; see pp. 1–7 (e6)) report coexpression data updates in ATTED-II (http://atted.jp), which now includes information from seven plant species. RNaseq-based coexpression data are also available for Arabidopsis, which covers 94% of Arabidopsis protein-coding genes. The new coexpression data were thoroughly evaluated in multiple ways to support their adequate use in agriculturally important plants. On pages 1–10 (e4), Akiyama et al. (2014) introduce the database RARGE II (http://rarge.psc.riken.jp/), which integrates phenotypic information on 66,209 Arabidopsis loss- and gainof-function mutants by mapping the descriptions onto Plant Ontology (PO) and Phenotypic Quality Ontology (PATO) terms. This approach renders it possible to manage the different phenotype databases as one large data set. The above-mentioned articles represent valuable additions to PCP’s Database Collection (http://www.oxfordjournals.org/ our_journals/pcp/2012_pcp_database.html), and will undoubtedly serve as useful tools to facilitate a wide range of plant science-oriented research activities. We welcome any
Graduate School of Information Sciences, Tohoku University, Sendai, Japan School of Agriculture, Meiji University, Kawasaki, Japan *Corresponding author: E-mail, [email protected]; Fax, +81-44-934-7046
2
characterized and documented. BrassiBase will close these various gaps and provide the full potential of research focusing on the adaptive characters and character trait evolution in the Brassicaceae. MEGANTE (https://megante.dna.affrc.go.jp/) is a web-based annotation system that makes plant genome annotation easy for non-bioinformaticians (Numa and Itoh 2014; see pp. 1–8 (e2)). Users can submit a sequence of up to 10 Mb in length and save up to 100 sequences on the server. The annotation is visualized with a genome browser and the results can be downloaded in a Microsoft Excel format. Yonemaru et al. (2014; see pp. 1–12 (e9)) introduce the HapRice database, which provides information about single nucleotide polymorphisms (SNPs) in different rice genomes. The SNP haplotypes were determined by the allele frequencies in two populations consisting of 3,334 SNPs within 76 world accessions and 3,252 SNPs within 177 Japanese accessions, and will aid marker-assisted breeding and provide useful additional markers for geneticists. The SNP information is available from the HapRice database (http://qtaro.abr.affrc.go.jp/). To allow a comprehensive understanding of the interactions between an organism’s metabolites and the chemical-level contribution of metabolites to human health, Nakamura et al. (2014; see pp. 1–9 (e7)) constructed a metabolite activity database known as the KNApSAcK Metabolite Activity DB (http://kanaya.naist.jp/MetaboliteActivity/top. jsp). The KNApSAcK Metabolite Activity DB is integrated within the KNApSAcK Family DBs (Afendi et al. 2012, Nakamura et al. 2013) to facilitate further systematized research in various omics fields, especially metabolomics, nutrigenomics and foodomics. The KNApSAcK Metabolite Activity DB could also be utilized for developing novel drugs and materials, as well as for identifying viable drug resources and other useful compounds. On pages 1–9 (e5), Fukami-Kobayashi et al. (2014) also describe the integration of omics information and resources in plant science. The original SABRE (Systematic consolidation of Arabidopsis and other Botanical Resources) database (Yamazaki et al. 2010), retrieved TAIR (The Arabidopsis Information Resource) gene models and their annotations, together with homologous gene clones from various species, and
Downloaded from http://pcp.oxfordjournals.org/ at University of Hawaii - Manoa on June 19, 2015
