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http://proline.bic.nus.edu.sg/dedb/

Database on Drosophila melanogaster exons presented in a splicing graph form. Data is based on release 3.2 of the Drosophila melanogaster genome annotations available at FlyBase. The gene structure information extracted from the annotations were checked, clustered and transformed into splicing graph. The splicing graph form of the gene constructs were then used for classification of the various types of alternative splicing events. In addition, Pfam domains were mapped onto the gene structure. Users can query the database using the query page using BLAST, FlyBase Gene Name, FlyBase Gene Symbol, Pfam Accession Number and Pfam Identifier. This allows users to determine the Drosophila melanogaster homology of their gene using a BLAST search and to visualize the alternative splicing variants if any. Users can also determine genes containing a particular domain using the Pfam Accession Numbers and Identifiers.

Proper citation: Drosophila melanogaster Exon Database (RRID:SCR_013441) Copy   

•   Source: SciCrunch Registry

http://toxodb.org/toxo/

A genome and functional genomic database for the protozoan parasite Toxoplasma gondii. It incorporates the sequence and annotation of the T. gondii ME49 strain, as well as genome sequences for the GT1, VEG and RH (Chr Ia, Chr Ib) strains. Sequence information is integrated with various other genomic-scale data, including community annotation, ESTs, gene expression and proteomics data. Organisms * Toxoplasma gondii (ME49, RH, GT1, Veg strains) * Neospora caninum * environmental isolate sequences from numerous species Tools * BLAST: Identify Sequence Similarities * Sequence Retrieval: Retrieve Specific Sequences using IDs and coordinates * PubMed and Entrez: View the Latest Toxoplasma, Neospora Pubmed and Entrez Results * Genome Browser: View Sequences and Features in the genome browser * Ancillary Genome Browse: Access Additional info like Probeset data and Toxoplasma Array info

Proper citation: ApiDB ToxoDB (RRID:SCR_013453) Copy   

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http://www.treefam.org

A database of phylogenetic trees of animal genes. It aims at developing a curated resource that gives reliable information about ortholog and paralog assignments, and evolutionary history of various gene families. TreeFam defines a gene family as a group of genes that evolved after the speciation of single-metazoan animals. It also tries to include outgroup genes like yeast (S. cerevisiae and S. pombe) and plant (A. thaliana) to reveal these distant members.TreeFam is also an ortholog database. Unlike other pairwise alignment based ones, TreeFam infers orthologs by means of gene trees. It fits a gene tree into the universal species tree and finds historical duplications, speciations and losses events. TreeFam uses this information to evaluate tree building, guide manual curation, and infer complex ortholog and paralog relations.The basic elements of TreeFam are gene families that can be divided into two parts: TreeFam-A and TreeFam-B families. TreeFam-B families are automatically created. They might contain errors given complex phylogenies. TreeFam-A families are manually curated from TreeFam-B ones. Family names and node names are assigned at the same time. The ultimate goal of TreeFam is to present a curated resource for all the families. phylogenetic tree, animal, vertebrate, invertebrate, gene, ortholog, paralog, evolutionary history, gene families, single-metazoan animals, outgroup genes like yeast (S. cerevisiae and S. pombe), plant (A. thaliana), historical duplications, speciations, losses, Human, Genome, comparative genomics

Proper citation: Tree families database (RRID:SCR_013401) Copy   

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http://dorina.mdc-berlin.de/rbp_browser/dorina.html

In animals, RNA binding proteins (RBPs) and microRNAs (miRNAs) post-transcriptionally regulate the expression of virtually all genes by binding to RNA. Recent advances in experimental and computational methods facilitate transcriptome-wide mapping of these interactions. It is thought that the combinatorial action of RBPs and miRNAs on target mRNAs form a post-transcriptional regulatory code. We provide a database that supports the quest for deciphering this regulatory code. Within doRiNA, we are systematically curating, storing and integrating binding site data for RBPs and miRNAs. Users are free to take a target (mRNA) or regulator (RBP and/or miRNA) centric view on the data. We have implemented a database framework with short query response times for complex searches (e.g. asking for all targets of a particular combination of regulators). All search results can be browsed, inspected and analyzed in conjunction with a huge selection of other genome-wide data, because our database is directly linked to a local copy of the UCSC genome browser. At the time of writing, doRiNA encompasses RBP data for the human, mouse and worm genomes. For computational miRNA target site predictions, we provide an update of PicTar predictions.

