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

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 developed to assist the phylogeneticist user in retrieving individual gene sequence alignments for genes in complete mammalian mitochondrial genomes. Data retrieval in MamMiBase requires three stages. At the first stage, the user must select the mammalian species or group that (s)he wishes to study. In the second stage, the user will select the outgroup from a list that included all species selected in the first stage plus Xenopus laevis and Gallus gallus. Finally, at the third stage, the user will select individual mitochondrial gene alignments or a phylogenetic tree that (s)he wishes to download.

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.

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.

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 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/

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

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.

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.

Genomics Data Analysis Software Market Outlook

As per our latest research, the global genomics data analysis software market size reached USD 1.98 billion in 2024, and is witnessing robust expansion driven by technological advancements and increasing adoption in healthcare and research. The market is expected to grow at a compound annual growth rate (CAGR) of 14.2% from 2025 to 2033, reaching an estimated USD 5.46 billion by 2033. This remarkable growth is fueled by the surge in demand for precision medicine, the proliferation of next-generation sequencing technologies, and the expanding applications of genomics in clinical diagnostics and pharmaceutical research.

The primary growth driver for the genomics data analysis software market is the rapid evolution of sequencing technologies, which has significantly lowered the cost and increased the speed of genomic data generation. With the cost of sequencing a human genome dropping from millions of dollars to just a few hundred dollars, the volume of genomic data being generated globally has skyrocketed. This explosion in data necessitates sophisticated software solutions capable of managing, analyzing, and interpreting massive datasets. As a result, both established and emerging software vendors are investing heavily in the development of advanced analytics platforms, leveraging artificial intelligence and machine learning algorithms to extract actionable insights from complex genomic datasets. These technological advancements are not only enhancing the accuracy of data analysis but are also enabling researchers and clinicians to make more informed decisions, thereby accelerating the adoption of genomics data analysis software across various end-user segments.

Another significant factor propelling market growth is the increasing integration of genomics into clinical workflows, particularly in the realms of precision medicine and drug discovery. Hospitals, clinics, and pharmaceutical companies are increasingly utilizing genomics data analysis software to identify genetic mutations, predict disease susceptibility, and develop targeted therapies. The ability to analyze and interpret vast quantities of genomic information in real time is transforming patient care, allowing for earlier diagnosis, personalized treatment plans, and improved patient outcomes. Furthermore, the growing focus on rare disease research and the expansion of direct-to-consumer genetic testing are broadening the scope of genomics applications, further driving the demand for robust and scalable data analysis platforms.

The genomics data analysis software market is also benefiting from increased funding and strategic collaborations between academic institutions, government agencies, and private sector players. Governments across North America, Europe, and Asia Pacific are launching large-scale genomics initiatives, investing in national genome sequencing projects, and fostering public-private partnerships to accelerate innovation. These efforts are not only expanding the user base for genomics data analysis solutions but are also facilitating the development of interoperable, cloud-based platforms that support collaborative research and data sharing. As the regulatory landscape evolves to accommodate the unique challenges associated with genomic data privacy and security, vendors are increasingly focusing on compliance and data protection features, further enhancing the value proposition of their software offerings.

Gene Expression Analysis plays a pivotal role in the genomics data analysis software market, offering insights into the functional elements of the genome and how they contribute to complex biological processes. This analysis is crucial for understanding the transcriptional activity of genes, which can reveal important information about cellular responses and disease mechanisms. By integrating gene expression data with other genomic information, researchers can identify biomarkers for disease diagnosis and prognosis, as well as potential therapeutic targets. The increasing availability of high-throughput sequencing technologies has made gene expression analysis more accessible, driving demand for sophisticated software tools capable of managing and interpreting large-scale datasets. As a result, the market is witnessing a surge in the development of platforms that offer comprehensive gene expression analysis cap

Quickviews bring together data from several LabKey tables for convenient access, including:

rare_disease_analysis Data for all rare disease participants including: sex, ethnicity, disease recruited for and relationship to proband; latest genome build, QC status of latest genome, path to latest genomes and whether tiering data are available; as well as family selection quality checks for rare disease genomes on GRCh38, reporting abnormalities of the sex chromosomes, family relatedness, Mendelian inconsistencies and reported vs genetic sex summary checks. Please note that only sex checks are unpacked into individual data fields; a final status is shown in the “genetic vs reported results” column.

cancer_analysis Data for all cancer participants whose genomes have been through Genomics England bioinformatics interpretation and passed quality checks, including: sex, ethnicity, disease recruited for and diagnosis; tumour ID, build of latest genome, QC status of latest genome and path to latest genomes; as well file paths to the genomes. This table includes information derived from laboratory_sample and cancer_participant_tumour.

