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http://www.sanger.ac.uk/Projects/C_elegans/index.shtml

The Sanger Institute and the Genome Sequencing Center at the Washington University School of Medicine, St. Louis have collaborated to sequence the genomes of both C. elegans and C. briggsae. The completed C. elegans genome sequence is represented by over 3,000 individual clone sequences which can be accessed through this site (or through WormBase). These sequences are submitted to EMBL whenever the sequence or annotation changes (e.g. modification to gene structures) and these submissions are then mirrored to GenBank and DDBJ. These sequences (along with ESTs and proteins) can be searched on our C. elegans BLAST server. WormBase is the repository of mapping, sequencing and phenotypic information for C. elegans. The worm informatics group at the Sanger Institute play a key role in assembling the whole database. They also curate and develop some of the constituent databases that comprise WormBase.

Proper citation: Caenorhabditis Genome Sequencing Projects (RRID:SCR_008155) Copy   

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http://dimer.tamu.edu/doodle/

THIS RESOURCE IS NO LONGER IN SERVICE, documented on July 15, 2013. Doodle is a database that was developed to store and distribute information about the protein oligomerization domains that are encoded by various genomes. The protein oligomerization domains described here were found using the lambda repressor fusion system. Doodle uses a schema that is based on EnsEMBL, while also utilizing bioperl modules to both store and retrieve data. The frontend was developed entirely in perl, while the backend utilizes MySQL. GMOD was used to develop the genomic view.

Proper citation: Database of oligomerization domains from lambda experiments (RRID:SCR_008107) Copy   

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http://chromium.lovd.nl/LOVD2/home.php?select_db=CDKN2A

THIS RESOURCE IS NO LONGER IN SERVICE, documented August 23, 2016. The CDKN2A Database presents the germline and somatic variants of the CDKN2A tumor suppressor gene recorded in human disease through June 2003, annotated with evolutionary, structural, and functional information, in a format that allows the user to either download it or manipulate it for their purposes online. The goal is to provide a database that can be used as a resource by researchers and geneticists and that aids in the interpretation of CDKN2A missense variants. Most online mutation databases present flat files that cannot be manipulated, are often incomplete, and have varying degrees of annotation that may or may not help to interpret the data. They hope to use CDKN2A as a prototype for integrating computational and laboratory data to help interpret variants in other cancer-related genes and other single nucleotide polymorphisms (SNPs) found throughout the genome. Another goal of the lab is to interpret the functional and disease significance of missense variants in cancer susceptibility genes. Eventually, these results will be relevant to the interpretation of single nucleotide polymorphisms (SNPs) in general. The CDKN2A locus is a valuable model for assessing relationships among variation, structure, function, and disease because: Variants of this gene are associated with hereditary cancer: Familial Melanoma (and related syndromes); somatic alterations play a role in carcinogenesis; allelic variants occur whose functional consequences are unknown; reliable functional assays exist; and crystal structure is known. All variants in the database are recorded according to the nomenclature guidelines as outlined by the Human Genome Variation Society. This database is currently designed for research purposes only and is not yet recommended as a clinical resource. Many of the mutations reported here have not been tested for disease association and may represent normal, non-disease causing polymorphisms.

