Manually curated database of all conditions with known genetic causes, focusing on medically significant genetic data with available interventions. Includes gene symbol, conditions, allelic conditions, inheritance, age in which interventions are indicated, clinical categorization, and general description of interventions/rationale. Contents are intended to describe types of interventions that might be considered. Includes only single gene alterations and does not include genetic associations or susceptibility factors related to more complex diseases.

Collection of curated structural variation in the human genome. Catalogue of human genomic structural variation identified in healthy control samples for studies aiming to correlate genomic variation with phenotypic data. It is continuously updated with new data from peer reviewed research studies. The Database is no longer accepting direct submission of data as they are currently part of a collaboration with two new archival CNV databases at EBI and NCBI, called DGVa and dbVAR, respectively. One of the changes to DGV as part of this collaborative effort is that they will no longer be accepting direct submissions, but rather obtain the datasets from DGVa (short for DGV archive). This will ensure that the three databases are synchronized, and will allow for an official accessioning of variants.

Not only is cacao the basic ingredient in the world’s favorite confection, chocolate, but it provides a livelihood for over 6.5 million farmers in Africa, South America and Asia and ranks as one of the top ten agriculture commodities in the world. Historically, cocoa production has been plagued by serious losses due to pests and diseases. The release of the cacao genome sequence will provide researchers with access to the latest genomic tools, enabling more efficient research and accelerating the breeding process, thereby expediting the release of superior cacao cultivars. The sequenced genotype, Matina 1-6, is representative of the genetic background most commonly found in the cacao producing countries, enabling results to be applied immediately and broadly to current commercial cultivars. Matina 1-6 is highly homozygous which greatly reduces the complexity of the sequence assembly process. While the sequence provided is a preliminary release, it already covers 92% of the genome, with approximately 35,000 genes. We will continue to refine the assembly and annotation, working toward a complete finished sequence. Updates will be made available via the main project website. Resources in this dataset:Resource Title: Cacao Genome Database. File Name: Web Page, url: http://www.cacaogenomedb.org/

The Rat Genome Database (RGD) is a collaborative effort between leading research institutions involved in rat genetic and genomic research to collect, consolidate, and integrate data generated from ongoing rat genetic and genomic research efforts and make these data widely available to the scientific community.

THIS RESOURCE IS NO LONGER IN SERVICE, documented August 29, 2016. Database containing structural annotations for the proteomes of just under 100 organisms. Using data derived from public databases of translated genomic sequences, representatives from the major branches of Life are included: Prokaryota, Eukaryota and Archaea. The annotations stored in the database may be accessed in a number of ways. The help page provides information on how to access the database. 3D-GENOMICS is now part of a larger project, called e-Protein. The project brings together similar databases at three sites: Imperial College London , University College London and the European Bioinformatics Institute . e-Protein''s mission statement is To provide a fully automated distributed pipeline for large-scale structural and functional annotation of all major proteomes via the use of cutting-edge computer GRID technologies. The following databases are incorporated: NRprot, SCOP, ASTRAL, PFAM, Prosite, taxonomy, COG The following eukaryotic genomes are incorporated: Anopheles gambiae, protein sequences from the mosquito genome; Arabidopsis thaliana, protein sequences from the Arabidopsis genome; Caenorhabditis briggsae, protein sequences from the C.briggsae genome; Caenorhabditis elegans protein sequences from the worm genome; Ciona intestinalis protein sequences from the sea squirt genome; Danio rerio protein sequences from the zebrafish genome; Drosophila melanogaster protein sequences from the fruitfly genome; Encephalitozoon cuniculi protein sequences from the E.cuniculi genome; Fugu rubripes protein sequences from the pufferfish genome; Guillardia theta protein sequences from the G.theta genome; Homo sapiens protein sequences from the human genome; Mus musculus protein sequences from the mouse genome; Neurospora crassa protein sequences from the N.crassa genome; Oryza sativa protein sequences from the rice genome; Plasmodium falciparum protein sequences from the P.falciparum genome; Rattus norvegicus protein sequences from the rat genome; Saccharomyces cerevisiae protein sequences from the yeast genome; Schizosaccharomyces pombe protein sequences from the yeast genome