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.

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/

Database for genetic, genomic, phenotype, and disease data generated from rat research. Centralized database that collects, manages, and distributes data generated from rat genetic and genomic research and makes these data available to scientific community. Curation of mapped positions for quantitative trait loci, known mutations and other phenotypic data is provided. Facilitates investigators research efforts by providing tools to search, mine, and analyze this data. Strain reports include description of strain origin, disease, phenotype, genetics, immunology, behavior with links to related genes, QTLs, sub-strains, and strain sources.

Asteraceae, the largest family of angiosperms, has attracted widespread attention for its exceptional medicinal, horticultural, and ornamental value. However, researches on Asteraceae plants face challenges due to their intricate genetic background. With the continuous advancement of sequencing technology, a vast number of genomes and genetic resources from Asteraceae species have been accumulated. This has spurred a demand for comprehensive genomic analysis within this diverse plant group. To meet this need, we developed the Asteraceae Genomics Database (AGD; http://cbcb.cdutcm.edu.cn/AGD/). The AGD serves as a centralized and systematic resource, empowering researchers in various fields such as gene annotation, gene family analysis, evolutionary biology, and genetic breeding. AGD not only encompasses high-quality genomic sequences, and organelle genome data, but also provides a wide range of analytical tools, including BLAST, JBrowse, SSR Finder, HmmSearch, Heatmap, Primer3, PlantiSMASH, and CRISPRCasFinder. These tools enable users to conveniently query, analyze, and compare genomic information across various Asteraceae species. The establishment of AGD holds great significance in advancing Asteraceae genomics, promoting genetic breeding, and safeguarding biodiversity by providing researchers with a comprehensive and user-friendly genomics resource platform.

The National Institute on Aging Genetics of Alzheimer's Disease Data Storage Site (NIAGADS) is a national genetics data repository facilitating access to genotypic and phenotypic data for Alzheimer's disease (AD). Data include GWAS, whole genome (WGS) and whole exome (WES), expression, RNA Seq, and CHIP Seq analyses. Data for the Alzheimer’s Disease Sequencing Project (ADSP) are available through a partnership with dbGaP (ADSP at dbGaP). Results are integrated and annotated in the searchable genomics database that also provides access to a variety of software packages, analytic pipelines, online resources, and web-based tools to facilitate analysis and interpretation of large-scale genomic data. Data are available as defined by the NIA Genomics of Alzheimer’s Disease Sharing Policy and the NIH Genomics Data Sharing Policy. Investigators return secondary analysis data to the database in keeping with the NIAGADS Data Distribution Agreement.

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.

Data views that are common to both the rare disease and the cancer domains. This data pertains to sample handling, genome sequencing, and participant data.

Data Relating to Participants:

• participant: Data on each individual participant in the 100,000 Genomes Project, e.g. personal information (such as relatives or self-reported ethnicity); points of contact with the Project (e.g. handling Genomic Medicine Centre or Trust); and a record of the status of their clinical review.
• death_details: Data on participant deaths submitted by GMCs, likely less complete than the data collected by ONS and NHSE.

Data Relating to Samples:

• clinic_sample: Data describing the taking and handling of participant samples at the Genomic Medicine Centres, i.e. in the clinic, as well as the type of samples obtained. Because of the complexities of handling and managing tumour tissues samples in a clinical setting, there are many fields that are cancer-specific.
• clinic_sample_quality_check_result: Data describing the quality control of obtaining and handling participant samples at the Genomic Medicine Centres, i.e. in the clinic.
• laboratory_sample: Data describing the handling of samples at the biorepository and in preparation for sequencing, as well as the type of sample.
• plated_sample: Data describing the handling and QC of samples at Illumina (the sequencing provider).
• laboratory_sample_omics_availability: Availability of samples collected from participants in the 100,000 Genomes Project for the purpose of omics research. Data includes: Participant ID, Sample Type (e.g. Serum, RNA Blood), the number of aliquots of that sample type for that participant, and the availability status - whether the sample has already been used for a research project. Research proposals for the use of these samples can be submitted, via the GECIP team, to the Scientific Advisory Committee and Access Review Committee.