Proper citation: CDKN2A Database (RRID:SCR_008179) Copy   

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http://www.lamhdi.org/

THIS RESOURCE IS NO LONGER IN SERVICE, it has been replaced by Monarch Initiative. LAMHDI, the initiative to Link Animal Models to Human DIsease, is designed to accelerate the research process by providing biomedical researchers with a simple, comprehensive Web-based resource to find the best animal model for their research. LAMDHI is a free, Web-based, resource to help researchers bridge the gap between bench testing and human trials. It provides a free, unbiased resource that enables scientists to quickly find the best animal models for their research studies. LAMHDI includes mouse data from MGI, the Mouse Genome Informatics website; zebrafish data from ZFIN, the Zebrafish Model Organism Database; rat data from RGD, the Rat Genome Database; yeast data from SGD, the Saccharomyces Genome Database; and fly data from FlyBase. LAMHDI.org is operational today, and data is added regularly. Enhancements are planned to let researchers contribute their knowledge of the animal models available through LAMHDI. The LAMHDI goal is to allow researchers to share information about and access to animal models so they can refine research and testing, and reduce or replace the use of animal models where possible. LAMHDI Database Search: LAMHDI brings together scientifically validated information from various sources to create a composite multi-species database of animal models of human disease. To do this, the LAMHDI database is prepared from a variety of sources. The LAMHDI team takes publicly available data from OMIM, NCBI''s Entrez Gene database, Homologene, and WikiPathways, and builds a mathematical graph (think of it as a map or a web) that links these data together. OMIM is used to link human diseases with specific human genes, and Entrez provides universal identifiers for each of those genes. Human genes are linked to their counterpart genes in other species with Homologene, and those genes are linked to other genes tentatively or authoritatively using the data in WikiPathways. This preparatory work gives LAMHDI a web of human diseases linked to specific human genes, orthologous human genes, homologous genes in other species, and both human and non-human genes involved in specific metabolic pathways associated with those diseases. LAMHDI includes model data that partners provide directly from their data structures. For instance, MGI provides information about mouse models, including a disease for each model, as well as some genetic information (the ID of the model, in fact, identifies one or more genes). ZFIN provides genetic information for each zebrafish model, but no diseases, so zebrafish models are integrated by using the genes as the glue. For instance, a zebrafish model built to feature the zebrafish PKD2 gene would plug into the larger disease-gene map at the node representing the zebrafish PKD2 gene, which is connected to the node representing the human PKD2 gene, which in turn is connected to the node representing the human disease known as polycystic kidney disease. (Some of the partner data LAMHDI receives can even extend the base map. MGI provides a disease for every model, and in some cases this allows the creation of a disease-to-gene relationship in the LAMHDI database that might not already be documented in the OMIM dataset.) With curatorial and model information in hand, LAMHDI runs a lengthy automated process that exhaustively searches for every possible path between each model and each disease in the data, up to a set number of hops, producing for each disease-to-model pair a set of links from the disease to the model. The algorithm avoids circular paths and paths that include more than one disease anywhere in the middle of the path. At the end of this phase, LAMHDI has a comprehensive set of paths representing all the disease-to-model relationships in the data, varying in length from one hop to many hops. Each disease-to-model path is essentially a string of nodes in the data, where each node represents a disease, a gene, a linkage between genes (an orthologue, a homologue, or a pathway connection, referred to as a gene cluster or association), or a model. Each node has a human-friendly label, a set of terms and keywords, and - in most cases - a URL linking the node to the data source where it originated. When a researcher submits a search on the LAMHDI website, LAMHDI searches for the user''s search terms in its precomputed list of all known disease-to-model paths. It looks for the terms not only in the disease and model nodes, but also in every node along each path. The complete set of hits may include multiple paths between any given disease-to-model pair of endpoints. Each of these disease-to-model pair sets is ordered by the number of hops it involves, and the one involving the fewest hops is chosen to represent its respective disease-to-model pair in the search results presented to the user. Results are sorted by scores that represent their matches. The number of hops is one barometer of the strength of the evidence linking the model and the disease; fewer hops indicates the relationship is stronger, more hops indicates it may be weaker. This indicator works best for comparing models from a single partner dataset: MGI explicitly identifies a disease for each mouse model, so there can be disease-to-model hits for mice that involve just one hop. Because ZFIN does not explicitly identify a disease for each model, no zebrafish model will involve fewer than four hops to the nearest disease, from the zebrafish model to a zebrafish gene to a gene cluster to a human gene to a human disease.

Proper citation: LAMHDI: The Initiative to Link Animal Models to Human DIsease (RRID:SCR_008643) Copy   

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http://mrna.otago.ac.nz/

Database that provides access to mRNA sequences and associated regulatory elements that were processed from Genbank. These mRNA sequences include complete genomes, which are divided into 5-prime UTRs, 3-prime UTRs, initiation sequences, termination regions and full CDS sequences. This data can be searched for a range of properties including specific mRNA sequences, mRNA motifs, codon usage, RSCU values, information content, etc.

