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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.thearkdb.org/arkdb/

This website contains the mapping sequence of poultry. The ArkDB database system aims to provide a comprehensive public repository for genome mapping data from farmed and other animal species. In doing so, it aims to provide a route in to genomic and other sequence from the initial viewpoint of linkage mapping, RH mapping, physical mapping or - possibly more importantly - QTL mapping data. It's supported, in part, by the USDA-CSREES National Animal Genome Research Program in order to serve the poultry genome mapping community. This system represents a complete rewrite of the original version with the code migrated to java and the underlying database targeted at postgres (although any standards-compliant database engine should suffice). The initial release records details of maps and the markers that they contain. There are alternative entry points that target either a chromosome or a specific mapping analysis as the starting point. Limited relationships between markers are recorded and displayed. As with the previous version, all maps are drawn using data extracted from the database on the fly.

Proper citation: ChickBase (RRID:SCR_008147) Copy   

•   Source: SciCrunch Registry

http://microbialgenomics.energy.gov/index.shtml

Through its Microbial Genome Program (MGP) and its Genomics:GTL (GTL) program, DOEs Office of Biological and Environmental Research (BER) has sequenced more than 485 microbial genomes and 30 microbial communities having specialized biological capabilities. Identifying these genes will help investigators discern how gene activities in whole living systems are orchestrated to solve myriad life challenges. The MGP was begun in 1994 as a spinoff from the Human Genome Program. The goal of the program was to sequence the genomes of a number of nonpathogenic microbes that would be useful in solving DOE''s mission challenges in environmental-waste cleanup, energy production, carbon cycling, and biotechnology. Past projects include microbial genome program, microbial cell project, and the Laboratory Science Program at the DOE Joint Genome Institute. The two ongoing projects are Genomics: GTL program and Community Sequencing Program at the DOE Joint Genome Institute. Sponsors: Site sponsored by the U.S. Department of Energy Office of Science, Office of Biological and Environmental Research, THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 16,2025.

Proper citation: Microbial Genomics Program (RRID:SCR_008140) Copy   

•   Source: SciCrunch Registry

http://mips.helmholtz-muenchen.de/genre/proj/mpcdb/

A database of manually annotated mammalian protein complexes. To obtain a high-quality dataset, information was extracted from individual experiments described in the scientific literature. Data from high-throughput experiments was not included.

Proper citation: Mammalian Protein Complex Data Base (RRID:SCR_008209) Copy   

•   Source: SciCrunch Registry

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   

•   Source: SciCrunch Registry

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   

•   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://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   

•   Source: SciCrunch Registry

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   

•   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://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://hyperbrowser.uio.no/hb/

A generic web-based system, providing statistical methodology and computing power to handle a variety of biological inquires on genomic datasets.

Proper citation: Genomic HyperBrowser (RRID:SCR_010909) Copy   

•   Source: SciCrunch Registry

https://github.com/HMPNK/CSA2.6

Software pipeline for high-throughput chromosome level vertebrate genome assembly. Pipeline, which after contig assembly performs post assembly improvements by ordering assembly and closing gaps, as well as splitting of low supported regions.

Proper citation: Chromosome Scale Assembler (RRID:SCR_017960) Copy   

•   Source: SciCrunch Registry

https://github.com/gatech-genemark/ProtHint

Software pipeline for predicting and scoring hints (in form of introns, start and stop codons) in genome of interest by mapping and spliced aligning predicted genes to database of reference protein sequences.

Proper citation: ProtHint (RRID:SCR_021167) Copy   

•   Source: SciCrunch Registry

http://www.dnastar.com/t-genvision.aspx

A genomic visualization application to support easy generation of publication quality graphics and maps. It produces high quality images of annotated genomes but it can also be customized to accentuate specific areas of interest, such as comparing gene functionality, illustrating gene expression levels, and visualizing the coverage in an assembled contig.

Proper citation: GenVision (RRID:SCR_001166) Copy   

•   Source: SciCrunch Registry

http://www.scripps.edu/florida/technologies/cbs/index.html

Core facility that provides access to genome-wide collections of cDNAs and siRNAs that can be used to interrogate cellular models of signal transduction pathways and phenotypes. Services include cell lines, hit-picking clones and various screening sets, and access to equipment.Provides instruments:Analyst Molecular Devices,Embla Molecular Devices, Envision Perkin Elmer, Platemate Matrix, Tecan M200, Wellmate Matrix.

Proper citation: Scripps Research Institute Florida Cell Based High Throughput Screening Core Facility (RRID:SCR_014877) Copy   

•   Source: SciCrunch Registry

http://montana.eagle-i.net/i/0000012b-00be-4e65-df3b-3fdc80000000

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on October 27, 2023. Core for Microarray analysis, Database development, Systems biology analysis, Genome assembly, Pathway data analysis, Expression data analysis, Metagenomics analysis. To maintain equipment and software for bioinformatic research, promote bioinformatics education on the MSU campus, and provide training and support to biologists implementing bioinformatics tools in their research.

Proper citation: Montana State University Bioinformatics Core Facility (RRID:SCR_009937) Copy   

•   Source: SciCrunch Registry

http://www.mqtldb.org/

Data collection of large scale genome wide DNA methylation analysis of 1,000 mother-child pairs at serial time points across life course (ARIES).

Proper citation: mqtldb (RRID:SCR_018002) Copy   

•   Source: SciCrunch Registry

http://www.clcbio.com/products/clc-main-workbench/

A suite of software for DNA, RNA and protein sequence data analysis. The software allows for the analysis and visualization of Sanger sequencing data as well as gene expression analysis, molecular cloning, primer design, phylogenetic analyses, and sequence data management.

Proper citation: CLC Main Workbench (RRID:SCR_000354) Copy   

•   Source: SciCrunch Registry