Community model organism database for laboratory mouse and authoritative source for phenotype and functional annotations of mouse genes. MGD includes complete catalog of mouse genes and genome features with integrated access to genetic, genomic and phenotypic information, all serving to further the use of the mouse as a model system for studying human biology and disease. MGD is a major component of the Mouse Genome Informatics.Contains standardized descriptions of mouse phenotypes, associations between mouse models and human genetic diseases, extensive integration of DNA and protein sequence data, normalized representation of genome and genome variant information. Data are obtained and integrated via manual curation of the biomedical literature, direct contributions from individual investigators and downloads from major informatics resource centers. MGD collaborates with the bioinformatics community on the development and use of biomedical ontologies such as the Gene Ontology (GO) and the Mammalian Phenotype (MP) Ontology.

CottonGen (https://www.cottongen.org) is a curated and integrated web-based relational database providing access to publicly available genomic, genetic and breeding data to enable basic, translational and applied research in cotton. Built using the open-source Tripal database infrastructure, CottonGen supersedes CottonDB and the Cotton Marker Database, which includes sequences, genetic and physical maps, genotypic and phenotypic markers and polymorphisms, quantitative trait loci (QTLs), pathogens, germplasm collections and trait evaluations, pedigrees, and relevant bibliographic citations, with enhanced tools for easier data sharing, mining, visualization, and data retrieval of cotton research data. CottonGen contains annotated whole genome sequences, unigenes from expressed sequence tags (ESTs), markers, trait loci, genetic maps, genes, taxonomy, germplasm, publications and communication resources for the cotton community. Annotated whole genome sequences of Gossypium raimondii are available with aligned genetic markers and transcripts. These whole genome data can be accessed through genome pages, search tools and GBrowse, a popular genome browser. Most of the published cotton genetic maps can be viewed and compared using CMap, a comparative map viewer, and are searchable via map search tools. Search tools also exist for markers, quantitative trait loci (QTLs), germplasm, publications and trait evaluation data. CottonGen also provides online analysis tools such as NCBI BLAST and Batch BLAST. This project is funded/supported by Cotton Incorporated, the USDA-ARS Crop Germplasm Research Unit at College Station, TX, the Southern Association of Agricultural Experiment Station Directors, Bayer CropScience, Corteva/Agriscience, Dow/Phytogen, Monsanto, Washington State University, and NRSP10. Resources in this dataset:Resource Title: Website Pointer for CottonGen. File Name: Web Page, url: https://www.cottongen.org/ Genomic, Genetic and Breeding Resources for Cotton Research Discovery and Crop Improvement organized by : Species (Gossypium arboreum, barbadense, herbaceum, hirsutum, raimondii, others), Data (Contributors, Download, Submission, Community Projects, Archives, Cotton Trait Ontology, Nomenclatures, and links to Variety Testing Data and NCBISRA Datasets), Search options (Colleague, Genes and Transcripts, Genotype, Germplasm, Map, Markers, Publications, QTLs, Sequences, Trait Evaluation, MegaSearch), Tools (BIMS, BLAST+, CottonCyc, JBrowse, Map Viewer, Primer3, Sequence Retrieval, Synteny Viewer), International Cotton Genome Initiative (ICGI), and Help sources (User manual, FAQs). Also provides Quick Start links for Major Species and Tools.