Proper citation: Transterm (RRID:SCR_008244) Copy   

•   Source: SciCrunch Registry

http://www.primervfx.com/#welcome

PrimerParadise is an online PCR primer database for genomics studies. The database contains predesigned PCR primers for amplification of exons, genes and SNPs of almost all sequenced genomes. Primers can be used for genome-wide projects (resequencing, mutation analysis, SNP detection etc). The primers for eukaryotic genomes have been tested with e-PCR to make sure that no alternative products will be generated. Also, all eukaryotic primers have been filtered to exclude primers that bind excessively throughout the genome. Genes are amplified as amplicons. Amplicons are defined as only one genes exons containing maximaly 3000 bp long dna segments. If gene is longer than 3000 bp then it is split into the segments at length 3000 bp. So for example gene at length 5000 bp is split into two segment and for both segments there were designed a separate primerpair. If genes exons length is over 3000 bp then it is split into amplicons as well. Every SNP has one primerpair. In addition of considering repetitive sequences and mono-dinucleotide repeats, we avoid designing primers to genome regions which contain other SNPs. -There are two ways to search for primers: you can use features IDs ( for SNP primers Reference ID, for gene/exon primers different IDs (Ensembl gene IDs, HUGO IDs for human genes, LocusLink IDs, RefSeq IDs, MIM IDs, NCBI gene names, SWISSPROT IDs for bacterial genes, VEGA gene IDs for human and mouse, Sanger S.pombe systematic gene names and common gene names, S.cerevisiae GeneBanks Locus, AccNo, GI IDs and common gene names) -you can use genome regions (chromosome coordinates, chromosome bands if exists) -Currently we provide 3 primers collections: proPCR for prokaryotic organisms genes primers -euPCR for eukaryotic organisms genes/exons primers -snpPCR for eukaryotic organisms SNP primers Sponsors: PrimerStudio is funded by the University of Tartu.

Proper citation: PrimerStudio (RRID:SCR_008232) Copy   

•   Source: SciCrunch Registry

http://www.molgen.ua.ac.be/ADMutations/default.cfm?MT=1&ML=0&Page=ADMDB

A locus-specific database aimed at collecting known mutations and non-pathogenic coding variations in the genes related to Alzheimer disease (AD) and frontotemporal dementia (FTD), following the guidelines of the Human Genome Variation Society. Mutations can be retrieved based on the gene, phenotype and publication. The database contains mutations reported in the literature and at scientific meetings, and unpublished mutations directly submitted to the database. To date, AD&FTDMDB contains mutations in the genes encoding the Amyloid Beta Precursor Protein (APP), Presenilin 1 (PSEN1), Presenilin 2 (PSEN2), Chromatin Modifying Protein 2B (CHMP2B), fusion (involved in t(12;16) in malignant liposarcoma) (FUS), Granulin (GRN), Microtubule Associated Protein Tau (MAPT), TAR DNA binding protein (TARDBP) and Valosin-containing Protein (VCP) and holds 415 different mutations observed in 1027 patients or families. As of March 2013, the latest publications referenced were from 2008, indicating that this resource may not be up to date.

Proper citation: Alzheimer Disease and Frontotemporal Dementia Mutation Database (RRID:SCR_008286) Copy   

•   Source: SciCrunch Registry

http://www.ebi.ac.uk/genomes/plasmid.html

The Plasmid Genome Database aims to collate biological and genomic data for all bacterial plasmids in the hopes of enabling rapid, interrogation of both meta- and genomic data. Data maintained includes access to all plasmid genomes and information on core genomic features obtained from parsing the original EMBL/DDBJ/NCBI submission. In addition a suite of third party analyses has been performed for each genome to supplement the original annotation. This site also links to Genome Atlases provided by the Centre for Biological Sequence Analysis (CBS). The motivation behind the construction of this site derived from observations from genome sequencing projects: the abundance and inferred importance of the horizontal gene pool (HGP) in bacterial adaptation and evolution. In so far as plasmids are autonomously replicating, extrachromosomal elements they are a readily identifiable and accessible component of the HGP. Also plasmids have been identified in almost all bacterial divisions, ranging in size from less than 2 kbp to > 1.5 Mbp and as such represent a defined, yet diverse and complex sample of genes in the HGP.

