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http://solgenomics.net/

A clade oriented, community curated database containing genomic, genetic, phenotypic and taxonomic information for plant genomes. Genomic information is presented in a comparative format and tied to important plant model species such as Arabidopsis. SGN provides tools such as: BLAST searches, the SolCyc biochemical pathways database, a CAPS experiment designer, an intron detection tool, an advanced Alignment Analyzer, and a browser for phylogenetic trees. The SGN code and database are developed as an open source project, and is based on database schemas developed by the GMOD project and SGN-specific extensions.

Proper citation: SGN (RRID:SCR_004933) Copy   

•   Source: SciCrunch Registry

http://spliceinfo.mbc.nctu.edu.tw/

A database of mRNA alternative splicing in the human genome. Within it, several modes of mRNA alternative splicing, such as exon skipping, alternative 5''-splicing sites, alternative 3''-splicing sites and mutually exclusive exons are computationally derived and extracted. Finally, for each type of alternative splicing, the flanking intronic sequences are collected and then exploited by motif discovery tools. The tissue-specific information and gene functionalities that correspond to the selected regions are also considered. The database provides a means of investigating alternative splicing and can be used for identifying alternative splicing - related motifs, such as the exonic splicing enhancer (ESE), the exonic splicing silencer (ESS) and other intronic splicing motifs.

Proper citation: SpliceInfo (RRID:SCR_005100) Copy   

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https://scicrunch.org/scicrunch/data/source/nlx_154697-6/search?q=*&l=

A virtual database currently indexing authoritative information on disease and treatment options from NINDS Disorder List and PubMed Health.

Proper citation: Integrated Disease (RRID:SCR_004892) Copy   

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http://exon.cshl.org/cgi-bin/atprobe/atprobe.pl

Arabidopsis thaliana promoter binding element database that focuses on specific binding elements on known genes, found with experimental methods.

Proper citation: AtProbe (RRID:SCR_005412) Copy   

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http://cgi-www.daimi.au.dk/cgi-chili/datfap/frontdoor.py

A database of transcription factors from 13 plant species, and PCR primers for around 90% of them.

Proper citation: DATFAP (RRID:SCR_005413) Copy   

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http://bioinfozen.uncc.edu/tfindit/

A database and web service for structural bioinformatics studies of transcription factor (TF)-DNA interactions. Various datasets can be generated based on one or more search options specified by users.

Proper citation: TFinDIT (RRID:SCR_005411) Copy   

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http://www.dbs.ifi.lmu.de/~bundschu/LHGDN.html

A text mining derived database with focus on extracting and classifying gene-disease associations with respect to several biomolecular conditions. It uses a machine learning based algorithm to extract semantic gene-disease relations from a textual source of interest. The semantic gene-disease relations were extracted with F-measures of 78. More specifically, the textual source utilized here originates from Entrez Gene''''s GeneRIF (Gene Reference Into Function) database (Mitchell, et al., 2003). LHGDN was created based on a GeneRIF version from March 31st, 2009, consisting of 414241 phrases. These phrases were further restricted to the organism Homo sapiens, which resulted in a total of 178004 phrases. We benchmark our approach on two different tasks. The first task is the identification of semantic relations between diseases and treatments. The available data set consists of manually annotated PubMed abstracts. The second task is the identification of relations between genes and diseases from a set of concise phrases, so-called GeneRIF (Gene Reference Into Function) phrases. In our experimental setting, we do not assume that the entities are given, as is often the case in previous relation extraction work. Rather the extraction of the entities is solved as a subproblem. Compared with other state-of-the-art approaches, we achieve very competitive results on both data sets. To demonstrate the scalability of our solution, we apply our approach to the complete human GeneRIF database. The resulting gene-disease network contains 34758 semantic associations between 4939 genes and 1745 diseases. The gene-disease network is publicly available as a machine-readable RDF graph. We extend the framework of Conditional Random Fields towards the annotation of semantic relations from text and apply it to the biomedical domain. Our approach is based on a rich set of textual features and achieves a performance that is competitive to leading approaches. The model is quite general and can be extended to handle arbitrary biological entities and relation types. The resulting gene-disease network shows that the GeneRIF database provides a rich knowledge source for text mining.