Proper citation: doRiNA (RRID:SCR_013222) Copy   

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http://www.echinobase.org/

Database that provide a genomic information and comparative genomics platform on sea urchins and related echinoderms. It provide collection of information to directly support experimental work on these useful research models in cell and developmental biology.

Proper citation: EchinoBase (RRID:SCR_013732) Copy   

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http://www.kazusa.or.jp/kaos/

This site has been developed by Kazusa DNA Research Institute for the purpose of offering the science community the analyzed sequence data produced by a multi-national Arabidopsis genome sequencing project coordinated by the Arabidopsis Genome Initiatives (AGI). The aim of this service is to enable users to browse the annotated sequence data produced by all the sequencing teams of AGI through an user-friendly graphic display system and search engines. Gene structures proposed on the annotated sequences as well as those predicted by computer programs are presented and each graphic item has a hyperlink to detailed information of the corresponding area. The nucleotide sequence data deposited in GenBank by AGI was downloaded, re-computer-analyzed at Kazusa and parsed results are displayed graphically.

Proper citation: Kazusa Arabidopsis data opening site (RRID:SCR_013511) Copy   

•   Source: SciCrunch Registry

http://www.informatics.jax.org/genes.shtml

Searchable database of mouse genes, DNA segments, cytogenetic markers and QTLs. MGI provides access to integrated data on mouse genes and genome features, from sequences and genomic maps to gene expression and disease models.

Proper citation: Genes, Genome Features and Maps (RRID:SCR_017524) Copy   

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http://www.mirtoolsgallery.org/miRToolsGallery/node/1055

Comprehensive resource of microRNA target predictions and expression profiles. Used for whole genome prediction of miRNA target genes. For each miRNA, target genes are selected on basis of sequence complementarity using position weighted local alignment algorithm, free energies of RNA-RNA duplexes, and conservation of target sites in related genomes. Provides information about set of genes potentially regulated by particular microRNA, co-occurrence of predicted target sites for multiple microRNAs in mRNA and microRNA expression profiles in tissues. Users are allowed to customize algorithm, numerical parameters, and position-specific rules., THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 16,2025.

Proper citation: miRanda (RRID:SCR_017496) Copy   

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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   

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https://fungi.ensembl.org/Neurospora_crassa/Info/Index

It's strategy involves Whole Genome Shotgun (WGS) sequencing, in which sequence from the entire genome is generated and reassembled. This method is standard for microbial genome sequencing, and has been successfully applied to Drosophila. Neurospora is an ideal candidate for this approach because of the low repeat content of the genome. Neurospora crassa Database has expanded the scope of its database by including a mitochondrial annotation, incorporating information from the Neurospora compendium, and assigning NCU numbers to tRNA and rRNAs. They have improved the annotation process to predict untranslated regions and to reduce the number of spurious predictions. As a result, version 3 contains 9,826 genes, 794 fewer than version 2. During the initial phase of a WGS project they sequence both ends of the 4 kb inserts from a plasmid library prepared using randomly sheared and sized-selected DNA. The shotgun reads are assembled by recognizing overlapping regions of sequence and making use of the knowledge of the orientation and distance of the paired reads from each plasmid. Obtaining deep sequence coverage though high levels of sequence redundancy assures that the majority of the genome is represented in the initial assembly and that the consensus sequence is of high quality. Their approach toward the initial assembly was conservative, meaning they would rather fail to join sequence contigs that might overlap each other than risk making false joins between two closely related but non-overlapping genomic regions. Hence, the initial assembly contains many sequence contigs and over time these contigs will increase in size and decrease in number as they are joined together. After shotgun sequencing and assembly there was a second phase of sequencing in which additional sequence was obtained from specific regions that were missing from the original assembly or are recognized to be of low quality in the consensus. The Neurospora crassa sequencing project reflects a close collaboration between the Broad Institute and the Neurospora research community. Principal investigators include Bruce Birren and Chad Nusbaum from the Broad Institute, Matt Sachs at the Oregon Graduate Institute of Science and Technology, Chuck Staben at the University of Kentucky and Jak Kinsey at the Fungal Genetics Stock Center at the University of Kansas Medical Center. In addition, we have a larger Advisory Board made up of a number of Neurospora researchers. Sponsors: They have been funded by the National Science Foundation to sequence the N. crassa genome and make the information publicly available.