Proper citation: Plasmid Genome Database (RRID:SCR_008228) Copy   

•   Source: SciCrunch Registry

http://mips.gsf.de/services/genomes/uwe25/

THIS RESOURCE IS NO LONGER IN SERVICE, documented on July 15, 2013. This is the official database of the environmental chlamydia genome project. This resource provides access to finished sequence for Parachlamydia-related symbiont UWE25 and to a wide range of manual annotations, automatical analyses and derived datasets. Functional classification and description has been manually annotated according to the Annotation guidelines. Chlamydiae are the major cause of preventable blindness and sexually transmitted disease. Genome analysis of a chlamydia-related symbiont of free-living amoebae revealed that it is twice as large as any of the pathogenic chlamydiae and had few signs of recent lateral gene acquisition. We showed that about 700 million years ago the last common ancestor of pathogenic and symbiotic chlamydiae was already adapted to intracellular survival in early eukaryotes and contained many virulence factors found in modern pathogenic chlamydiae, including a type III secretion system. Ancient chlamydiae appear to be the originators of mechanisms for the exploitation of eukaryotic cells. Environmental chlamydiae have recently been recognized as obligate endosymbionts of free-living amoebae and have been implicated as potential human pathogens. Environmental chlamydiae form a deep branching evolutionary lineage within the medically important order Chlamydiales. Despite their high diversity and ubiquitous distribution in clinical and environmental samples only limited information about genetics and ecology of these microorganisms is available. The Parachlamydia-related Acanthamoeba symbiont UWE25 was therefore selected as representative environmental chlamydia strain for whole genome sequencing. Comparative genome analysis was performed using PEDANT and simap. Sponsors: The environmental chlamydia genome project was funded by the bmb+f (German Federal Ministry of Education and Research) and is part of the Competence Network PathoGenoMiK.

Proper citation: Protochlamydia amoebophila UWE25 (RRID:SCR_008222) Copy   

•   Source: SciCrunch Registry

http://dnatraffic.ibb.waw.pl/

DNAtraffic database is dedicated to be an unique comprehensive and richly annotated database of genome dynamics during the cell life. DNAtraffic contains extensive data on the nomenclature, ontology, structure and function of proteins related to control of the DNA integrity mechanisms such as chromatin remodeling, DNA repair and damage response pathways from eight model organisms commonly used in the DNA-related study: Homo sapiens, Mus musculus, Drosophila melanogaster, Caenorhabditis elegans, Saccharomyces cerevisiae, Schizosaccharomyces pombe, Escherichia coli and Arabidopsis thaliana. DNAtraffic contains comprehensive information on diseases related to the assembled human proteins. Database is richly annotated in the systemic information on the nomenclature, chemistry and structure of the DNA damage and drugs targeting nucleic acids and/or proteins involved in the maintenance of genome stability. One of the DNAtraffic database aim is to create the first platform of the combinatorial complexity of DNA metabolism pathway analysis. Database includes illustrations of pathway, damage, protein and drug. Since DNAtraffic is designed to cover a broad spectrum of scientific disciplines it has to be extensively linked to numerous external data sources. Database represents the result of the manual annotation work aimed at making the DNAtraffic database much more useful for a wide range of systems biology applications. DNAtraffic database is freely available and can be queried by the name of DNA network process, DNA damage, protein, disease, and drug.

