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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   

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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   

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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   

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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   

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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   

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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   

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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   

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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   

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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   

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http://plantta.jcvi.org/

The TIGR database is a collection of plant transcript sequences. Transcript assemblies are searchable using BLAST and accession number. The construction of plant transcript assemblies (TAs) is similar to the TIGR gene indices. The sequences that are used to build the plant TAs are expressed transcripts collected from dbEST (ESTs) and the NCBI GenBank nucleotide database (full length and partial cDNAs). "Virtual" transcript sequences derived from whole genome annotation projects are not included. All plant species for which more than 1,000 ESTs or cDNA sequences are available are included in this project. TAs are clustered and assembled using the TGICL tool (Pertea et al., 2003), Megablast (Zhang et al., 2000) and the CAP3 assembler (Huang and Madan, 1999). TGICL is a wrapper script which invokes Megablast and CAP3. Sequences are initially clustered based on an all-against-all comparisons using Megablast. The initial clusters are assembled to generate consensus sequences using CAP3. Assembly criteria include a 50 bp minimum match, 95% minimum identity in the overlap region and 20 bp maximum unmatched overhangs. Any EST/cDNA sequences that are not assembled into TAs are included as singletons. All singletons retain their GenBank accession numbers as identifiers. Plant TA identifiers are of the form TAnumber_taxonID, where number is a unique numerical identifier of the transcript assembly and taxonID represents the NCBI taxon id. In order to provide annotation for the TAs, each TA/singleton was aligned to the UniProt Uniref database. For release 1 TAs, a masked version of the Uniref90 database was used. For release 2 and onwards, a masked version of the UniRef100 database is used. Alignments were required to have at least 20% identity and 20% coverage. The annotation for the protein with the best alignment to each TA or singleton was used as the annotation for that sequence. Additionally, the relative orientation of each TA/singleton to the best matching protein sequence was used to determine the orientation of each TA/singleton. Some sequences did not have alignments to the protein database that met our quality criteria, and those sequences have neither annotation nor orientation assignments. The release number for the plant TAs refers to the release version for a particular species. For the initial build, all TA sets are of version 1. Subsequent TA updates for new releases will be carried out when the percentage increase of the EST and cDNA counts exceeds 10% of the previous release and when the increase contains more than 1,000 new sequences. New releases will also include additional plant species with more than 1,000 EST or cDNA sequences that have become publicly available.

Proper citation: TIGR Plant Transcript Assembly database (RRID:SCR_005470) Copy   

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http://oger.tu-bs.de/strepto_db

A database for comparative genomics of group A and group B streptococci. It is based on OGeR (Open Genome Resource for comparative analysis of prokaryotic genomes) and includes all sequenced GAS and GBS strains and serovars available as EMBL genome review or NCBI GenBank files. Strepto-DB identifies the homologous proteins deduced from the genomes of interest. It allows for the elucidation of the GAS and GBS core- and pan-genomes via genome-wide comparisons. Moreover, an intergenic region analysis tool provides alignments and predictions for transcription factor binding sites in the non-coding sequences. An interactive genome browser visualizes functional annotations. Strepto-DB (http://oger.tu-bs.de/strepto_db) was created by the use of OGeR, the Open Genome Resource for comparative analysis of prokaryotic genomes. OGeR is a newly developed open source database and tool platform for the web-based storage, distribution, visualization and comparison of prokaryotic genome data. The system automatically creates the dedicated relational database and web interface and imports an arbitrary number of genomes derived from standardized genome files.

Proper citation: Strepto-DB (RRID:SCR_005196) Copy   

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http://tabit.ucsd.edu/sdec/

A next-generation web-based application that aims to provide an integrated solution for both visualization and analysis of deep-sequencing data, along with simple access to public datasets.

Proper citation: Systems Transcriptional Activity Reconstruction (RRID:SCR_005622) Copy   

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http://www.youtube.com/user/sfnvideo

The Society for Neuroscience (SfN) is a nonprofit membership organization of scientists and physicians who study the brain and nervous system. SFNVideo - YouTube are videos uploaded to YouTube by the Society for Neuroscience (SfN). Since inception in 1969, the Society has grown from 500 members to more than 41,000. Today, SfN is the world''s largest organization of scientists and physicians devoted to advancing understanding of the brain and nervous system.

Proper citation: sfnvideo - YouTube (RRID:SCR_005463) Copy   

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http://www.gene-regulation.com/pub/databases.html#transfac

Manually curated database of eukaryotic transcription factors, their genomic binding sites and DNA binding profiles. Used to predict potential transcription factor binding sites.

Proper citation: TRANSFAC (RRID:SCR_005620) Copy   

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http://www.tigr.org/tdb/e2k1/plant.repeats

It assists in the compilation and identification of repeat sequences in plant genomes. All of the repetitive sequences in the database are coded for the convenience of future analyses. In plants, ploidy levels and repetitive sequences contribute significantly to genome size. A number of different repetitive sequences have been reported in the plant genome and these can be classified into super-classes, classes, and subclasses based on structure and sequence composition. The transposable element (TEs) super-class includes retrotransposons, transposons, and miniature inverted-repeat transposable elements (MITEs). Other repetitive sequences are associated the centromere and telomere. Another super-class of repetitive sequences are rDNAs which encode the structural RNA components of ribosomes.

Proper citation: Plant Repeat Databases (RRID:SCR_005460) Copy   

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https://www.youtube.com/user/iniusc

Videos uploaded to YouTube by the Laboratory of Neuro Imaging (LONI). The Laboratory of Neuro Imaging at UCLA strives to improve our understanding of the brain in health and disease. LONI is a leader in the development of advanced computational algorithms and scientific approaches for the comprehensive and quantitative mapping of brain structure and function.

Proper citation: Laboratory of Neuro Imaging - YouTube (RRID:SCR_005462) Copy   

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http://www.hematology.org/Publications/Videos/

THIS RESOURCE IS NO LONGER IN SERVICE, documented on August 18, 2016. ASH's video library includes a number of films produced on various topics, including ASH''s history and award winners, Society programs such as the Clinical Research Training Institute, and a trailer and clips from the hematology documentary Blood Detectives, which aired on Discovery Health. These videos were created for educational purposes, and we encourage members of the hematology community to share them with others.

Proper citation: ASH Video Library (RRID:SCR_005777) Copy   

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http://microrna.osu.edu/.UCbase4

THIS RESOURCE IS NO LONGER IN SERVICE, documented on July 16, 2013. UCbase & miRfunc is a database of (i) human, mouse and rat microRNAs and (ii) Ultraconserved elements providing information about function, expression and correlation between these classes of non-coding RNAs and the disorders related to their aberrant expression. The genomics interface allows the user to explore where whole-genome collections of miRNAs and UCRs are located with respect to annotation sets such as band, disorders and known genes. The Blast interface provides a web tool for matching miRNAs/UCRs elements against any given sequence and providing specific functional information on the results. 481 Ultraconserved sequences (UCRs) longer than 200 bases were discovered in the genomes of human, mouse and rat. These are DNA sequences showing 100 percent identity among the human, mouse and rat genomes. UCRs are frequently located at genomic regions involved in cancer, differentially expressed in human leukemias and carcinomas and in some instances regulated by microRNAs (miRNAs), the most extensively studied category of non-coding RNAs (ncRNAs). Here we present the first database which links UCRs and miRNAs with the related human disorders and genomic properties.

Proper citation: UCbase & miRfunc: Ultraconserved Sequences and miRNA Funciton Database (RRID:SCR_005771) 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://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   

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