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Database of compiled, public, deep sequencing miRNA data and several novel tools to facilitate exploration of massive data. The miR-seq browser supports users to examine short read alignment with the secondary structure and read count information available in concurrent windows. Features such as sequence editing, sorting, ordering, import and export of user data are of great utility for studying iso-miRs, miRNA editing and modifications. miRNA����??target relation is essential for understanding miRNA function. Coexpression analysis of miRNA and target mRNAs, based on miRNA-seq and RNA-seq data from the same sample, is visualized in the heat-map and network views where users can investigate the inverse correlation of gene expression and target relations, compiled from various databases of predicted and validated targets.
Proper citation: miRGator (RRID:SCR_007793) Copy
SYSTERS is a database of protein sequences grouped into homologous families and superfamilies. The SYSTERS project aims to provide a meaningful partitioning of the whole protein sequence space by a fully automatic procedure. A refined two-step algorithm assigns each protein to a family and a superfamily. The sequence data underlying SYSTERS release 4 now comprise several protein sequence databases derived from completely sequenced genomes (ENSEMBL, TAIR, SGD and GeneDB), in addition to the comprehensive Swiss-Prot/TrEMBL databases. To augment the automatically derived results, information from external databases like Pfam and Gene Ontology are added to the web server. Furthermore, users can retrieve pre-processed analyses of families like multiple alignments and phylogenetic trees. New query options comprise a batch retrieval tool for functional inference about families based on automatic keyword extraction from sequence annotations. A new access point, PhyloMatrix, allows the retrieval of phylogenetic profiles of SYSTERS families across organisms with completely sequenced genomes. Gene, Human, Vertebrate, Genome, Human ORFs
Proper citation: SYSTERS (RRID:SCR_007955) Copy
http://supfam.org/SUPERFAMILY/
SUPERFAMILY is a database of structural and functional protein annotations for all completely sequenced organisms. The SUPERFAMILY annotation is based on a collection of hidden Markov models, which represent structural protein domains at the SCOP superfamily level. A superfamily groups together domains which have an evolutionary relationship. The annotation is produced by scanning protein sequences from over 1,700 completely sequenced genomes against the hidden Markov models.
Proper citation: SUPERFAMILY (RRID:SCR_007952) Copy
It provides a database based on a pre-computed similarity matrix covering the similarity space formed by >4 million amino acid sequences from public databases and completely sequenced genomes. The database is capable of handling very large datasets and is updated incrementally. For sequence similarity searches and pairwise alignments, we implemented a grid-enabled software system, which is based on FASTA heuristics and the Smith Waterman algorithm. SimpleSIMAP and AdvancedSIMAP retrieve homologs for given protein sequences that need to be contained in the SIMAP database. While SimpleSIMAP provides only selected parameters and preconfigured search spaces, the AdvancedSIMAP allows the user to specify search space, filtering and sorting parameters in a flexible manner. Both types of queries result in lists of homologs that are linked in turn to their homologs. So the web interfaces allow users to explore quickly and interactively the protein world by homology. Sponsors: SIMAP is supported by the Department of Genome Oriented Bioinformatics of the Technische Universitt Mnchen and the Institute for Bioinformatics of the GSF-National Research Center for Environment and Health.
Proper citation: SIMAP (RRID:SCR_007927) Copy
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
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
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
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
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
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
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
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
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
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
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
https://github.com/Nextomics/NextPolish
Software tool to fix base errors SNV/Indel in genome generated by noisy reads. Used to correct error bases in reference genome.
Proper citation: NextPolish (RRID:SCR_025232) Copy
https://github.com/BackofenLab/HVSeeker/tree/main
Software tool for distinguishing between bacterial and phage sequences. Consists of two separate models: one analyzing DNA sequences and the other focusing on proteins.
Proper citation: HVSeeker (RRID:SCR_026120) Copy
http://www.genome.gov/27549169
THIS RESOURCE IS NO LONGER IN SERVICE. Documented on October 30,2025. 2012 workshop to establish a Central Resource of Data from Genome Sequencing Projects. The workshop addressed the challenges to aggregating and analyzing data sets from genome sequencing studies, such as: * Data sets being generally hard to access. * Data residing in various databases. * Variant and exposure/phenotype data not being comparable across studies. Participants in the workshop discussed options for dealing with these challenges, along with their costs and tradeoffs. Videos and accompanying slides from the workshop are available. Also available as a video playlist on GenomeTV
Proper citation: NHGRI: Establishing a Central Resource of Data from Genome Sequencing Projects (RRID:SCR_003205) Copy
http://sites.huji.ac.il/malaria/
Data set of metabolic pathways for the malaria parasite based on the present knowledge of parasite biochemistry and on pathways known to occur in other unicellular eukaryotes. This site extracted the pertinent information from the universal sites and presented them in an educative and informative format. The site also includes, cell-cell interactions (cytoadherence and rosetting), invasion of the erythrocyte by the parasite and transport functions. It also contains an artistic impression of the ultrastructural morphology of the interaerythrocytic cycle stages and some details about the morphology of mitochondria and the apicoplast. Most pathways are relevant to the erythrocytic phase of the parasite cycle. All maps were checked for the presence of enzyme-coding genes as they are officially annotated in the Plasmodium genome (http://plasmodb.org/). The site is constructed in a hierarchical pattern that permits logical deepening: * Grouped pathways of major chemical components or biological process ** Specific pathways or specific process *** Chemical structures of substrates and products or process **** Names of enzymes and their genes or components of process Each map is linked to other maps thus enabling to verify the origin of a substrate or the fate of a product. Clicking on the EC number that appears next to each enzyme, connects the site to BRENDA, SWISSPROT ExPASy ENZYME, PlasmoDB and to IUBMB reaction scheme. Clicking of the name of a metabolite, connects the site to KEGG thus providing its chemical structure and formula. Next to each enzyme there is a pie that depicts the stage-dependent transcription of the enzyme''s coding gene. The pie is constructed as a clock of the 48 hours of the parasite cycle, where red signifies over-transcription and green, under-transcription. Clicking on the pie links to the DeRisi/UCSF transcriptome database.
Proper citation: Malaria Parasite Metabolic Pathways (RRID:SCR_007072) Copy
http://www.broad.mit.edu/node/305
The Connectivity Map aims to generate a detailed map that links gene patterns associated with disease to corresponding patterns produced by drug candidates and a variety of genetic manipulations. The Connectivity Map is the most comprehensive effort yet for using genomics in a drug-discovery framework. It allows researchers to screen compounds against genome-wide disease signatures, rather than a pre-selected set of target genes. Drugs are paired with diseases using sophisticated pattern-matching methods with a high level of resolution and specificity. To build a Connectivity Map, the Broad Institute brings together molecular biologists, genomics specialists, computational scientists, pharmacologists, chemists and chemical biologists, as well as expertise from across the breadth and depth of medicine.Connectivity map is a large public database of signatures of drugs and genes, and pattern-matching tools to detect similarities among these signatures.The parent site for the Broad Institute at MIT has a software library of software applications developed for use in genetic analysis.
Proper citation: National Institute of Mental Health (NIMH) Human Genetics Initiative (RRID:SCR_007436) Copy
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