Proper citation: Literature-derived human gene-disease network (RRID:SCR_005653) Copy   

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http://www.phenologs.org/

Database for identifying orthologous phenotypes (phenologs). Mapping between genotype and phenotype is often non-obvious, complicating prediction of genes underlying specific phenotypes. This problem can be addressed through comparative analyses of phenotypes. We define phenologs based upon overlapping sets of orthologous genes associated with each phenotype. Comparisons of >189,000 human, mouse, yeast, and worm gene-phenotype associations reveal many significant phenologs, including novel non-obvious human disease models. For example, phenologs suggest a yeast model for mammalian angiogenesis defects and an invertebrate model for vertebrate neural tube birth defects. Phenologs thus create a rich framework for comparing mutational phenotypes, identify adaptive reuse of gene systems, and suggest new disease genes. To search for phenologs, go to the basic search page and enter a list of genes in the box provided, using Entrez gene identifiers for mouse/human genes, locus ids for yeast (e.g., YHR200W), or sequence names for worm (e.g., B0205.3). It is expected that this list of genes will all be associated with a particular system, trait, mutational phenotype, or disease. The search will return all identified model organism/human mutational phenotypes that show any overlap with the input set of the genes, ranked according to their hypergeometric probability scores. Clicking on a particular phenolog will result in a list of genes associated with the phenotype, from which potential new candidate genes can identified. Currently known phenotypes in the database are available from the link labeled ''Find phenotypes'', where the associated gene can be submitted as queries, or alternately, can be searched directly from the link provided.

Proper citation: Phenologs (RRID:SCR_005529) Copy   

•   Source: SciCrunch Registry

http://www.youtube.com/user/WholeBrainCatalog?feature=autoshare

Videos uploaded to YouTube by the Whole Brain Catalog.

Proper citation: WholeBrainCatalog's Channel - YouTube (RRID:SCR_005436) Copy   

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http://med.emory.edu/ADRC/research/core_neurology_database.html

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on June 9, 2025. A database which retains extensive clinical information about study subjects recruited by the Alzheimer's Disease Research Center Clinical Core, as well as other individuals with neurological diseases. In addition to clinical information, the database has basic demographics, medical history (including risk factors such as smoking), and a detailed family history from all subjects. Some entries have neuropsychological measures. Users can access a Summary Database which contains the most commonly requested variables. A data dictionary describing the variables in the Summary Database is available.

Proper citation: Emory Neurology Database (RRID:SCR_005277) Copy   

•   Source: SciCrunch Registry

http://maize.tigr.org

A database of maize genomic sequences, searchable by BLAST, by repeat sequences, and sequence name, gene name, locus, or other landmark. TIGR is a member of the Consortium for Maize Genomics. The Consortium received a funding award from the National Science Foundation in September 2002, to evaluate two gene-enrichment techniques, methylation filtration and high Cot selection, to sequence the maize 'genespace'. Draft assemblies of 287 maize BAC clones selected by the maize community and the Consortium were also produced in the Consortium project. We have recently developed an improved version of the TIGR annotation pipeline optimized for maize genomic assemblies. The latest maize genomic assemblies obtained by gene-enrichment (AZM5) and the 287 maize draft BAC assemblies have been annotated using the improved pipeline. Gene model annotation and functional annotation can be accessed via the TIGR maize BLAST server or the TIGR maize gbrowse display. The first version of the Maize Repeat Database contained 485 characterized maize repeat sequences from the TIGR Cereal Repeat Database. To these we added repetitive sequences downloaded from GenBank and a file of retrotransposon sequences compiled by Phillip SanMiguel (Purdue University). In addition we searched our maize genomic assemblies (AZMs) to identify new repeats. Any sequence within an AZM that showed at least 80% identity over a minimum stretch of 100 bp with an entry in the TIGR Cereal Repeat Database was coded accordingly and added to the Maize Repeat Database.

