Plant Metabolic Pathways in MetaCyc and SolCyc

MetaCyc is a metabolic encyclopedia of experimentally validated biochemical pathways curated from scientific literature, that spans all organisms, with an emphasis on plants and microbes. The Pathway tools is a complex curation software suite that enables curation of reactions, construction of pathways and annotation with one or more representative enzymes, that include information such as substrate specificity, kinetic properties, activators, inhibitors, cofactor requirements, genes if cloned and links to external databases. In addition curators are able to provide concise, review-level summaries and extensive literature citations. The present database release includes more than 1200 pathways from more than 1549 organisms, 7312 reactions, 5127 enzymes, 4748 genes, 7234 chemical compounds, curated from 17916 citations. The MetaCyc database is the reference database on which the pathways are predicted from annotated genomes by PathoLogic called Pathway/genome Databases (PGDB's). The Biocyc Database ("biocyc.org":http://biocyc.org) has a collection over 300 PGDB's. Each BioCyc Database describes the genome and predicted metabolic pathways of a single organism, which are then taken up by interested groups for curation. SolCyc is one such PGDB, developed for the clade oriented Solanceae Genomics Network (SGN) database. It has predicted metabolic pathway databases of significant species belonging to Solanaceae and includes Lycocyc(tomato), Solacyc (eggplant), Nicotianacyc (tobacco),Petuniacyc (Petunia), Capcyc (Capsicum) , Potatocyc (potato). An interactive webinterface has been developed for the seamless flow of information from the SGN phenotype and locus database with SolCyc. This facilitates researchers with the capacity to search for underlying metabolic pathway information of genes and phenotypes that has been curated into the SolCyc database

The SOL Genomics Network Model: Making Community Annotation Work

The concept of community annotation is a growing discipline for achieving participation of the research community in depositing up‐to‐date knowledge in biological databases.
The Solanaceae Genomics Network ("SGN":http://sgn.cornell.edu/) is a clade‐oriented database (COD) focusing on plants of the nightshade family, including tomato, potato, pepper, eggplant, and tobacco, and is one of the bioinformatics nodes of the international tomato genome sequencing project. One of our major efforts is linking Solanaceae phenotype information with the underlying genes, and subsequently the genome. As part of this goal, SGN has introduced a database for locus names and descriptors, and a database for phenotypes of natural and induced variation. These two databases have web interfaces that allow cross references, associations with tomato gene models, and in‐house curated information of sequences, literature, ontologies, gene networks, and the Solanaceae biochemical pathways database ("SolCyc":http://solcyc.sgn.cornell.edu). All of our curator tools are open for online community annotation, through specially assigned “submitter” accounts. 

Currently the community database consists of 5,548 phenotyped accessions, and 5,739 curated loci, out of which more than 300 loci where contributed or annotated by 66 active submitters, creating a database that is truly community driven.
This framework is easily adaptable for other projects working on other taxa (for example see "http://chlamybase.org":http://chlamybase.org), greatly expanding the application of this user‐friendly online annotation system. Community participation is fostered by an active outreach program that includes contacting potential submitters via emails, at meetings and conferences, and by promoting featured user submitted annotations on the SGN homepage. The source code and database schema for all SGN functionalities are freely available. Please contact SGN at "sgn‐feedback[at]sgn.cornell.edu":mailto:[email protected] for more information

The Plant Ontology Database: a community resource for plant structure and developmental stages controlled vocabulary and annotations

The Plant Ontology Consortium (POC, http://www.plantontology.org) is a collaborative effort among model plant genome databases and plant researchers that aims to create, maintain and facilitate the use of a controlled vocabulary (ontology) for plants. The ontology allows users to ascribe attributes of plant structure (anatomy and morphology) and developmental stages to data types, such as genes and phenotypes, to provide a semantic framework to make meaningful cross-species and database comparisons. The POC builds upon groundbreaking work by the Gene Ontology Consortium (GOC) by adopting and extending the GOC's principles, existing software and database structure. Over the past year, POC has added hundreds of ontology terms to associate with thousands of genes and gene products from Arabidopsis, rice and maize, which are available through a newly updated web-based browser (http://www.plantontology.org/amigo/go.cgi) for viewing, searching and querying. The Consortium has also implemented new functionalities to facilitate the application of PO in genomic research and updated the website to keep the contents current. In this report, we present a brief description of resources available from the website, changes to the interfaces, data updates, community activities and future enhancement

The Plant Ontology Database: a community resource for plant structure and developmental stages controlled vocabulary and annotations

