Tools   Select Another Resource Report Type

http://tomcat.esat.kuleuven.be/txtgate/

TXTGate is a literature index database and is part of an experimental platform to evaluate (combinations of) information extraction and indexing from a variety of biological annotation databases. It is designed towards the summarization and analysis of groups of genes based on text. By means of tailored vocabularies, selected textual fields and MedLine abstracts of LocusLink and SGD are indexed. Subclustering and links to external resources allow for an in-depth analysis of the resulting term profiles. You need to be registered in order to use the TXTGate application. Platform: Online tool

Proper citation: TXTGate (RRID:SCR_005812) Copy   

•   Source: SciCrunch Registry

http://www.utsouthwestern.edu/education/medical-school/departments/pathology/pathdb/classifi.html

THIS RESOURCE IS NO LONGER IN SERVICE, documented on July 10, 2012. Cluster Assignment for Biological Inference (CLASSIFI) is a data-mining tool that can be used to identify significant co-clustering of genes with similar functional properties (e.g. cellular response to DNA damage). Briefly, CLASSIFI uses the Gene Ontology gene annotation scheme to define the functional properties of all genes/probes in a microarray data set, and then applies a cumulative hypergeometric distribution analysis to determine if any statistically significant gene ontology co-clustering has occurred. Platform: Online tool

Proper citation: CLASSIFI - Cluster Assignment for Biological Inference (RRID:SCR_005752) Copy   

•   Source: SciCrunch Registry

http://www.btool.org/ADGO2

A web-based tool that provides composite interpretations for microarray data comparing two sample groups as well as lists of genes from diverse sources of biological information. It provides multiple gene set analysis methods for microarray inputs as well as enrichment analyses for lists of genes. It screens redundant composite annotations when generating and prioritizing them. It also incorporates union and subtracted sets as well as intersection sets. Users can upload their gene sets (e.g. predicted miRNA targets) to generate and analyze new composite sets.

Proper citation: ADGO (RRID:SCR_006343) Copy   

•   Source: SciCrunch Registry

http://tbdb.org

Database providing integrated access to genome sequence, expression data and literature curation for Tuberculosis (TB) that houses genome assemblies for numerous strains of Mycobacterium tuberculosis (MTB) as well assemblies for over 20 strains related to MTB and useful for comparative analysis. TBDB stores pre- and post-publication gene-expression data from M. tuberculosis and its close relatives, including over 3000 MTB microarrays, 95 RT-PCR datasets, 2700 microarrays for human and mouse TB related experiments, and 260 arrays for Streptomyces coelicolor. (July 2010) To enable wide use of these data, TBDB provides a suite of tools for searching, browsing, analyzing, and downloading the data.

Proper citation: Tuberculosis Database (RRID:SCR_006619) Copy   

•   Source: SciCrunch Registry

https://scicrunch.org/scicrunch/data/source/nlx_154697-7/search?q=*

Virtual database currently indexing interaction between genes and diseases from Online Mendelian Inheritance in Man (OMIM) and Comparative Toxicogenomics Database (CTD).

Proper citation: Integrated Gene-Disease Interaction (RRID:SCR_006173) Copy   

•   Source: SciCrunch Registry

https://www.jax.org/jax-mice-and-services/in-vivo-pharmacology/mouse-tumor-biology-database

Database supports use of mouse model system for human cancer by providing comprehensive resource for data and information on various tumor models.

Proper citation: Mouse Tumor Biology Database (RRID:SCR_006517) Copy   

•   Source: SciCrunch Registry

http://www.droidb.org

A gene and protein interactions database designed specifically for the model organism Drosophila including protein-protein, transcription factor-gene, microRNA-gene, and genetic interactions. For advanced searches and dynamic graphing capabilities the IM Browser and a DroID Cytoscape plugin are available.

Proper citation: DroID - Drosophila Interactions Database (RRID:SCR_006634) Copy   

•   Source: SciCrunch Registry

https://www.arb-silva.de

High quality ribosomal RNA databases providing comprehensive, quality checked and regularly updated datasets of aligned small (16S/18S, SSU) and large subunit (23S/28S, LSU) ribosomal RNA (rRNA) sequences for all three domains of life (Bacteria, Archaea and Eukarya). Supplementary services include a rRNA gene aligner, online tools for probe and primer evaluation and optimized browsing, searching and downloading on the website. The extensively curated SILVA taxonomy and the new non-redundant SILVA datasets provide an ideal reference for high-throughput classification of data from next-generation sequencing approaches. Alignment tool, SINA, is available for download as well as available for use online.