NOTE: This dataset is no longer publicly available. This database houses over 500,000 sequences that were generated and assembled into approximately 15,000 contigs, annotated and functionally mapped to Gene Ontology (GO) terms. Blueberry (Vaccinium corymbosum) is a major berry crop in the United States. Next generation sequencing methodologies, such as 454, have been demonstrated to be successful and efficient in producing a snap-shot of transcriptional activities during an organism’s developmental stage(s) or its response to biotic or abiotic stresses. Such application of this new sequencing technique allows for high-throughput, genome-wide experimental verification of known and novel transcripts. We have applied a high-throughput pyrosequencing technology (454 EST sequencing) for transcriptome profiling of blueberry during different stages of fruit development to gain an understanding of the genes that are up or down regulated during this process. We have also sequenced flower buds at four different stages of cold acclimation to gain a better understanding of the genes and biochemical pathways that are up- or down-regulated during cold acclimation, since extreme low temperatures are known to reduce crop yield and cause major losses to US farmers. We have also sequenced a leaf sample to compare its transcriptome profile with that of bud and fruit samples. Over 500,000 sequences were generated and assembled into approximately 15,000 contigs and were annotated and functionally mapped to Gene Ontology (GO) terms. A database was developed to house these sequences and their annotations. A web based interface was also developed to allow collaborators to search\browse the data and aid in the analysis and interpretation of the data. The availability of these sequences will allow for future advances, such as the development of a blueberry microarray to study gene expression, and will aid in the blueberry genome sequencing effort that is underway. This work was supported by grant 2008-51180-04861 from the USDA - Cooperative State Research, Education, and Extension Service (CSREES) Specialty Crop Research Initiative program.

SoyBase is a repository for genetics, genomics and related data resources for soybean. It contains current genetic, physical and genomic sequence maps integrated with qualitative and quantitative traits. SoyBase database was established in the 1990s as the USDA Soybean Genetics Database. Originally, it contained only genetic information about soybeans such as genetic maps and information about the Mendelian genetics of soybean. In time SoyBase was expanded to include molecular data regarding soybean genes and sequences as they became available. In 2010, the soybean genome sequence was published and it and supporting gene sequences have been integrated into the SoyBase sequence browser. SoyBase genetic maps were used in the assembly of both the Williams 82 2010 assembly (Wm82.a1.v1) and the newest genome assembly (Wm82.a2.v1). SoyBase also incorporates information about mutant and other soybean genetic stocks and serves as a contact point for ordering strains from those populations. As association analyses continue due to various re-sequencing efforts SoyBase will also incorporate those data into the soybean genome browser as they become available. Gene expression patterns are also available at SoyBase through the SoyBase expression pages and the Soybean Gene Atlas. Other expression/transcriptome/methylomic data sets also have been and continue to be incorporated into the SoyBase genome browser. Project No:3625-21000-062-00D Accession No: 0425040 Resources in this dataset:Resource Title: SoyBase, the USDA-ARS soybean genetics and genomics database web site. File Name: Web Page, url: https://soybase.org SoyBase database was established in the 1990s as the USDA Soybean Genetics Database. Originally, it contained only genetic information about soybeans such as genetic maps and information about the Mendelian genetics of soybean. In time SoyBase was expanded to include molecular data regarding soybean genes and sequences as they became available. In 2010, the soybean genome sequence was published and it and supporting gene sequences have been integrated into the SoyBase sequence browser. SoyBase genetic maps were used in the assembly of both the Williams 82 2010 assembly (Wm82.a1.v1) and the newest genome assembly (Wm82.a2.v1).

Soybean Pods and Seeds SoyBase also incorporates information about mutant and other soybean genetic stocks and serves as a contact point for ordering strains from those populations. As association analyses continue due to various re-sequencing efforts SoyBase will also incorporate those data into the soybean genome browser as they become available. Gene expression patterns are also available at SoyBase through the SoyBase expression pages and the Soybean Gene Atlas. Other expression/transcriptome/methylomic data sets also have been and continue to be incorporated into the SoyBase genome browser.

Database and integrated tools to improve annotation of the bovine genome and to integrate the genome sequence with other genomics data.

This dataset is supplemental to the article "BBGD: an online database for blueberry genomic data," (2007); it is titled "list of genes printed on microarray slides." The article, "BBGD: an online database for blueberry genomic data," (2007) involving blueberry cold hardiness experiments has a list of all the genes that were printed on microarray slides. This dataset, supplemental to the article, is called: "list of genes printed on microarray slides." 1471-2229-7-5-s1.xls 663k. By using the BBGD database, researchers developed EST-based markers for mapping, and have identified a number of "candidate" cold tolerance genes that are highly expressed in blueberry flower buds after exposure to low temperatures.