Proper citation: Neurospora crassa Database (RRID:SCR_001372) Copy   

•   Source: SciCrunch Registry

https://gtexportal.org/home/

Database and browser that provides a central resource to archive and display association between genetic variation and high-throughput molecular-level phenotypes. This effort originated with the NIH GTEx roadmap project: however the scope of this resource will be extended to include any available genotype/molecular phenotype datasets.

Proper citation: GTEx eQTL Browser (RRID:SCR_001618) Copy   

•   Source: SciCrunch Registry

https://www.bgee.org/

Database to retrieve and compare gene expression patterns between animal species. Bgee first maps heterogeneous expression data (currently bulk RNA-Seq, scRNA-Seq, Affymetrix, in situ hybridization, and EST data) to anatomy and development of different species. Bgee is based exclusively on curated healthy wild-type expression data (e.g., no gene knock-out, no treatment, no disease), to provide a comparable reference of gene expression.

Proper citation: Bgee: dataBase for Gene Expression Evolution (RRID:SCR_002028) Copy   

•   Source: SciCrunch Registry

http://giladlab.uchicago.edu/orthoExon/

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 23,2022. Database of orthologous exon regions in the genomes of human, chimpanzee, and rhesus macaque. It can be used in analysis of multi-species RNA-seq expression data, allowing for comparisons of exon-level expression across primates, as well as comparative examination of alternative splicing and transcript isoforms.

Proper citation: Primate Orthologous Exon Database (RRID:SCR_002065) Copy   

•   Source: SciCrunch Registry

http://www.nih.gov/science/models/rat/

The Rat Genome Program was launched after the National Institutes of Health (NIH) realized the potential of rat models in understanding basic biology and human health and disease. The purpose of this NIH Rat Genomics and Genetics web site is to serve as a central point for information on NIH sponsored and related rat genetic and genomic activities and resources. It will provide information on: the follow up to recommendations made to the NIH; funding opportunities for rat genomic and genetic tools and resources; major rat genomic resources available and/or produced in response to the NIH Rat Program; courses and meetings related to rat genomics and genetics; and selected reports and publications. These programs have produced a wide variety of resources and a way to link and capitalize upon the data and resources of other model organisms and the human. In conjunction with and in addition to these programs, the NIH, through the RGWG, has convened advisory groups and workshops to discuss the opportunities that rat models offer and provide recommendations on the investments that are needed to capitalize on these opportunities.

Proper citation: NIH Rat Genomics and Genetics (RRID:SCR_002267) Copy   

•   Source: SciCrunch Registry

http://cancer.sanger.ac.uk/cancergenome/projects/cosmic/

Database to store and display somatic mutation information and related details and contains information relating to human cancers. The mutation data and associated information is extracted from the primary literature. In order to provide a consistent view of the data a histology and tissue ontology has been created and all mutations are mapped to a single version of each gene. The data can be queried by tissue, histology or gene and displayed as a graph, as a table or exported in various formats.
Some key features of COSMIC are:
* Contains information on publications, samples and mutations. Includes samples which have been found to be negative for mutations during screening therefore enabling frequency data to be calculated for mutations in different genes in different cancer types.
* Samples entered include benign neoplasms and other benign proliferations, in situ and invasive tumours, recurrences, metastases and cancer cell lines.

Proper citation: COSMIC - Catalogue Of Somatic Mutations In Cancer (RRID:SCR_002260) Copy   

•   Source: SciCrunch Registry

http://mech.ctb.pku.edu.cn/protisa/

Database of confirmed translation initiation sites (TISs) for prokaryotic genomes. The confirmed data has supporting evidence from different sources, including experiments records in the public protein database Swiss-Prot, literature, conserved domain search and sequence alignment among orthologous genes. Combing with predictions from the-state-of-the-art TIS predictor MED-Start/MED-StartPlus (in release 1.0 & 1.2) and TriTISA (since release 1.4) and annotations on potential regulatory signals, the database can serve as a refined annotation resource for the public database RefSeq.