Proper citation: SILVA (RRID:SCR_006423) Copy   

•   Source: SciCrunch Registry

http://www.ncbi.nlm.nih.gov/books/NBK1116/

Provides clinically relevant and medically actionable information for inherited conditions in standardized journal-style format, covering diagnosis, management, and genetic counseling for patients and their families. Searchable book of expert-authored, peer-reviewed disease descriptions presented in standardized format and focused on clinically relevant and medically actionable information on diagnosis, management, and genetic counseling of patients and families with specific inherited conditions.

Proper citation: GeneReviews (RRID:SCR_006560) Copy   

•   Source: SciCrunch Registry

http://decipher.sanger.ac.uk/

Interactive database which incorporates a suite of tools designed to aid the interpretation of submicroscopic chromosomal imbalance. Used to enhance clinical diagnosis by retrieving information from bioinformatics resources relevant to the imbalance found in the patient. Contributing to the DECIPHER database is a Consortium, comprising an international community of academic departments of clinical genetics. Each center maintains control of its own patient data (which are password protected within the center''''s own DECIPHER project) until patient consent is given to allow anonymous genomic and phenotypic data to become freely viewable within Ensembl and other genome browsers. Once data are shared, consortium members are able to gain access to the patient report and contact each other to discuss patients of mutual interest, thus facilitating the delineation of new microdeletion and microduplication syndromes.

Proper citation: DECIPHER (RRID:SCR_006552) Copy   

•   Source: SciCrunch Registry

http://biogps.org/

An extensible and customizable gene annotation portal that emphasizes community extensibility and user customizability. It is a complete resource for learning about gene and protein function. Community extensibility reflects a belief that any BioGPS user should be able to add new content to BioGPS using the simple plugin interface, completely independently of the core developer team. User customizability recognizes that not all users are interested in the same set of gene annotation data, so the gene report layouts enable each user to define the information that is most relevant to them. Currently, BioGPS supports eight species: Human (Homo sapiens), Mouse (Mus musculus), Rat (Rattus norvegicus), Fruitfly (Drosophila melanogaster), Nematode (Caenorhabditis elegans), Zebrafish (Danio rerio), Thale-cress (Arabidopsis thaliana), Frog (Xenopus tropicalis), and Pig (Sus scrofa). BioGPS presents data in an ortholog-centric format, which allows users to display mouse plugins next to human ones. Our data for defining orthologs comes from NCBI's HomoloGene database.

Proper citation: BioGPS: The Gene Portal Hub (RRID:SCR_006433) Copy   

•   Source: SciCrunch Registry

http://wishart.biology.ualberta.ca/BacMap

An interactive visual database containing hundreds of fully labeled, zoomable, and searchable maps of bacterial genomes. It uses a visualization tool (CGView) to generate high-resolution circular genome maps from sequence feature information. Each map includes an interface that allows the image to be expanded and rotated. In the default view, identified genes are drawn to scale and colored according to coding directions. When a region of interest is expanded, gene labels are displayed. Each label is hyperlinked to a custom ''gene card'' which provides several fields of information concerning the corresponding DNA and protein sequences. Each genome map is searchable via a local BLAST search and a gene name/synonym search. A complete listing of the species and strains in the BacMap database is available on the BacMap homepage. Below each species/strain name is a list of the sequenced chromosomes and plasmids that are available. Some features of BacMap include: * Maps are available for 2023 bacterial chromosomes. * Each map supports zooming and rotation. * Map gene labels are hyperlinked to detailed textual annotations. * Maps can be explored manually, or with the help of BacMap''s built in text search and BLAST search. * A written synopsis of each bacterial species is provided. * Several charts illustrating the proteomic and genomic characteristics of each chromosome are available. * Flat file versions of the BacMap gene annotations, gene sequences and protein sequences can be downloaded. BacMap can be used to: * Obtain basic genome statistics. * Visualize the genomic context of genes. * Search for orthologues and paralogues in a genome of interest. * Search for conserved operon structure. * Look for gene content differences between bacterial species. * Obtain pre-calculated annotations for bacterial genes of interest.