BBGD (http://bioinformatics.towson.edu/BBGD/) is a public online database, and was developed for blueberry genomics. BBGD is both a sequence and gene expression database: it stores both EST and microarray data, and allows scientists to correlate expression profiles with gene function. Presently, the main focus of the database is the identification of genes in blueberry that are significantly induced or suppressed after low temperature exposure. Data was collected sometime between 2000 and 2007 - exact dates are unknown. Resources in this dataset:Resource Title: List of genes printed on microarray slides, 1471-2229-7-5-s1.xls. File Name: 1471-2229-7-5-s1.xlsResource Title: Data dictionary. File Name: BBGD-data-dictionary.csvResource Description: Defines fields for list of genes.

Blockchain Wildlife Disease Genomic Database Market Outlook

According to our latest research, the global Blockchain Wildlife Disease Genomic Database market size reached USD 312.4 million in 2024, reflecting a robust adoption trajectory. The market is anticipated to grow at a CAGR of 18.7% from 2025 to 2033, reaching an estimated USD 1,612.7 million by the end of the forecast period. This remarkable growth is driven by increasing investments in wildlife disease surveillance, the urgent need for advanced genomic data management solutions, and the rising prevalence of zoonotic diseases. As per the latest research, the integration of blockchain technology with genomic databases is revolutionizing the way wildlife disease data is stored, shared, and analyzed, providing a secure, transparent, and immutable platform for stakeholders across the globe.

A primary growth factor for the Blockchain Wildlife Disease Genomic Database market is the escalating incidence of zoonotic outbreaks, which has heightened global awareness regarding the importance of robust disease monitoring and prevention systems. The COVID-19 pandemic underscored the need for real-time, transparent, and secure data sharing among research institutes, government bodies, and conservation organizations. Blockchain technology enables the creation of immutable records, ensuring data integrity and traceability throughout the genomic data lifecycle. This is particularly crucial for wildlife disease surveillance, where rapid response and accurate information can mean the difference between containment and widespread transmission. As a result, there has been a surge in government funding and international collaborations aimed at deploying blockchain-based genomic databases for wildlife disease management.

Another significant driver is the advancement in genomic sequencing technologies, which has led to an exponential increase in the volume and complexity of genomic data generated from wildlife samples. Traditional data management systems struggle to handle the scale, security, and interoperability requirements of such datasets. Blockchain-based solutions address these challenges by offering decentralized, tamper-proof storage and streamlined access controls, fostering greater collaboration among stakeholders. Furthermore, the integration of artificial intelligence and machine learning with blockchain platforms is enhancing data analytics capabilities, enabling predictive disease modeling and more effective conservation strategies. These technological advancements are propelling the adoption of blockchain genomic databases across diverse application areas, from research and development to on-the-ground conservation efforts.

The market is also benefiting from a growing emphasis on biodiversity conservation and the implementation of regulatory frameworks mandating transparent data practices. Conservation organizations and veterinary hospitals are increasingly leveraging blockchain wildlife disease genomic databases to monitor endangered species, track disease outbreaks, and inform policy decisions. The ability to securely share sensitive genomic data across borders without compromising privacy or data ownership is a key advantage driving market expansion. Additionally, public-private partnerships and cross-sector collaborations are accelerating the deployment of blockchain solutions, particularly in regions with rich biodiversity and high disease risk. These trends underscore the vital role of blockchain technology in supporting global wildlife health and ecosystem sustainability.

Regionally, North America and Europe are leading the adoption of blockchain wildlife disease genomic databases, driven by strong research infrastructure, government initiatives, and a high level of technological maturity. The Asia Pacific region is emerging as a high-growth market, fueled by increasing investments in wildlife conservation, rapid digital transformation, and the presence of biodiversity hotspots. Latin America and the Middle East & Africa are also witnessing growing interest, supported by international funding and collaborative projects aimed at improving disease surveillance capabilities. This regional diversification is creating new opportunities for market players to expand their footprint and address the unique challenges faced by different geographies.

description:

BBGD (http://bioinformatics.towson.edu/BBGD/) was developed as a database for blueberry genomics. BBGD is both a sequence and gene expression database. It stores both EST and microarray data and allows scientists to correlate expression profiles with gene function. BBGD is a public online database. "Presently, the main focus of the database is the identification of genes in blueberry that are significantly induced or suppressed after low temperature exposure. "

To gain a better understanding of changes in gene expression associated with cold acclimation in blueberry, the Rowland laboratory (USDA-ARS, Beltsville, MD) has undertaken a genomics approach based on the analysis of Expressed Sequence Tags (ESTs). Initially, two standard cDNA libraries were constructed using RNA from cold-acclimated and non-acclimated floral buds of the blueberry cultivar Bluecrop (Vaccinium corymbosum L.) and about 1200 5-end ESTs were generated from each of the libraries. About 100 3-end ESTs were generated from the cold-acclimated library as well.

