Saccharomyces cerevisiae is a species of yeast. The species has been instrumental in winemaking, baking, and brewing since ancient times. It is believed to have been originally isolated from the skin of grapes. It is one of the most intensively studied eukaryotic model organisms in molecular and cell biology, much like Escherichia coli as the model bacterium. It is the microorganism which causes many common types of fermentation. S. cerevisiae cells are round to ovoid, 5–10 μm in diameter. It reproduces by budding.
The Gene Ontology (GO) is a major bioinformatics initiative to unify the representation of gene and gene product attributes across all species. More specifically, the project aims to: 1) maintain and develop its controlled vocabulary of gene and gene product attributes; 2) annotate genes and gene products, and assimilate and disseminate annotation data; and 3) provide tools for easy access to all aspects of the data provided by the project, and to enable functional interpretation of experimental data using the GO, for example via enrichment analysis. GO is part of a larger classification effort, the Open Biomedical Ontologies, being one of the Initial Candidate Members of the OBO Foundry.
BRENDA is the world's most comprehensive online database for functional, biochemical and molecular biological data on enzymes, metabolites and metabolic pathways. It contains data on the properties, function and significance of all enzymes classified by the Enzyme Commission of the International Union of Biochemistry and Molecular Biology (IUBMB). As ELIXIR Core Data Resource and Global Core Biodata Resource, BRENDA is considered a data resource of critical importance to the international life sciences research community. The database compiles a representative overview of enzymes and metabolites using current research data from primary scientific literature and thus serves the purpose of facilitating information retrieval for researchers. BRENDA is subject to the terms of the Creative Commons license, is accessible worldwide and can be used free of charge. As one of the digital resources of the Leibniz Institute DSMZ-German Collection of Microorganisms and Cell Cultures, BRENDA is part of the integrated biodata infrastructure DSMZ Digital Diversity.
The Rat Genome Database (RGD) is a database of rat genomics, genetics, physiology and functional data, as well as data for comparative genomics between rat, human and mouse. RGD is responsible for attaching biological information to the rat genome via structured vocabulary, or ontology, annotations assigned to genes and quantitative trait loci (QTL), and for consolidating rat strain data and making it available to the research community. They are also developing a suite of tools for mining and analyzing genomic, physiologic and functional data for the rat, and comparative data for rat, mouse, human, and five other species.
The Biological General Repository for Interaction Datasets (BioGRID) is a curated biological database of protein-protein interactions, genetic interactions, chemical interactions, and post-translational modifications created in 2003 (originally referred to as simply the General Repository for Interaction Datasets by Mike Tyers, Bobby-Joe Breitkreutz, and Chris Stark at the Lunenfeld-Tanenbaum Research Institute at Mount Sinai Hospital. It strives to provide a comprehensive curated resource for all major model organism species while attempting to remove redundancy to create a single mapping of data. Users of The BioGRID can search for their protein, chemical or publication of interest and retrieve annotation, as well as curated data as reported, by the primary literature and compiled by in house large-scale curation efforts. The BioGRID is hosted in Toronto, Ontario, Canada and Dallas, Texas, United States and is partnered with the Saccharomyces Genome Database, FlyBase, WormBase, PomBase, and the Alliance of Genome Resources. The BioGRID is funded by the NIH and CIHR. BioGRID is an observer member of the International Molecular Exchange Consortium.
The Generic Model Organism Database (GMOD) project provides biological research communities with a toolkit of open-source software components for visualizing, annotating, managing, and storing biological data. The GMOD project is funded by the United States National Institutes of Health, National Science Foundation and the USDA Agricultural Research Service.
The Pathogen-Host Interactions database (PHI-base) is a biological database that contains manually curated information on genes experimentally proven to affect the outcome of pathogen-host interactions. The database has been maintained by researchers at Rothamsted Research and external collaborators since 2005. PHI-base has been part of the UK node of ELIXIR, the European life-science infrastructure for biological information, since 2016.
The BioCyc database collection is an assortment of organism specific Pathway/Genome Databases (PGDBs) that provide reference to genome and metabolic pathway information for thousands of organisms. As of July 2023, there were over 20,040 databases within BioCyc. SRI International, based in Menlo Park, California, maintains the BioCyc database family.