Proper citation: DNAtraffic (RRID:SCR_008886) Copy   

•   Source: SciCrunch Registry

http://www.ebi.ac.uk/genomes

The EBI genomes pages give access to a large number of complete genomes including bacteria, archaea, viruses, phages, plasmids, viroids and eukaryotes. Methods using whole genome shotgun data are used to gain a large amount of genome coverage for an organism. WGS data for a growing number of organisms are being submitted to DDBJ/EMBL/GenBank. Genome entries have been listed in their appropriate category which may be browsed using the website navigation tool bar on the left. While organelles are all listed in a separate category, any from Eukaryota with chromosome entries are also listed in the Eukaryota page. Within each page, entries are grouped and sorted at the species level with links to the taxonomy page for that species separating each group. Within each species, entries whose source organism has been categorized further are grouped and numbered accordingly. Links are made to: * taxonomy * complete EMBL flatfile * CON files * lists of CON segments * Project * Proteomes pages * FASTA file of Proteins * list of Proteins

Proper citation: EBI Genomes (RRID:SCR_002426) Copy   

•   Source: SciCrunch Registry

https://jmorp.megabank.tohoku.ac.jp/

Japanese multi omics reference panel. Provides multidimensional approach to diversity of Japanese population. Public database for plasma metabolome and proteome analyses. Updated to metabolome, genome, transcriptome, metagenome, number of samples, analysis methods of each dataset, expanding links between each layer and links between hierarchies.

Proper citation: jMORP (RRID:SCR_024755) Copy   

•   Source: SciCrunch Registry

http://www.ncbi.nlm.nih.gov/RefSeq/

Collection of curated, non-redundant genomic DNA, transcript RNA, and protein sequences produced by NCBI. Provides a reference for genome annotation, gene identification and characterization, mutation and polymorphism analysis, expression studies, and comparative analyses. Accessed through the Nucleotide and Protein databases.

Proper citation: RefSeq (RRID:SCR_003496) Copy   

•   Source: SciCrunch Registry

http://genomequebec.mcgill.ca/PReMod

Database that describes more than 100,000 computational predicted transcriptional regulatory modules within the human genome. These modules represent the regulatory potential for 229 transcription factors families and are the first genome-wide / transcription factor-wide collection of predicted regulatory modules for the human genome. The algorithm used involves two steps: (i) Identification and scoring of putative transcription factor binding sites using 481 TRANSFAC 7.2 position weight matrices (PWMs) for vertebrate transcription factors. To this end, each non-coding position of the human genome was evaluated for its similarity to each PWM using a log-likelihood ratio score with a local GC-parameterized third-order Markov background model. Corresponding orthologous positions in mouse and rat genomes were evaluated similarly and a weighted average of the human, mouse, and rat log-likelihood scores at aligned positions (based on a Multiz (Blanchette et al. 2004) genome-wide alignment of these three species) was used to define the matrix score for each genomic position and each PWM. (ii) Detection of clustered putative binding sites. To assign a module score to a given region, the five transcription factors with the highest total scoring hits are identified, and a p-value is assigned to the total score observed of the top 1, 2, 3, 4, or 5 factors. The p-value computation takes into consideration the number of factors involved (1 to 5), their total binding site scores, and the length and GC content of the region under evaluation. Users can retrieve all information for a given region, a given PWM, a given gene and so on. Several options are given for textual output or visualization of the data.

Proper citation: PReMod (RRID:SCR_003403) Copy   

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http://www.flytf.org/

A database of genomic and protein data for Drosophila site-specific transcription factors.