We are proud to present the fourth Database Issue of Plant and Cell Physiology (PCP) (available online at http://pcp.oxfordjour nals.org/). As we have introduced in previous years (Matsuoka and Yano 2010, Obayashi and Yano 2013), the ultimate goal of PCP’s annual Database Issue is to provide a useful online resource and forum for discussion of bioinformatics research, and in particular the development and maintenance of the infrastructure of web databases for plant science. Recently, largescale biological data sets, such as those for DNA polymorphisms and gene expression, have been obtained using next-generation sequencing technology. In addition, website and database construction has also become easier due to the many commercial and non-commercial types of software that have been recently released and widely distributed. This has resulted in the creation of many diverse web-based tools and databases that house and/or analyze large-scale data. However, it is not always easy for researchers to extract data efficiently from such databases, since their concept, contents and/or functions may sometimes be unclear. Therefore, to promote discussion and develop bioinformatics approaches and omics/knowledge information specifically for the plant sciences, databases ought to be designed with particular specifications in mind, for instance to include user-friendly interfaces, search functions and manuals. To ensure that a common consensus is reached in this and future Database Issues, PCP has updated its Instructions for Authors and implemented standard key features and descriptions of databases and online tools (see http://www.oxfordjournals.org/our_journals/pcp/pcp_2013_ call_for_paper.html), which will aid researchers in extracting relevant data. This year’s collection of Database articles promises to provide useful omics data to plant researchers, and comprise descriptions of nine sophisticated web databases. On pages 1–9 (e3), Kiefer et al. (2014) report on a new Brassica database—BrassiBase (http://brassibase.cos.uni-heidelberg.de/). The authors aim to develop an online database system of cross-referenced information and resources on Brassicaceae taxonomy, systematics, evolution, traits and germplasm resources. Biological material and resources, either collected directly in the wild or held in germplasm collections, are often taxonomically misidentified and are very rarely further
Plant Cell Physiol. 55(1): 1–2 (2014) doi:10.1093/pcp/pct193, available online at www.pcp.oxfordjournals.org ! The Author 2014. Published by Oxford University Press on behalf of Japanese Society of Plant Physiologists. All rights reserved. For permissions, please email: [email protected]
Plant Cell Physiol. 55(1): 1–2 (2014) doi:10.1093/pcp/pct193 ! The Author 2014.
1
2
feedback from PCP readers on how to improve future database issues and eagerly await the submission of more excellent papers describing novel databases, their features and any relevant updates, in time for the next PCP Database Issue, which will be published in early 2015.
References Afendi, F.M., Okada, T., Yamazaki, M., Hirai-Morita, A., Nakamura, Y., Nakamura, K. et al. (2012) KNApSAcK family databases: integrated metabolite–plant species databases for multifaceted plant research. Plant Cell Physiol. 53: e1. Akiyama, K., Kurotani, A., Iida, K., Kuromori, T., Shinozaki, K. and Sakurai, T. (2014) RARGE II: an integrated phenotype database of Arabidopsis mutant traits using a controlled vocabulary. Plant Cell Physiol. 55: e4. Asamizu, E., Ichihara, H., Nakaya, A., Nakamura, Y., Hirakawa, H., Ishii, T. et al. (2014) Plant Genome DataBase Japan (PGDBj): a portal website for the integration of plant genome-related databases. Plant Cell Physiol 55: e8. Fukami-Kobayashi, K., Nakamura, Y., Tamura, T. and Kobayashi, M. (2014) SABRE2: a database connecting plant EST/full-length cDNA clones with Arabidopsis information. Plant Cell Physiol 55: e5. Kiefer, M., Schmickl, R., German, D.A., Manda´kova´, T., Lysak, M.A., AlShehbaz, I.A. et al. (2014) BrassiBase: introduction to a novel knowledge database on Brassicaeae evolution. Plant Cell Physiol. 55: e3. Mano, S., Miwa, T., Nishikawa, S., Mimura, T. and Nishimura, M. (2011) Plant Organelles Database 2 (PODB2). Plant Cell Physiol. 52: 244–253. Mano, S., Nakamura, T., Kondo, M., Miwa, T., Nishikawa, S.-i., Mimura, T. et al. (2014) The Plant Organelles Database 3 (PODB3) update 2014: integrating electron micrographs and new options for plant organelle research. Plant Cell Physiol. 55: e1. Matsuoka, M. and Yano, K. (2010) Editorial. Plant Cell Physiol. 51: 1247. Nakamura, K., Shimura, N., Otabe, Y., Hirai-Morita, A., Nakamura, Y. and Ono, N. (2013) KNApSAcK-3D: a three-dimensional structure database of plant metabolites. Plant Cell Physiol. 