The Blueberry EST database contains EST sequences from a number blueberry libraries including cold acclimated and non-acclimated libraries. It also includes forward and reverse subtractive libraries.

You can query the sequence database by clone ID, accession number or gene (clone) name below. Or you can get a list (in tabular) format of all the clones in a particular library by clicking on the library name on the left side navigation bar.

Attribution for photo: D2601-1 - Blueberry plant: Copyright free, public domain photo by Mark Ehlenfeldt

BBGD (http://bioinformatics.towson.edu/BBGD/) was developed as a database for blueberry genomics. BBGD is both a sequence and gene expression database. It stores both EST and microarray data and allows scientists to correlate expression profiles with gene function. BBGD is a public online database. "Presently, the main focus of the database is the identification of genes in blueberry that are significantly induced or suppressed after low temperature exposure. "

To gain a better understanding of changes in gene expression associated with cold acclimation in blueberry, the Rowland laboratory (USDA-ARS, Beltsville, MD) has undertaken a genomics approach based on the analysis of Expressed Sequence Tags (ESTs). Initially, two standard cDNA libraries were constructed using RNA from cold-acclimated and non-acclimated floral buds of the blueberry cultivar Bluecrop (Vaccinium corymbosum L.) and about 1200 5-end ESTs were generated from each of the libraries. About 100 3-end ESTs were generated from the cold-acclimated library as well.

The Blueberry EST database contains EST sequences from a number blueberry libraries including cold acclimated and non-acclimated libraries. It also includes forward and reverse subtractive libraries.

You can query the sequence database by clone ID, accession number or gene (clone) name below. Or you can get a list (in tabular) format of all the clones in a particular library by clicking on the library name on the left side navigation bar.

Attribution for photo: D2601-1 - Blueberry plant: Copyright free, public domain photo by Mark Ehlenfeldt

THIS RESOURCE IS NO LONGER IN SERVICE, documented August 22, 2016. A database of information on bacterial phages. It contains multiple phage genomes, which users can BLAST and MegaBLAST, and also hosts a Phage Forum in which users can discuss phage data. Interactive browsing of completed phage genomes is available using the program. The browser allows users to scan the genome for particular features and to download sequence information plus analyses of those features. Views of the genome are generated showing named genes BLAST similarities to other phages predicted tRNAs and other sequence features.

The Oomycete Genomics Database is a publicly accessible resource that includes functional assays and expression data, combined with transcript and genomic analysis and annotation. OGD builds upon data available from the Phytophthora Genome Consortium, Syngenta Phytophthora Consortium and the Phytophthora Functional Genomics Database. Data are analyzed and annotated using NCGR''s XGI System. The knowledge gained from these studies provide significant insight into key molecular processes regulating an economically important pathosystem and will provide novel tools for improvement of disease resistance in crop plants.

The Candida Genome Database (CGD) provides access to genomic sequence data and manually curated functional information about genes and proteins of the human pathogen Candida albicans. It collects gene names and aliases, and assigns gene ontology terms to describe the molecular function, biological process, and subcellular localization of gene products.