Proper citation: FlyTF.org (RRID:SCR_004123) Copy   

•   Source: SciCrunch Registry

http://bioinfo.mbi.ucla.edu/ASAP/

THIS RESOURCE IS NO LONGER IN SERVICE, documented on 8/12/13. Database to access and mine alternative splicing information coming from genomics and proteomics based on genome-wide analyses of alternative splicing in human (30 793 alternative splice relationships found) from detailed alignment of expressed sequences onto the genomic sequence. ASAP provides precise gene exon-intron structure, alternative splicing, tissue specificity of alternative splice forms, and protein isoform sequences resulting from alternative splicing. They developed an automated method for discovering human tissue-specific regulation of alternative splicing through a genome-wide analysis of expressed sequence tags (ESTs), which involves classifying human EST libraries according to tissue categories and Bayesian statistical analysis. They use the UniGene clusters of human Expressed Sequence Tags (ESTs) to identify splices. The UniGene EST's are clustered so that a single cluster roughly corresponds to a gene (or at least a part of a gene). A single EST represents a portion of a processed (already spliced) mRNA. A given cluster contains many ESTs, each representing an outcome of a series of splicing events. The ESTs in UniGene contain the different mRNA isoforms transcribed from an alternatively spliced gene. They are not predicting alternative splicing, but locating it based on EST analysis. The discovered splices are further analyzed to determine alternative splicing events. They have identified 6201 alternative splice relationships in human genes, through a genome-wide analysis of expressed sequence tags (ESTs). Starting with 2.1 million human mRNA and EST sequences, they mapped expressed sequences onto the draft human genome sequence and only accepted splices that obeyed the standard splice site consensus. After constructing a tissue list of 46 human tissues with 2 million human ESTs, they generated a database of novel human alternative splices that is four times larger than our previous report, and used Bayesian statistics to compare the relative abundance of every pair of alternative splices in these tissues. Using several statistical criteria for tissue specificity, they have identified 667 tissue-specific alternative splicing relationships and analyzed their distribution in human tissues. They have validated our results by comparison with independent studies. This genome-wide analysis of tissue specificity of alternative splicing will provide a useful resource to study the tissue-specific functions of transcripts and the association of tissue-specific variants with human diseases.

Proper citation: ASAP: the Alternative Splicing Annotation Project (RRID:SCR_003415) Copy   

•   Source: SciCrunch Registry

http://www.ncbi.nlm.nih.gov/mapview/map_search.cgi?taxid=7165

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on January 11, 2023. A database for the Anopheles gambiae str. PEST genome that was sequenced using a whole genome shotgun approach. The database aims to contribute to the understanding of mosquito genome structure and organization and will assist the development of malaria control strategies and improved anti-malarial drugs and vaccines. Sequences were generated and assembled into contigs for submission to GenBank.

Proper citation: Anopheles gambiae (African malaria mosquito) genome view (RRID:SCR_004402) Copy   

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http://swissregulon.unibas.ch/fcgi/sr/swissregulon

A database of genome-wide annotations of regulatory sites. The predictions are based on Bayesian probabilistic analysis of a combination of input information including: * Experimentally determined binding sites reported in the literature. * Known sequence-specificities of transcription factors. * ChIP-chip and ChIP-seq data. * Alignments of orthologous non-coding regions. Predictions were made using the PhyloGibbs, MotEvo, IRUS and ISMARA algorithms developed in their group, depending on the data available for each organism. Annotations can be viewed in a Gbrowse genome browser and can also be downloaded in flat file format.

Proper citation: SwissRegulon (RRID:SCR_005333) Copy   

•   Source: SciCrunch Registry

http://bigg.ucsd.edu/

A knowledgebase of Biochemically, Genetically and Genomically structured genome-scale metabolic network reconstructions. BiGG integrates several published genome-scale metabolic networks into one resource with standard nomenclature which allows components to be compared across different organisms. BiGG can be used to browse model content, visualize metabolic pathway maps, and export SBML files of the models for further analysis by external software packages. Users may follow links from BiGG to several external databases to obtain additional information on genes, proteins, reactions, metabolites and citations of interest.

Proper citation: BiGG Database (RRID:SCR_005809) Copy   

•   Source: SciCrunch Registry

http://h-invitational.jp/varygene/

It consists of a Genome Browser, an LD Search System, and the VaryGene 2 system. The Generic Genome Browser is a combination of database and interactive Web page for manipulating and displaying annotations on genomes, while LDSearchSystem is a search system for linkage disequilibrium (LD) bins. VaryGene 2 is a system to search, display, and download our research results on human polymorphism based on publicly available data and annotations of transcripts presented by H-InvDB. VaryGene 2 provides information about single nucleotide polymorphisms (SNPs), deletion-insertion polymorphisms (DIPs), short tandem repeats (STRs), single amino acid repeats (SARs), structural variation (or copy number variations: CNVs), and their relations to the genome, transcripts, and functional domains. Users can search by polymorphisms, transcripts, STRs/SARs, and CNVs.

Proper citation: VarySysDB (RRID:SCR_005880) Copy   

•   Source: SciCrunch Registry