Proper citation: BacMap: Bacterial Genome Atlas (RRID:SCR_006988) Copy   

•   Source: SciCrunch Registry

http://www.hgvs.org/dblist/glsdb.htm

Database of Locus Specific Mutation Databases. Fields include HGNC Gene symbol / OMIM No., Database name / Internet address, and Curators. If you wish to add an LSDB please go to the LSDB Submission Page.

Proper citation: HGVS Locus Specific Mutation Databases (RRID:SCR_006730) Copy   

•   Source: SciCrunch Registry

http://www.iiserpune.ac.in/~coee/histome/

Database of human histone variants, sites of their post-translational modifications and various histone modifying enzymes. The database covers 5 types of histones, 8 types of their post-translational modifications and 13 classes of modifying enzymes. Many data fields are hyperlinked to other databases (e.g. UnprotKB/Swiss-Prot, HGNC, OMIM, Unigene etc.). Additionally, this database also provides sequences of promoter regions (-700 TSS +300) for all gene entries. These sequences were extracted from the UCSC genome browser. Sites of post-translational modifications of histones were manually searched from PubMed listed literature. Current version contains information for about ~50 histone proteins and ~150 histone modifying enzymes. HIstome is a combined effort of researchers from two institutions, Advanced Center for Treatment, Research and Education in Cancer (ACTREC), Navi Mumbai and Center of Excellence in Epigenetics (CoEE), Indian Institute of Science Education and Research (IISER), Pune.

Proper citation: HIstome: The Histone Infobase (RRID:SCR_006972) Copy   

•   Source: SciCrunch Registry

http://www.pdgene.org/

The PDGene database aims to provide a comprehensive, unbiased and regularly updated collection of genetic association studies performed on Parkinson's disease (PD) phenotypes. Eligible publications are identified following systematic searches of scientific literature databases, as well as the table of contents of journals in genetics, neurology, and psychiatry. The database can be searched either by a variety of dropdown menus or by specific keywords. For each gene, summary overviews are provided displaying key characteristics for each publication, including links to genotype distributions of the polymorphisms studied, random-effects allelic meta-analyses, and funnel plots for an assessment of publication bias. The PDGene database, developed by Massachusetts General Hospital/Harvard Medical School, The Michael J. Fox Foundation and the Alzheimer Research Forum, is supported by a grant from The Michael J. Fox Foundation in partnership with the Alzheimer Research Forum.

Proper citation: PDGene - A database for Parkinsons disease genetic association studies (RRID:SCR_006666) Copy   

•   Source: SciCrunch Registry

http://www.szgene.org/

The SZGene database provides a comprehensive, unbiased and regularly updated field synopsis of genetic association studies performed in schizophrenia. In addition, hundreds of up-to-date meta-analyses are available for all eligible polymorphisms with sufficient data. Eligible publications are identified following systematic searches of scientific literature databases, as well as the table of contents of journals in genetics and psychiatry. The database can be searched either by a variety of dropdown menus or by specific keywords. For each gene, summary overviews are provided displaying key characteristics for each publication, including links to genotype distributions of the polymorphisms studied, random-effects allelic meta-analyses, and funnel plots for an assessment of publication bias.

Proper citation: Schizophrenia Research Forum: Published Candidate Genes for Schizophrenia (RRID:SCR_006938) Copy   

•   Source: SciCrunch Registry

http://datf.cbi.pku.edu.cn/

Database that collects all arabidopsis transcription factors (totally 1922 Loci; 2290 Gene Models) and classifies them into 64 families. It uses not only locus (gene), but also gene model (transcript, protein) and the detail information is for each gene model not for locus. It adds multiple alignment of the DNA-binding domain of each family, Neighbor-Joining phylogenetic tree of each family, the GO annotation, homolog with the Database of Rice Transcription Factors (DRTF). It also keeps old information items such as the unique cloned and sequenced information of about 1200 transcription factors, protein domains, 3D structure information with BLAST hits against PDB, predicted Nuclear Location Signals, UniGene information, as well as links to literature reference.