54: e4. Nakamura, Y., Mochamad Afendi, F., Kawsar Parvin, A., Ono, N., Tanaka, K., Hirai Morita, A. et al. (2014) KNApSAcKMetabolite activity database for retrieving the relationships between metabolites and biological activities. Plant Cell Physiol. 55: e7. Numa, H. and Itoh, T. (2014) MEGANTE: a web-based system for integrated plant genome annotation. Plant Cell Physiol. 55: e2. Obayashi, T., Okamura, Y., Ito, S., Tadaka, S., Aoki, Y., Shirota, M. et al. (2014) ATTED-II in 2014: evaluation of gene coexpression in agriculturally important plants. Plant Cell Physiol 55: e6. Obayashi, T. and Yano, K. (2013) The 2013 Plant and Cell Physiology Database Issue. Plant Cell Physiol 54: 169–170. Yamazaki, Y., Akashi, R., Banno, Y., Endo, T., Ezura, H., FukamiKobayashi, K. et al. (2010) NBRP databases: databases of biological resources in Japan. Nucleic Acids Res. 38: D26–D32. Yonemaru, J.-i., Ebana, K. and Yano, M. (2014) HapRice, a SNP haplotype database and a web tool for rice. Plant Cell Physiol. 55: e9.
Plant Cell Physiol. 55(1): 1–2 (2014) doi:10.1093/pcp/pct193 ! The Author 2014.
Downloaded from http://pcp.oxfordjournals.org/ at University of Hawaii - Manoa on June 19, 2015
thus facilitated using TAIR annotations of Arabidopsis genes for research on homologous genes from other model plants. SABRE has recently been upgraded to version SABRE2 (http://sabre. epd.brc.riken.jp/SABRE2.html), which now stores information on >1.5 million plant expressed sequence tag (EST)/cDNA clones of the National BioResource Project (NBRP) of Japan. All clones are actual experimental resources from 14 plant species, including the major crops such as barley, soybean, tomato and wheat, and are available from the core facilities at the NBRP. Asamizu et al. (2014; see pp. 1–7 (e8)) introduce an integrated database, the Plant Genome DataBase Japan (PGDBj), which provides a comprehensive coverage of plant genomerelated databases in Japan (http://pgfbj.jp). The PGDBj comprises three component databases (DBs): Ortholog DB, Plant Resource DB and DNA Marker DB. Users are able to locate their desired information from the component DBs as well as various types of linked external DBs by performing searches though a cross-search window. Also included in this issue are three papers describing recent updates to already existing, publically available databases. Mano et al. (2011) developed the Plant Organelles Database 2 (PODB2), a specialized database to facilitate plant organellar research. The original database contained image and movie data and protocols, and has now been updated to version PODB3 (http://podb.nibb.ac.jp/Organellome/), to deal also with electron micrographs and information on organelle dynamics in response to external stimuli (Mano et al. 2014). In addition, the user interface for access and the redesign of pages have been enhanced in PODB3. All improvements and additional novel features of PODB3 with respect to version PODB2 are described fully in the article (see pp. 1–9 (e1)). Obayashi et al. (2014; see pp. 1–7 (e6)) report coexpression data updates in ATTED-II (http://atted.jp), which now includes information from seven plant species. RNaseq-based coexpression data are also available for Arabidopsis, which covers 94% of Arabidopsis protein-coding genes. The new coexpression data were thoroughly evaluated in multiple ways to support their adequate use in agriculturally important plants. On pages 1–10 (e4), Akiyama et al. (2014) introduce the database RARGE II (http://rarge.psc.riken.jp/), which integrates phenotypic information on 66,209 Arabidopsis loss- and gainof-function mutants by mapping the descriptions onto Plant Ontology (PO) and Phenotypic Quality Ontology (PATO) terms. This approach renders it possible to manage the different phenotype databases as one large data set. The above-mentioned articles represent valuable additions to PCP’s Database Collection (http://www.oxfordjournals.org/ our_journals/pcp/2012_pcp_database.html), and will undoubtedly serve as useful tools to facilitate a wide range of plant science-oriented research activities. We welcome any
