BRENDA - The Comprehensive Enzyme Information System
GeneNetwork is a group of linked data sets and tools used to study complex networks of genes, molecules, and higher order gene function and phenotypes. GeneNetwork combines more than 25 years of legacy data generated by hundreds of scientists together with sequence data (SNPs) and massive transcriptome data sets (expression genetic or eQTL data sets). The quantitative trait locus (QTL) mapping module that is built into GN is optimized for fast on-line analysis of traits that are controlled by combinations of gene variants and environmental factors. GeneNetwork can be used to study humans, mice (BXD, AXB, LXS, etc.), rats (HXB), Drosophila, and plant species (barley and Arabidopsis). Most of these population data sets are linked with dense genetic maps (genotypes) that can be used to locate the genetic modifiers that cause differences in expression and phenotypes, including disease susceptibility.
Gramene is a curated, open-source, integrated data resource for comparative functional genomics in crops and model plant species. Our goal is to facilitate the study of cross-species comparisons using information generated from projects supported by public funds. Gramene currently hosts annotated whole genomes in over two dozen plant species and partial assemblies for almost a dozen wild rice species in the Ensembl browser, genetic and physical maps with genes, ESTs and QTLs locations, genetic diversity data sets, structure-function analysis of proteins, plant pathways databases (BioCyc and Plant Reactome platforms), and descriptions of phenotypic traits and mutations.
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A repository for maize mutants utilized by scientists conducting biological research. Order genetic stocks and obtain information about maize mutants.
MaizeGDB is a community-oriented, long-term, federally funded informatics service to researchers focused on the crop plant and model organism Zea mays.
Developed by personnel at the comparative genomics database Gramene and the maize model organism database MaizeGDB in collaboration with the Maize Genome Sequencing Project (MGSC), MaizeCyc is a catalog of known and/or predicted metabolic and transport pathways from maize (Zea mays ssp. mays). Pathways and genes presented in this catalog are based on the electronic and manual annotations of the B73 RefGen_v2 gene models. It includes various sequence-based associations provided by Gramene, MaizeSequence.org, and MaizeGDB to external database entries from EntrezGene, UniProtKB-SwissProt and GenBank. In this round, manual annotations of genes include mapping of classical phenotype genes to sequenced genomic loci provided by Schnable and Freeling, and proteomics-supported gene annotations from Friso et al (2010). The database was created using the Pathway Tools PathoLogic module developed by Peter D. Karp and coworkers at the Bioinformatics Research Group at SRI International.
The aerial surfaces of land plants are protected by unique lipids, which provide a primary line of defense against numerous biological and environmental stresses. The surface lipids on the stigmatic silks of maize are biologically unique because they are rich in hydrocarbons, which are the inert end-point metabolites of the surface lipid network. This project will utilize maize silks as the model biological system to provide a fundamental understanding of this unique, discrete metabolic process by comprehensively dissecting within a single organism the metabolic and genetic networks that produce important surface lipids.
The Panzea Database contains the genotypic and phenotypic data and genetic marker information produced by the Molecular and Functional Diversity in the Maize Genome project. More information about the project can be found on the Project Info page. The Panzea Database design is based on the Genomic Diversity and Phenotype Data Model (GDPDM).
Metabolomic profiling yields a rich resource of information, and one that is often rife with surprises. The overwhelming majority of detected metabolites have yet to be annotated or identified. Metabolomics analysis provides an important resource for establishing functions of genes responsible for metabolism and its regulation, and for modeling metabolic networks. The metabolome enables a deeper understanding of the metabolic potential of a species, and gives investigators the opportunity to uncover new aspects of the synthesis and accumulation of structurally diverse compounds.