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

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.

The Sol Genomics Network (SGN) is a clade-oriented database dedicated to the biology of the Solanaceae family which includes a large number of closely related and many agronomically important species such as tomato, potato, tobacco, eggplant, pepper, and the ornamental Petunia hybrida. SGN is part of the International Solanaceae Initiative (SOL), which has the long-term goal of creating a network of resources and information to address key questions in plant adaptation and diversification. A key problem of the post-genomic era is the linking of the phenome to the genome, and SGN allows to track and help discover new such linkages. Data: Solanaceae and other Genomes SGN is a home for Solanaceae and closely related genomes, such as selected Rubiaceae genomes (e.g., Coffea). The tomato, potato, pepper, and eggplant genome are examples of genomes that are currently available. If you would like to include a Solanaceae genome that you sequenced in SGN, please contact us. ESTs SGN houses EST collections for tomato, potato, pepper, eggplant and petunia and corresponding unigene builds. EST sequence data and cDNA clone resources greatly facilitate cloning strategies based on sequence similarity, the study of syntenic relationships between species in comparative mapping projects, and are essential for microarray technology. Unigenes SGN assembles and publishes unigene builds from these EST sequences. For more information, see Unigene Methods. Maps and Markers SGN has genetic maps and a searchable catalog of markers for tomato, potato, pepper, and eggplant. Tools SGN makes available a wide range of web-based bioinformatics tools for use by anyone, listed here. Some of our most popular tools include BLAST searches, the SolCyc biochemical pathways database, a CAPS experiment designer, an Alignment Analyzer and browser for phylogenetic trees. The VIGS tool can help predict the properties of VIGS (Viral Induced Gene Silencing) constructs. The data in SGN have been submitted by many different research groups around the world. A web form is available to submit data for display on SGN. SGN community-driven gene and phenotype database: Simple web interfaces have been developed for the SGN user-community to submit, annotate, and curate the Solanaceae locus and phenotype databases. The goal is to share biological information, and have the experts in their field review existing data and submit information about their favorite genes and phenotypes. Resources in this dataset:Resource Title: Website Pointer to Sol Genomics Network. File Name: Web Page, url: https://solgenomics.net/ Specialized Search interfaces are provided for: Organisms/Taxon; Genes and Loci; Genomic sequences and annotations; QTLs, Mutants & Accessions, Traits; Transcripts: Unigenes, ESTs, & Libraries; Unigene families; Markers; Genomic clones; Images; Expression: Templates, Experiments, Platforms; Traits.

The primary mission of the Alliance of Genome Resources (the Alliance) is to develop and maintain sustainable genome information resources that facilitate the use of diverse model organisms in understanding the genetic and genomic basis of human biology, health and disease. This understanding is fundamental for advancing genome biology research and for translating human genome data into clinical utility. The unified Alliance information system will represent the union of the data and information represented in the current individual MODs rather than the intersection, and thus provide the best of each in one place while maintaining community integrity and preserving the unique aspects of each model organism. By working together we can be more comprehensive and efficient, and hence more sustainable. Through the implementation of a shared, modular information system architecture, the Alliance seeks to serve diverse user communities including (i) human geneticists who want access to all model organism data for orthologous human genes; (ii) basic science researchers who use specific model organisms to understand fundamental biology; (iii) computational biologists and data scientists who need access to standardized, well-structured data, both big and small; and (iv) educators and students. Community genome resources such as the Model Organism Databases and the Gene Ontology Consortium have developed high quality resources enabling cost and time effective information retrieval and aggregation that would otherwise require countless hours to achieve. Regardless of their success and utility, there remain challenges to using and sustaining MODs. Searching across multiple model organism database resources remains a barrier to realizing the full impact of these resources in advancing genome biology and genomic medicine. In addition, despite a growing need for MODs by the biomedical research community as well as the increasing volumes of data and publications, the financial resources available to sustain MODs and related information resources are being reduced. We believe that one contribution to solving these challenges while continuing to serve our diverse user communities is to unify our efforts. To this end, six MODs (Saccharomyces Genome Database, WormBase, FlyBase, Zebrafish Information Network, Mouse Genome Database, Rat Genome Database) and the Gene Ontology (GO) project joined together in the fall of 2016 to form the Alliance of Genome Resources (the Alliance) consortium. Resources in this dataset:Resource Title: Alliance of Genome Resources. File Name: Web Page, url: https://www.alliancegenome.org/

List of all genomes and accession numbers used for this analysis.

N/A  =  Not Applicable, x  =  Present, -  =  Absent *SNPedia only provides an API to webpages of individual SNPs, not access to genetic data of individuals.

Genetic Variant Databases Market Outlook

According to our latest research, the global genetic variant databases market size in 2024 stood at USD 1.72 billion, reflecting robust expansion driven by the integration of genomics in clinical and research settings. The market is experiencing a strong compound annual growth rate (CAGR) of 12.4% from 2025 to 2033. By the end of 2033, the market is forecasted to reach a value of USD 4.92 billion. This growth is primarily propelled by the rising adoption of precision medicine, increasing investments in genomics research, and the critical need for comprehensive data repositories to facilitate genetic variant interpretation and clinical decision-making.