The Plant Ontology Consortium (POC, http://www.plantontology.org) is a collaborative effort among model plant genome databases and plant researchers that aims to create, maintain and facilitate the use of a controlled vocabulary (ontology) for plants. The ontology allows users to ascribe attributes of plant structure (anatomy and morphology) and developmental stages to data types, such as genes and phenotypes, to provide a semantic framework to make meaningful cross-species and database comparisons. The POC builds upon groundbreaking work by the Gene Ontology Consortium (GOC) by adopting and extending the GOC's principles, existing software and database structure. Over the past year, POC has added hundreds of ontology terms to associate with thousands of genes and gene products from Arabidopsis, rice and maize, which are available through a newly updated web-based browser (http://www.plantontology.org/amigo/go.cgi) for viewing, searching and querying. The Consortium has also implemented new functionalities to facilitate the application of PO in genomic research and updated the website to keep the contents current. In this report, we present a brief description of resources available from the website, changes to the interfaces, data updates, community activities and future enhancement

An ontology approach to comparative phenomics in plants

BACKGROUND: Plant phenotype datasets include many different types of data, formats, and terms from specialized vocabularies. Because these datasets were designed for different audiences, they frequently contain language and details tailored to investigators with different research objectives and backgrounds. Although phenotype comparisons across datasets have long been possible on a small scale, comprehensive queries and analyses that span a broad set of reference species, research disciplines, and knowledge domains continue to be severely limited by the absence of a common semantic framework. RESULTS: We developed a workflow to curate and standardize existing phenotype datasets for six plant species, encompassing both model species and crop plants with established genetic resources. Our effort focused on mutant phenotypes associated with genes of known sequence in Arabidopsis thaliana (L.) Heynh. (Arabidopsis), Zea mays L. subsp. mays (maize), Medicago truncatula Gaertn. (barrel medic or Medicago), Oryza sativa L. (rice), Glycine max (L.) Merr. (soybean), and Solanum lycopersicum L. (tomato). We applied the same ontologies, annotation standards, formats, and best practices across all six species, thereby ensuring that the shared dataset could be used for cross-species querying and semantic similarity analyses. Curated phenotypes were first converted into a common format using taxonomically broad ontologies such as the Plant Ontology, Gene Ontology, and Phenotype and Trait Ontology. We then compared ontology-based phenotypic descriptions with an existing classification system for plant phenotypes and evaluated our semantic similarity dataset for its ability to enhance predictions of gene families, protein functions, and shared metabolic pathways that underlie informative plant phenotypes. CONCLUSIONS: The use of ontologies, annotation standards, shared formats, and best practices for cross-taxon phenotype data analyses represents a novel approach to plant phenomics that enhances the utility of model genetic organisms and can be readily applied to species with fewer genetic resources and less well-characterized genomes. In addition, these tools should enhance future efforts to explore the relationships among phenotypic similarity, gene function, and sequence similarity in plants, and to make genotype-to-phenotype predictions relevant to plant biology, crop improvement, and potentially even human health.This item is part of the UA Faculty Publications collection. For more information this item or other items in the UA Campus Repository, contact the University of Arizona Libraries at [email protected]

Gramene QTL database: development, content and applications

Gramene is a comparative information resource for plants that integrates data across diverse data domains. In this article, we describe the development of a quantitative trait loci (QTL) database and illustrate how it can be used to facilitate both the forward and reverse genetics research. The QTL database contains the largest online collection of rice QTL data in the world. Using flanking markers as anchors, QTLs originally reported on individual genetic maps have been systematically aligned to the rice sequence where they can be searched as standard genomic features. Researchers can determine whether a QTL co-localizes with other QTLs detected in independent experiments and can combine data from multiple studies to improve the resolution of a QTL position. Candidate genes falling within a QTL interval can be identified and their relationship to particular phenotypes can be inferred based on functional annotations provided by ontology terms. Mutations identified in functional genomics populations and association mapping panels can be aligned with QTL regions to facilitate fine mapping and validation of gene–phenotype associations. By assembling and integrating diverse types of data and information across species and levels of biological complexity, the QTL database enhances the potential to understand and utilize QTL information in biological research

Plant Ontology (PO): a Controlled Vocabulary of Plant Structures and Growth Stages