MaizeMine is the data mining resource of the Maize Genetics and Genome Database (MaizeGDB; http://maizemine.maizegdb.org). It enables researchers to create and export customized annotation datasets that can be merged with their own research data for use in downstream analyses. MaizeMine uses the InterMine data warehousing system to integrate genomic sequences and gene annotations from the Zea mays B73 RefGen_v3 and B73 RefGen_v4 genome assemblies, Gene Ontology annotations, single nucleotide polymorphisms, protein annotations, homologs, pathways, and precomputed gene expression levels based on RNA-seq data from the Z. mays B73 Gene Expression Atlas. MaizeMine also provides database cross references between genes of alternative gene sets from Gramene and NCBI RefSeq. MaizeMine includes several search tools, including a keyword search, built-in template queries with intuitive search menus, and a QueryBuilder tool for creating custom queries. The Genomic Regions search tool executes queries based on lists of genome coordinates, and supports both the B73 RefGen_v3 and B73 RefGen_v4 assemblies. The List tool allows you to upload identifiers to create custom lists, perform set operations such as unions and intersections, and execute template queries with lists. When used with gene identifiers, the List tool automatically provides gene set enrichment for Gene Ontology (GO) and pathways, with a choice of statistical parameters and background gene sets. With the ability to save query outputs as lists that can be input to new queries, MaizeMine provides limitless possibilities for data integration and meta-analysis.

PeanutBase (peanutbase.org) is the primary genetics and genomics database for cultivated peanut and its wild relatives. It houses information about genome sequences, genes and predicted functions, genetic maps, markers, links to germplasm resources, and maps of peanut germplasm origins. This resource is being developed for U.S. and International peanut researchers and breeders, with support from The Peanut Foundation and the many contributors that have made the Peanut Genomics Initiative possible. Funded by The Peanut Foundation as part of the Peanut Genomics Initiative. Additional support from USDA-ARS. Database developed and hosted by the USDA-ARS SoyBase and Legume Clade Database group at Ames, IA, with NCGR and other participants. Resources in this dataset:Resource Title: PeanutBase.org. File Name: Web Page, url: https://peanutbase.org Website pointer for PeanutBase.org - Genetic and genomic data to enable more rapid crop improvement in peanuts. The peanut genome has been sequenced and analyzed as part of the International Peanut Genomic Initiative, in order to accelerate breeding progress and get more productive, disease-resistant, stress-tolerant varieties to farmers. The two diploid progenitors have been sequenced and are available, along with predicted genes and descriptions. The genomes of the diploid progenitors will be used to help identify and assemble the similar chromosomes in cultivated peanut. Cultivated peanut, Arachis hypogaea, is an allotetraploid (2n=4x=40) that contains two complete genomes, labeled the A and B genomes. A. duranensis (2n=2x=20) has likely contributed the A genome, and A. ipaensis has likely contributed the B genome. It may be helpful to remember these two associations by using the mnemonic: "A" comes before "B" and "duranensis" comes before "ipaensis". Because of the difficulty of assembly a tetraploid genome, the two diploids, A. duranensis and A. ipaensis, have been sequenced and assembled first. Together these provide a good initial basis for the tetraploid genome. Additionally, the two will help guide assembly of the tetraploid genome. Sequencing work on the tetraploid genome is underway; stay tuned for updates in 2015.

BeetleBase is a comprehensive sequence database and community resource for Tribolium genetics, genomics and developmental biology. It incorporates information about genes, mutants, genetic markers, expressed sequence tags and publications.

MaizeGDB is a community-oriented, long-term, federally funded informatics service to researchers focused on the crop plant and model organism Zea mays. Genomic, genetic, sequence, germplasm, gene product, metabolic pathways, functional characterization, literature reference, diversity, and expression are among the datatypes stored at MaizeGDB. At the project's website are custom interfaces enabling researchers to browse data and to seek out specific information matching explicit search criteria. First released in 1991 with the name MaizeDB, the Maize Genetics and Genomics Database, now MaizeGDB (since 2003), is funded, developed, and hosted by the USDA-ARS located at Ames, Iowa. Resources in this dataset:Resource Title: MaizeGDB, the community database for maize genetics and genomics.. File Name: Web Page, url: https://maizegdb.org/ MaizeGDB is a community-oriented, long-term, federally funded informatics service to researchers focused on the crop plant and model organism Zea mays. Established as a USDA-ARS resource in 2003, MaizeGDB supplies data and resources related to maize. The types of data include genomic, genetic, sequence, germplasm, gene product, metabolic pathways, functional characterization, literature reference, diversity, and expression.