Proper citation: ProTISA (RRID:SCR_002138) Copy   

•   Source: SciCrunch Registry

http://www.ebi.ac.uk/swissprot/hpi/hpi.html

THIS RESOURCE IS NO LONGER IN SERVICE, documented on August 03, 2011. IT HAS BEEN REPLACED BY A NEW UniProtKB/Swiss-Prot ANNOTATION PROGRAM CALLED UniProt Chordata protein annotation program. The Human Proteome Initiative (HPI) aims to annotate all known human protein sequences, as well as their orthologous sequences in other mammals, according to the quality standards of UniProtKB/Swiss-Prot. In addition to accurate sequences, we strive to provide, for each protein, a wealth of information that includes the description of its function, domain structure, subcellular location, similarities to other proteins, etc. Although as complete as currently possible, the human protein set they provide is still imperfect, it will have to be reviewed and updated with future research results. They will also create entries for newly discovered human proteins, increase the number of splice variants, explore the full range of post-translational modifications (PTMs) and continue to build a comprehensive view of protein variation in the human population. The availability of the human genome sequence has enabled the exploration and exploitation of the human genome and proteome to begin. Research has now focused on the annotation of the genome and in particular of the proteome. With expert annotation extracted from the literature by biologists as the foundation, it has been possible to expand into the areas of data mining and automatic annotation. With further development and integration of pattern recognition methods and the application of alignments clustering, proteome analysis can now be provided in a meaningful way. These various approaches have been integrated to attach, extract and combine as much relevant information as possible to the proteome. This resource should be valuable to users from both research and industry. We maintain a file containing all human UniProtKB/Swiss-Prot entries. This file is updated at every biweekly release of UniProt and can be downloaded by FTP download, HTTP download or by using a mirroring program which automatically retrieves the file at regular intervals.

Proper citation: Human Proteomics Initiative (RRID:SCR_002373) Copy   

•   Source: SciCrunch Registry

http://megasun.bch.umontreal.ca/ogmpproj.html

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on October 28,2025. It investigates mitochondrial genome diversity and evolution by systematically determining the complete mitochondrial DNA (mtDNA) sequences of a phylogenetically broad selection of protists. The mtDNAs of lower fungi and choanoflagellates are being analyzed by the Fungal Mitochondrial Genome Project (FMGP), a sister project to the OGMP.

Proper citation: Organelle Genomics (RRID:SCR_002137) Copy   

•   Source: SciCrunch Registry

http://pallab.serc.iisc.ernet.in/gester/

Database of intrinsic terminators of transcription that is comprized of >2,200,000 bacterial terminators identified from a total of 2036 chromosomes and 1508 plasmids. Information about structural parameters of individual terminators such as sequence, length of stem and loop, mismatches and gaps, U-trail, genomic coordinates and gene name and accession number is available in both tabular form and as a composite figure. Summary statistics for terminator profiles of whole genome can be also obtained. Raw data files for individual genomes can be downloaded (.zip files) for detailed investigations. Data is organized into different tiers such that users can fine-tune their search by entering name of the species, or taxon ID or genomes with a certain number of terminators. To visualize the occurrence of the terminators, an interactive map, with the resolution to single gene level, has been developed.

Proper citation: WebGeSTer DB (RRID:SCR_002165) Copy   

•   Source: SciCrunch Registry

http://bibiserv.techfak.uni-bielefeld.de/agt-sdp/

Database providing automatic test cases for protein-protein docking. A consensus-type approach is proposed processing the whole PDB and classifying protein structures into complexes and unbound proteins by combining information from three different approaches. Out of this classification test cases are generated automatically. All calculations were run on the database. The information stored is available via a web interface. The user can choose several criteria for generating his own subset out of the test cases, e.g. for testing docking algorithms. In unbound protein--protein docking, the complex of two proteins is predicted using the unbound conformations of the proteins (Halperin et al.,2002). For testing of docking algorithms, two unbound proteins which form a known complex have to be identified, so that the result of the docking algorithm can be compared to the known complex. For the identification of test cases, the structures taken from the PDB have to be classified as unbound proteins or complexes and unbound proteins with a 100% sequence identity to one complex part have to be searched. By now, most groups use handpicked test sets. The largest collection of test cases used so far is described by Chen et al. (Chen et al.,2003) and contains 31 test cases for unbound docking. Because of the exponential growth of available protein structures in the PDB, automatic generation of test cases will become more and more important in the future.

Proper citation: Automatic Generated Test-Sets Database for Protein-Protein Docking (RRID:SCR_002281) Copy   

•   Source: SciCrunch Registry