Proper citation: TIGR Maize database (RRID:SCR_005431) Copy   

•   Source: SciCrunch Registry

http://rnp.uthscsa.edu/rnp/tmRDB/tmRDB.html

The tmRDB is a tool in the study of the structures and functions of the tmRNA (earlier called 10S RNA). As the name implies, tmRNA has properties of tRNA and mRNA combined in a single molecule. The tmRDB provides aligned, annotated and phylogenetically ordered tmRNA sequences. The alignments of the sequences represent conserved secondary structure elements where each base pair is proven by comparative sequence analysis. Where possible, we established direct links to primary sources. We acknowledge support provided by the National Institutes of Health and the Danish Technical Research Council. tRNA, mRNA, trans-translation, rescue, ribosome, broken mRNA, bacteria, mitochondria chloroplasts, cyanelles, bacteriphage, phylogenetic

Proper citation: tmRNA Database (RRID:SCR_005540) Copy   

•   Source: SciCrunch Registry

http://trbase.ex.ac.uk/

This TRbase is a relational tandem repeat database that relates tandem repeats to gene locations and disease genes of the human genome. The TRbase stores both perfect and imperfect repeats of 1 to 2000 bp unit lengths that were identified using the Tandem Repeat Finder program. Disease information for all 24 chromosomes was retrieved from the Online Mendelian Inheritance in Man (OMIM) database. There are five main search forms by which the user may query the database: 1. The Advanced tandem repeat search: This allows a complete search for tandem repeats using a combination of criteria, such as total tandem repeat length, repeat unit length, copy number of the repeats, percentage matches and the consensus repeat pattern. On submission, the number of repeats and the detailed tandem repeat characteristics of each repeat that match the user query are tabulated. 2. The Main search: This relates tandem repeat data to genes and diseases. The user may specify a gene of interest to view details of all repeats associated with it or search for tandem repeats present in a particular disease by entering the name/keyword for the disease or the MIM number of the disease gene. 3. The Composite search: This more advanced search allows the user to query specifically for repeats present in exons, introns or intergenic regions of a gene or disease gene. 4. The Gene Search: Further information on genes can be available by a simple gene name search on this page. 5. The Disease search: This allows extensive information on disease genes on all chromosomes of the human genome. Searching for a MIM number, or keyword searches specifying the features of the disease, will retrieve the information on the disease and the chromosome in which the disease gene occurs. Each entry retrieved is linked to the OMIM database for detailed literature and gene map information on the disease.

Proper citation: TRbase: A Database Of Tandem Repeats In The Human Genome (RRID:SCR_005658) Copy   

•   Source: SciCrunch Registry

http://tmbeta-genome.cbrc.jp/annotation/

A collection of amino acid sequences for all the completed genomes and the annotated trans beta-barrel membrane proteins (TMBs) using different discrimination algorithms. For each genome, the calculations have been performed with statistical methods and machine learning techniques and the results are accumulated in the database. TMBETA-GENOME has the feasibility of selecting the organism from the three kingdoms of life, archaea, bacteria and eukaryote. Further, users have the option to select any of the methods or their combinations, and display the results with/without amino acid sequence information.

Proper citation: TMBETA-GENOME- Annotation of Beta-Barrel Membrane Proteins in Genomic Sequences (RRID:SCR_005538) Copy   

•   Source: SciCrunch Registry

http://tandem.bu.edu/cgi-bin/trdb/trdb.exe

A public repository of information on tandem repeats in genomic DNA and contains a variety of tools for their analysis. These currently include the Tandem Repeats Finder algorithm, query and filtering capabilities for finding particular repeats of interest, repeat clustering algorithms based on sequence similarity, polymorphism prediction based on common patterns of mutation, PCR primer selection, and data download in a variety of formats. In addition, TRDB serves as a centralized research workbench, provides storage space for results of analysis, and permits collaborators to privately share their data and analysis.

