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.

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.

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.

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/

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 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 dataset comprises of microbial metagenomics sequencing reads of seawater collected across 48 reef sites across the Great Barrier Reef. Samples were collected across four Long Term Monitoring Program (LTMP) field trips between November 2019-July 2020, combining water chemistry data, LTMP field surveys and microbial metagenomics data. This data collection was a major part of the QRCIF IMOS GBR microbial genomic database project, which aims to generate a comprehensive open access repositor of microbial genomic data from across the region. Seawater was collected in quadruplicate either by SCUBA or using Niskin Bottles at each reef site, 5L of seawater was pre-filtered using a 5µm filter and applied to a 0.22µm sterivex filter, snap frozen and stored at -20°C in preparation of DNA extraction. DNA was extracted from sterivex filters using phenol:chloroform:Iso-amyl alcolol extraction, ethanol precipitation and cleanup using the Zymo Clean and Concentrator® kit before submission for sequencing at the Australian Centre for Ecogenomics sequencing facility, Illumina. The data presented as illumina paired-end shotgun metagenomics sequencing runs, in fastq format, generated by Microba Life Sciences, Brisbane, QLD, Australia. Each downloadable archive contains forward and reverse reads for all replicate sampling performed at that particular site. Water quality particulate and dissolved nutrient data was generated as previously described (https://doi.org/10.25845/5c09b551f315b) from water samples collected simultaneously at each reef site.

Zip files are available through the spatial layer under each site's 'illumina.seawater.zip' - please note these are large downloads (between 6 - 14 GB).

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.

In the late 80s, around 0.01 gigabyte (10 megabyte) of genomics data was generated annually. Compared to that, in the late 2010s, the amount of genomics data generated annually was around twenty petabytes (20 million gigabytes).

The Stanley Online Genomics Database uses samples from the Stanley Medical Research Institute (SMRI) Brain Bank. These samples were processed and run on gene expression arrays by a variety of researchers in collaboration with the SMRI. These researchers have performed analyses on their respective studies using a range of analytic approaches. All of the genomic data have been aggregated in this online database, and a consistent set of analyses have been applied to each study. Additionally, a comprehensive set of cross-study analyses have been performed. A thorough collection of gene expression summaries are provided, inclusive of patient demographics, disease subclasses, regulated biological pathways, and functional classifications. Raw data is also available to download. The database is derived from two sets of brain samples, the Stanley Array collection and the Stanley Consortium collection. The Stanley Array collection contains 105 patients, and the Stanley Consortium collection contains 60 patients. Multiple genomic studies have been conducted using these brain samples. From these studies, twelve were selected for inclusion in the database on the basis of number of patients studied, genomic platform used, and data quality. The Consortium collection studies have fewer patients but more diversity in brain regions and array platforms, while the Array collection studies are more homogenous. There are tradeoffs, the Consortium results will be more variable, but findings may be more broadly representative. The collections contain brain samples from subjects in four main groups: Bipolar Schizophrenia, Depression, and Controls Brain regions used in the studies include: Broadman Area 6, Broadman Area 8/9, Broadman Area 10, Broadman Area 46, Cerebellum The 12 studies encompass a range of microarray platforms: Affymetrix HG-U95Av2, Affymetrix HG-U133A, Affymetrix HG-U133 2.0+, Codelink Human 20K, Agilent Human I, Custom cDNA Publications based on any of the clinical or genomic data should credit the Stanley Medical Research Institute, as well as any individual SMRI collaborators whose data is being used. Publications which make use of analytic results/methods in the database should additionally cite Dr. Michael Elashoff. Registration is required to access the data.

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.

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.

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.

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.

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.

Contains tables related to long-reads sequencing data for 100,000 Genomes Project participants.

• lrs_laboratory_sample: Data describing the characteristics and processing methods (DNA to library preparation) of samples from participants in the 100,000 Genomes Project for which long-reads sequencing has been carried out.
• lrs_sequencing_data: This table includes data describing long-read sequencing of a subset of 100,000 Genomes Project participants and associated output, including paths to raw and BAM files.
• cancer_ont_cohorts: Table listing participant ids, sample data, file paths and sequencing statistics for Oxford Nanopore cancer cohorts available in the Research Environment, along with corresponding matched germline and Illumina short reads files where available
• rare_disease_pacbio_pilot: This is a dataset of 91 rare disease samples from the 100k genome project re-sequenced with Pacific Biosciences (PacBio) as an example dataset to to demonstrate the utility of their HiFi technology.

NIAGADS is a national genetics data repository that archives and distributes genetics and genomics data for Alzheimer’s disease and related dementias to qualified researchers. It serves as the data coordinating center for the  Alzheimer’s Disease Sequencing Project (ADSP), and is responsible for collecting phenotypes and consent information from participating cohorts as well as sharing data with the research community. *Some subjects in NIAGADS are also part of other GAAIN Data Partners.

Initiated in 2003, the Genome Database for Rosaceae (GDR) is a curated and integrated web-based relational database providing centralized access to Rosaceae genomics, genetics and breeding data and analysis tools to facilitate basic, translational and applied Rosaceae research. GDR is supported by grants from the National Science Foundation Plant Genome Program (2003-2008), USDA National Institute of Food and Agriculture (NIFA) Specialty Crop Research Program (2009-2019), USDA NIFA National Research Support Project 10 (2014-2019), and the Washington Tree Fruit Research Commission (2008-2016), Clemson University, University of Florida and Washington State University. http://www.ars.usda.gov/is/graphics/photos/aug97/k6084-1.htm">K6084-1: Photo by Jack Dykinga Resources in this dataset:Resource Title: Genome Database for Rosaceae - Download Data. File Name: Web Page, url: https://www.rosaceae.org/data/download This is the download page for the Genome Database for Rosaceae - datasets can be downloaded directly from this location

A database of human mitochondrial genomes containing mtDNA sequences, polymorphic sites, and the ability to search for specific variants. It contains 1865 complete sequences and 839 coding region sequences.

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