MicrobesOnline is a publicly and freely accessible website that hosts multiple comparative genomic tools for comparing microbial species at the genomic, transcriptomic and functional levels. MicrobesOnline was developed by the Virtual Institute for Microbial Stress and Survival, which is based at the Lawrence Berkeley National Laboratory in Berkeley, California. The site was launched in 2005, with regular updates until 2011.
SUPERFAMILY is a database and search platform of structural and functional annotation for all proteins and genomes. It classifies amino acid sequences into known structural domains, especially into SCOP superfamilies. Domains are functional, structural, and evolutionary units that form proteins. Domains of common Ancestry are grouped into superfamilies. The domains and domain superfamilies are defined and described in SCOP. Superfamilies are groups of proteins which have structural evidence to support a common evolutionary ancestor but may not have detectable sequence homology.
Protein function prediction methods are techniques that bioinformatics researchers use to assign biological or biochemical roles to proteins. These proteins are usually ones that are poorly studied or predicted based on genomic sequence data. These predictions are often driven by data-intensive computational procedures. Information may come from nucleic acid sequence homology, gene expression profiles, protein domain structures, text mining of publications, phylogenetic profiles, phenotypic profiles, and protein-protein interaction. Protein function is a broad term: the roles of proteins range from catalysis of biochemical reactions to transport to signal transduction, and a single protein may play a role in multiple processes or cellular pathways.
YEASTRACT is a curated repository of more than 48000 regulatory associations between transcription factors (TF) and target genes in Saccharomyces cerevisiae, based on more than 1200 bibliographic references. It also includes the description of about 300 specific DNA binding sites for more than a hundred characterized TFs. Further information about each Yeast gene has been extracted from the Saccharomyces Genome Database (SGD). For each gene the associated Gene Ontology (GO) terms and their hierarchy in GO was obtained from the GO consortium. Currently, YEASTRACT maintains more than 7100 terms from GO. The nucleotide sequences of the promoter and coding regions for Yeast genes were obtained from Regulatory Sequence Analysis Tools (RSAT). All the information in YEASTRACT is updated regularly to match the latest data from SGD, GO consortium, RSA Tools and recent literature on yeast regulatory networks.
In bioinformatics, the PANTHER classification system is a large curated biological database of gene/protein families and their functionally related subfamilies that can be used to classify and identify the function of gene products. PANTHER is part of the Gene Ontology Reference Genome Project designed to classify proteins and their genes for high-throughput analysis.
In bioinformatics, a Gene Disease Database is a systematized collection of data, typically structured to model aspects of reality, in a way to comprehend the underlying mechanisms of complex diseases, by understanding multiple composite interactions between phenotype-genotype relationships and gene-disease mechanisms. Gene Disease Databases integrate human gene-disease associations from various expert curated databases and text mining derived associations including Mendelian, complex and environmental diseases.
PomBase is a model organism database that provides online access to the fission yeast Schizosaccharomyces pombe genome sequence and annotated features, together with a wide range of manually curated functional gene-specific data. The PomBase website was redeveloped in 2016 to provide users with a more fully integrated, better-performing service.
Model organism databases (MODs) are biological databases, or knowledgebases, dedicated to the provision of in-depth biological data for intensively studied model organisms. MODs allow researchers to easily find background information on large sets of genes, efficiently plan experiments, integrate their data with existing knowledge, and formulate new hypotheses. They allow users to analyse results and interpret datasets, and the data they generate are increasingly used to describe less well studied species. Where possible, MODs share common approaches to collect and represent biological information. For example, all MODs use the Gene Ontology (GO) to describe functions, processes and cellular locations of specific gene products. Projects also exist to enable software sharing for curation, visualization and querying between different MODs. Organismal diversity and varying user requirements however mean that MODs are often required to customize capture, display, and provision of data.
SoyBase is a database created by the United States Department of Agriculture. It contains genetic information about soybeans. It includes genetic maps, information about Mendelian genetics and molecular data regarding genes and sequences. It was started in 1990 and is freely available to individuals and organizations worldwide.
Canto is a web-based tool to support the curation of gene-specific scientific data, by both professional biocurators and publication authors. Canto was developed as part of the PomBase project, and is funded by the Wellcome Trust.
Biocuration is the field of life sciences dedicated to organizing biomedical data, information and knowledge into structured formats, such as spreadsheets, tables and knowledge graphs. The biocuration of biomedical knowledge is made possible by the cooperative work of biocurators, software developers and bioinformaticians and is at the base of the work of biological databases.