Proper citation: Database of Arabidopsis Transcription Factors (RRID:SCR_007101) Copy   

•   Source: SciCrunch Registry

http://www.epilepsygenes.org/page/show/homepage

The Epilepsy Genetic Association Database (epiGAD) is an online repository of data relating to genetic association studies in the field of epilepsy. It summarizes the results of both published and unpublished studies, and is intended as a tool for researchers in the field to keep abreast of recent studies, providing a bird''s eye view of this research area. The goal of epiGAD is to collate all association studies in epilepsy in order to help researchers in this area identify all the available gene-disease associations. Finally, by including unpublished studies, it hopes to reduce the problem of publication bias and provide more accurate data for future meta-analyses. It is also hoped that epiGAD will foster collaboration between the different epilepsy genetics groups around the world, and faciliate formation of a network of investigators in epilepsy genetics. There are 4 databases within epiGAD: - the susceptibility genes database - the epilepsy pharmacogenetics database - the meta-analysis database - the genome-wide association studies (GWAS) database The susceptibility genes database compiles all studies related to putative epilepsy susceptibility genes (eg. interleukin-1-beta in TLE), while the pharmacogenetics studies in epilepsy (eg. ABCB1 studies) are stored in ''phamacogenetics''. The meta-analysis database compiles all existing published epilepsy genetic meta-analyses, whether for susceptibility genes, or pharmacogenetics. The GWAS database is currently empty, but will be filled once GWAS are published. Sponsors: The epiGAD website is supported by the ILAE Genetics Commission.

Proper citation: Epilepsy Genetic Association Database (RRID:SCR_006840) Copy   

•   Source: SciCrunch Registry

http://crcview.hegroup.org/

Web-based microarray data analysis and visualization system powered by CRC, or Chinese Restaurant cluster, a Dirichlet process model-based clustering algorithm recently developed by Dr. Steve Qin. It also incorporates several gene expression analysis programs from Bioconductor, including GOStats, genefilter, and Heatplus. CRCView also installs from the Bioconductor system 78 annotation libraries of microarray chips for human (31), mouse (24), rat (14), zebrafish (1), chicken (1), Drosophila (3), Arabidopsis (2), Caenorhabditis elegans (1), and Xenopus Laevis (1). CRCView allows flexible input data format, automated model-based CRC clustering analysis, rich graphical illustration, and integrated Gene Ontology (GO)-based gene enrichment for efficient annotation and interpretation of clustering results. CRC has the following features comparing to other clustering tools: 1) able to infer number of clusters, 2) able to cluster genes displaying time-shifted and/or inverted correlations, 3) able to tolerate missing genotype data and 4) provide confidence measure for clusters generated. You need to register for an account in the system to store your data and analyses. The data and results can be visited again anytime you log in.

Proper citation: CRCView (RRID:SCR_007092) Copy   

•   Source: SciCrunch Registry

http://funspec.med.utoronto.ca/

FunSpec is a web-based tool for statistical evaluation of groups of genes and proteins (e.g. co-regulated genes, protein complexes, genetic interactors) with respect to existing annotations, including GO terms. FunSpec (an acronym for Functional Specification) inputs a list of yeast gene names, and outputs a summary of functional classes, cellular localizations, protein complexes, etc. that are enriched in the list. The classes and categories evaluated were downloaded from the MIPS Database and the GO Database . In addition, many published datasets have been compiled to evaluate enrichment against. Hypertext links to the publications are given. The p-values, calculated using the hypergeometric distribution, represent the probability that the intersection of given list with any given functional category occurs by chance. The Bonferroni-correction divides the p-value threshold, that would be deemed significant for an individual test, by the number of tests conducted and thus accounts for spurious significance due to multiple testing over the categories of a database. After the Bonferroni correction, only those categories are displayed for which the chance probability of enrichment is lower than: p-value/#CD where #CD is the number of categories in the selected database. Without the Bonferroni Correction, all categories are displayed for which the same probability of enrichment is lower than: p-value threshold in an individual test Note that many genes are contained in many categories, especially in the MIPS database (which are hierarchical) and that this can create biases for which FunSpec currently makes no compensation. Also the databases are treated as independent from one another, which is really not the case, and each is searched seperately, which may not be optimal for statistical calculations. Nonetheless, we find it useful for sifting through the results of clustering analysis, TAP pulldowns, etc. Platform: Online tool

Proper citation: FunSpec (RRID:SCR_006952) Copy   

•   Source: SciCrunch Registry