The surge in demand for genetic variant databases is fundamentally driven by the rapid advancements in next-generation sequencing (NGS) technologies and the exponential increase in genomic data generation. As sequencing costs continue to fall, more healthcare institutions, research centers, and pharmaceutical companies are leveraging genetic data to uncover disease associations, inform drug development, and personalize patient care. The proliferation of genome-wide association studies (GWAS), coupled with the growing utility of multi-omics approaches, has intensified the necessity for robust, scalable, and interoperable databases that can store, curate, and analyze vast volumes of genetic variants. These databases not only enable efficient data sharing and collaboration across the scientific community but also underpin the development of AI-driven diagnostic and therapeutic solutions, further propelling market growth.

Another significant growth factor for the genetic variant databases market is the increasing emphasis on clinical diagnostics and the integration of genomic data into routine healthcare. The expanding role of genomics in identifying hereditary diseases, predicting disease risk, and tailoring treatment regimens has heightened the demand for accurate, up-to-date, and clinically relevant variant databases. Regulatory initiatives and guidelines, such as those from the American College of Medical Genetics and Genomics (ACMG), mandate the use of curated variant databases to ensure standardized variant interpretation and reporting. Moreover, the rise in rare disease diagnosis, oncology genomics, and pharmacogenomics has amplified the requirement for disease-specific and locus-specific databases, supporting clinicians in making evidence-based decisions and improving patient outcomes.

The market is also benefiting from increased collaboration between public and private stakeholders, fostering the development of integrated and population-scale genetic variant databases. Governments and international consortia are investing heavily in national genomics initiatives, biobanks, and open-access repositories to enhance data accessibility and support large-scale research endeavors. The emergence of cloud-based platforms and AI-powered data analytics is further streamlining data integration, interpretation, and sharing, thereby accelerating discoveries in genomics and translational medicine. However, the market faces challenges related to data privacy, security, and standardization, which necessitate ongoing innovation and regulatory oversight to ensure ethical and responsible data utilization.

From a regional perspective, North America continues to dominate the genetic variant databases market, owing to its advanced healthcare infrastructure, substantial R&D investments, and the presence of leading genomics companies and academic institutions. Europe follows closely, driven by robust government initiatives and collaborative research frameworks. The Asia Pacific region is witnessing the fastest growth, fueled by increasing adoption of genomics in healthcare, expanding biopharmaceutical sectors, and supportive government policies in countries like China, Japan, and India. Latin America and the Middle East & Africa are gradually emerging as promising markets, supported by growing awareness and investments in precision medicine and genomics research.

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.

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.

Genomic data

Comprehensive collection of high quality microbial genomics reference data for bacteria, viruses, and fungi in holdings of American Type Culture Collection.

MBGD is a database for comparative analysis of completely sequenced microbial genomes, the number of which is now growing rapidly. The aim of MBGD is to facilitate comparative genomics from various points of view such as ortholog identification, paralog clustering, motif analysis and gene order comparison. The heart of MBGD function is to create orthologous or homologous gene cluster table. For this purpose, similarities between all genes are precomputed and stored into the database, in addition to the annotations of genes such as function categories that were assigned by the original authors and motifs that were found in the translated sequence. Using these homology data, MBGD dynamically creates orthologous gene cluster table. Users can change a set of organisms or cutoff parameters to create their own orthologous grouping. Based on this cluster table, users can further analyze multiple genomes from various points of view with the functions such as global map comparison, local map comparison, multiple sequence alignment and phylogenetic tree construction.

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

Species detection using eDNA is revolutionizing the global capacity to monitor biodiversity. However, the lack of regional, vouchered, genomic sequence information—especially sequence information that includes intraspecific variation—creates a bottleneck for management agencies wanting to harness the complete power of eDNA to monitor taxa and implement eDNA analyses. eDNA studies depend upon regional databases of complete mitogenomic sequence information to evaluate the effectiveness of such data to differentiate, identify and detect taxa. We created the Oregon Biodiversity Genome Project working group to utilize recent advances in sequencing technology to create a database of complete, near error-free mitogenomic sequences for all of Oregon's resident freshwater fishes. So far, we have successfully assembled the complete mitogenomes of 313 specimens of freshwater fish representing 7 families, 55 genera, and 129 (88%) of the 146 resident species and lineages. Our comparative analyses of...

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.

Genetic Variant Databases Market Outlook

According to our latest research, the global genetic variant databases market size reached USD 1.45 billion in 2024, reflecting robust growth as advanced genomics and precision medicine initiatives accelerate worldwide. The market is projected to expand at a CAGR of 10.3% during the forecast period, reaching a value of approximately USD 3.51 billion by 2033. This impressive growth is driven by the increasing integration of genetic information into clinical practice, the rising prevalence of genetic disorders, and the growing demand for personalized therapies across healthcare and pharmaceutical sectors.