Proper citation: Tandem Repeats Database (RRID:SCR_005659) Copy   

•   Source: SciCrunch Registry

http://www.biosino.org/bodyfluid/

A database of bodily fluid proteome data. It contains information on proteins from humanplasma/serum, urine, cerebrospinal fluid, saliva, bronchoalveolar lavage fluid, synovial fluid, nipple aspirate fluid, tear fluid, seminal fluid, human milk, and amniotic fluid. Our body fluid protein database, Sys-BodyFluid, contains 11 body fluid proteomes, including plasma/serum, urine, cerebrospinal fluid, saliva, bronchoalveolar lavage fluid, synovial fluid, nipple aspirate fluid, tear fluid, seminal fluid, human milk, and amniotic fluid. Over 10,000 proteins are included in the Sys-BodyFluid. These body fluid proteome data come from 50 peer-review publications of different laboratories all over the world. Protein annotation are provided including protein description, Gene ontology, Domain information, Protein sequence and involved pathway. User can access the proteome data by protein name, protein accession number, sequence similarity. In addition, user could perform query cross different body fluids to get more comprehensive understanding. The difference and similarity between these 11 body fluids are also analyzed. Thus , the Sys-BodyFluid database could serve as a reference database for body fluid research and disease proteomics. plasm, serum, urine, cerebrospinal fluid, saliva, bronchoalveolar lavage fluid, synovial fluid, nipple aspirate fluid, tear fluid, seminal fluid, human milk, and amniotic fluid, protein, proteomics

Proper citation: Sys-BodyFluid (RRID:SCR_005335) Copy   

•   Source: SciCrunch Registry

http://www.knockoutmouse.org/

Database of the international consortium working together to mutate all protein-coding genes in the mouse using a combination of gene trapping and gene targeting in C57BL/6 mouse embryonic stem (ES) cells. Detailed information on targeted genes is available. The IKMC includes the following programs: * Knockout Mouse Project (KOMP) (USA) ** CSD, a collaborative team at the Children''''s Hospital Oakland Research Institute (CHORI), the Wellcome Trust Sanger Institute and the University of California at Davis School of Veterinary Medicine , led by Pieter deJong, Ph.D., CHORI, along with K. C. Kent Lloyd, D.V.M., Ph.D., UC Davis; and Allan Bradley, Ph.D. FRS, and William Skarnes, Ph.D., at the Wellcome Trust Sanger Institute. ** Regeneron, a team at the VelociGene division of Regeneron Pharmaceuticals, Inc., led by David Valenzuela, Ph.D. and George D. Yancopoulos, M.D., Ph.D. * European Conditional Mouse Mutagenesis Program (EUCOMM) (Europe) * North American Conditional Mouse Mutagenesis Project (NorCOMM) (Canada) * Texas A&M Institute for Genomic Medicine (TIGM) (USA) Products (vectors, mice, ES cell lines) may be ordered from the above programs.

Proper citation: International Knockout Mouse Consortium (RRID:SCR_005574) Copy   

•   Source: SciCrunch Registry

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://ssd.rbvi.ucsf.edu/

The SSD has been developed to address the need for resources and tools for understanding large sets of superpositions in order to understand evolutionary relationships and to make predictions of function. We have therefore created the Structure Superposition Database (SSD) for accessing, viewing and understanding large sets of structure superposition data. It contains the results of pairwise, all-by-all superpositions of a representative set of 115 (beta/alpha) barrel structures (TIM barrels). The initial implementation of the SSD contains the results of pairwise, all-by-all superpositions of a representative set of 115 (/alpha)8 barrel structures (TIM barrels). Future plans call for extending the database to include representative structure superpositions for many additional folds. The SSD can be browsed with a user interface module developed as an extension to Chimera, an extensible molecular modeling program. Features of the user interface module facilitate viewing multiple superpositions together.

Proper citation: Structure Superposition Database (RRID:SCR_005236) Copy   

•   Source: SciCrunch Registry

http://transpogene.tau.ac.il/

A publicly available database of Transposed elements (TEs) which are located within protein-coding genes of 7 organisms: human, mouse, chicken, zebrafish, fruilt fly, nematode and sea squirt. Using TranspoGene the user can learn about the many aspects of the effect these TEs have on their hosting genes, such as: exonization events (including alternative splicing-related data), insertion of TEs into introns, exons, and promoters, specific location of the TE over the gene, evolutionary divergence of the TE from its consensus sequence and involvement in diseases. TranspoGene database is quickly searchable through its website, enables many kinds of searches and is available for download. TranspoGene contains information regarding specific type and family of the TEs, genomic and mRNA location, sequence, supporting transcript accession and alignment to the TE consensus sequence. The database also contains host gene specific data: gene name, genomic location, Swiss-Prot and RefSeq accessions, diseases associated with the gene and splicing pattern. The TranspoGene and microTranspoGene databases can be used by researchers interested in the effect of TE insertion on the eukaryotic transcriptome.

Proper citation: TranspoGene (RRID:SCR_005634) Copy   

•   Source: SciCrunch Registry