The Plant Ontology Consortium (POC) (www.plantontology.org) is a collaborative effort among several plant databases and experts in plant systematics, botany and genomics. A primary goal of the POC is to develop simple yet robust and extensible controlled vocabularies that accurately reflect the biology of plant structures and developmental stages. These provide a network of vocabularies linked by relationships (ontology) to facilitate queries that cut across datasets within a database or between multiple databases. The current version of the ontology integrates diverse vocabularies used to describe Arabidopsis, maize and rice (Oryza sp.) anatomy, morphology and growth stages. Using the ontology browser, over 3500 gene annotations from three species-specific databases, The Arabidopsis Information Resource (TAIR) for Arabidopsis, Gramene for rice and MaizeGDB for maize, can now be queried and retrieved

Whole-Plant Growth Stage Ontology for Angiosperms and Its Application in Plant Biology

Plant growth stages are identified as distinct morphological landmarks in a continuous developmental process. The terms describing these developmental stages record the morphological appearance of the plant at a specific point in its life cycle. The widely differing morphology of plant species consequently gave rise to heterogeneous vocabularies describing growth and development. Each species or family specific community developed distinct terminologies for describing whole-plant growth stages. This semantic heterogeneity made it impossible to use growth stage description contained within plant biology databases to make meaningful computational comparisons. The Plant Ontology Consortium (http://www.plantontology.org) was founded to develop standard ontologies describing plant anatomical as well as growth and developmental stages that can be used for annotation of gene expression patterns and phenotypes of all flowering plants. In this article, we describe the development of a generic whole-plant growth stage ontology that describes the spatiotemporal stages of plant growth as a set of landmark events that progress from germination to senescence. This ontology represents a synthesis and integration of terms and concepts from a variety of species-specific vocabularies previously used for describing phenotypes and genomic information. It provides a common platform for annotating gene function and gene expression in relation to the developmental trajectory of a plant described at the organismal level. As proof of concept the Plant Ontology Consortium used the plant ontology growth stage ontology to annotate genes and phenotypes in plants with initial emphasis on those represented in The Arabidopsis Information Resource, Gramene database, and MaizeGDB.This is the publisher’s final pdf. The published article is copyrighted by the American Society of Plant Biologists and can be found at: http://www.plantphysiol.org/

The Plant Ontology Database: a community resource for plant structure and developmental stages controlled vocabulary and annotations

The Plant Ontology Consortium (POC, http://www.plantontology.org) is a collaborative effort among model plant genome databases and plant researchers that aims to create, maintain and facilitate the use of a controlled vocabulary (ontology) for plants. The ontology allows users to ascribe attributes of plant structure (anatomy and morphology) and developmental stages to data types, such as genes and phenotypes, to provide a semantic framework to make meaningful cross-species and database comparisons. The POC builds upon groundbreaking work by the Gene Ontology Consortium (GOC) by adopting and extending the GOC’s principles, existing software and database structure. Over the past year, POC has added hundreds of ontology terms to associate with thousands of genes and gene products from Arabidopsis, rice and maize, which are available through a newly updated web-based browser (http://www.plantontology.org/amigo/go.cgi) for viewing, searching and querying. The Consortium has also implemented new functionalities to facilitate the application of PO in genomic research and updated the website to keep the contents current. In this report, we present a brief description of resources available from the website, changes to the interfaces, data updates, community activities and future enhancement.This is the publisher’s final pdf. The published article is copyrighted by the author(s) and published by Oxford University Press. The published article can be found at: http://nar.oxfordjournals.org/

The MetaCyc database of metabolic pathways and enzymes and the BioCyc collection of pathway/genome databases

The MetaCyc database (MetaCyc.org) is a comprehensive and freely accessible resource for metabolic pathways and enzymes from all domains of life. The pathways in MetaCyc are experimentally determined, small-molecule metabolic pathways and are curated from the primary scientific literature. With more than 1400 pathways, MetaCyc is the largest collection of metabolic pathways currently available. Pathways reactions are linked to one or more well-characterized enzymes, and both pathways and enzymes are annotated with reviews, evidence codes, and literature citations. BioCyc (BioCyc.org) is a collection of more than 500 organism-specific Pathway/Genome Databases (PGDBs). Each BioCyc PGDB contains the full genome and predicted metabolic network of one organism. The network, which is predicted by the Pathway Tools software using MetaCyc as a reference, consists of metabolites, enzymes, reactions and metabolic pathways. BioCyc PGDBs also contain additional features, such as predicted operons, transport systems, and pathway hole-fillers. The BioCyc Web site offers several tools for the analysis of the PGDBs, including Omics Viewers that enable visualization of omics datasets on two different genome-scale diagrams and tools for comparative analysis. The BioCyc PGDBs generated by SRI are offered for adoption by any party interested in curation of metabolic, regulatory, and genome-related information about an organism