One of the primary growth factors propelling the genetic variant databases market is the exponential increase in genetic sequencing data generated globally. Advances in next-generation sequencing (NGS) technologies have significantly reduced the cost and time required to sequence entire genomes, leading to an unprecedented accumulation of genetic information. This surge in data necessitates robust, scalable, and accessible databases to catalog, annotate, and interpret genetic variants. As healthcare providers, researchers, and pharmaceutical companies increasingly rely on genetic data to inform diagnostics, treatment decisions, and drug discovery, the demand for comprehensive and interoperable genetic variant databases is expected to rise sharply. The integration of artificial intelligence and machine learning tools further enhances the utility of these databases by enabling high-throughput analysis, variant prioritization, and clinical interpretation, thereby accelerating the pace of genomic medicine.

Another significant driver for the genetic variant databases market is the expanding landscape of precision medicine and population genomics initiatives. Governments and private organizations worldwide are investing heavily in large-scale genomic projects, such as the UK Biobank, the All of Us Research Program in the United States, and the GenomeAsia 100K initiative. These projects aim to collect and analyze genetic data from diverse populations, fueling the need for databases that can handle population-specific and disease-specific variant information. Such initiatives not only enhance the understanding of genetic diversity and disease mechanisms but also support the development of targeted therapies and personalized interventions. As the global healthcare ecosystem shifts towards more individualized approaches, the role of genetic variant databases in supporting clinical diagnostics, risk assessment, and therapeutic decision-making becomes increasingly indispensable.

The market is also benefiting from the growing collaboration between academic institutions, healthcare providers, and the life sciences industry. Strategic partnerships are being forged to create, curate, and share genetic variant data on a global scale, breaking down traditional silos and fostering data interoperability. The adoption of standardized formats and ontologies, such as those promoted by the Global Alliance for Genomics and Health (GA4GH), is facilitating the seamless exchange of genetic information across platforms and borders. Additionally, regulatory agencies are providing clearer guidelines for the use and sharing of genetic data, further supporting market growth. However, challenges related to data privacy, security, and ethical considerations remain critical, necessitating ongoing investment in robust governance frameworks and secure data management solutions.

From a regional perspective, North America currently holds the largest share of the genetic variant databases market, driven by its advanced healthcare infrastructure, strong research ecosystem, and early adoption of genomic medicine. Europe follows closely, benefiting from well-established genomic initiatives and supportive regulatory environments. The Asia Pacific region is emerging as a high-growth market, fueled by increasing genomic research investments, rising awareness of genetic testing, and expanding healthcare access. Latin America and the Middle East & Africa, while currently representing smaller market shares, are witnessing growing interest in genomic technologies and are expected to contribute to future market expansion as infrastructure and expertise develop further.

Database Type Analysis

The database type segment of the genetic variant databases market is diverse, encompassing germline variant databases, somat

DoTS (Database Of Transcribed Sequences) is a human and mouse transcript index created from all publicly available transcript sequences. The input sequences are clustered and assembled to form the DoTS Consensus Transcripts that comprise the index. These transcripts are assigned stable identifiers of the form DT.123456 (and are often referred to as dots). The transcripts are in turn clustered to form putative DoTS Genes. These are assigned stable identifiers of the form DG.1234356. As of September 1, 2004, the DoTS annotation team has manually annotated 43,164 human and 78,054 mouse DoTS Transcripts (DTs), corresponding to 3,939 human and 7,752 mouse DoTS Genes (DGs). Use the manually annotated gene query to see the DoTS Transcripts that have been manually annotated. The focus of the DoTS project is integrating the various types of data (e.g., EST sequences, genomic sequence, expression data, functional annotation) in a structured manner which facilitates sophisticated queries that are otherwise not easy to perform. DoTS is built on the GUS Platform which includes a relational database that uses controlled vocabularies and ontologies to ensure that biologically meaningful queries can be posed in a uniform fashion. An easy way to start using the site is to search for DoTS Transcripts using an existing cDNA or mRNA sequence. Click on the BLAST tab at the top of the page and enter your sequence in the form provided. All the transcripts with significant sequence similarity to your query sequence will be displayed. Or use one of the provided queries to retrieve transcripts using a number of criteria. These queries are listed on the query page, which can also be reached by clicking on the tab marked query at the top of the page. Finally, the boolean query page allows these queries to be combined in a variety of ways. Sponsors: Funding provided by -NIH grant RO1-HG-01539-03 -DOE grant DE-FG02-00ER62893

Database facilitating information integration and mining within the pig and across species of all genomics / genetics research results accumulated over the years including pig gene expression, quantitative trait loci (QTL), candidate gene, and whole genome association study (WGAS) results. The key functions developed so far include pig gene pages (a centralized gene search tool), a local copy of Biomart (for customizable genome information queries), genome feature alignment tools (Pig QTLdb and Gbrowse), integrated gene expression information (ANEXDB and ESTdb), a dedicated pig genome and gene set BLAST server, and virtual comparative map database and tools (VCmap). By developing the PGD, it is our aim to collaboratively utilize existing databases and tools via networked functions, such as web services, database API, etc., to maximize the potential of all related databases through the PGD implementation.

The proportion of sites that is polymorphic in each category using SNPs where at least one derived allele is reported (Derived Allele Count (DAC) > = 1) and those SNPs where at least six derived alleles are reported (DAC > = 6).

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.