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http://www.plantontology.org

Ontology and database that links plant anatomy, morphology and growth and development to plant genomics data.Plant Ontology Consortium develops, curates and shares controlled vocabularies (ontologies) that describe plant structures and growth and developmental stages, providing semantic framework for meaningful cross species queries across databases. PO is under active development to expand to encompass terms and annotations from all plants.

Proper citation: Plant Ontology (RRID:SCR_006494) Copy   

•   Source: SciCrunch Registry

http://www.rarechromo.org/html/home.asp

Unique is a source of information and support to families and individuals affected by any rare chromosome disorder and to the professionals who work with them. Unique is a UK-based charity but welcomes members worldwide. Unique''''s Karyotype Database allows users to search the Registered Chromosome Disorders by chromosome, arm and disorder. You may have been given a diagnosis or indication of a chromosome disorder by a geneticist or other medical professional and they may have used a medical term which is unfamiliar to you. So to help you decide if Unique is the appropriate organization for you, we thought it would be useful to describe the different categories of rare chromosome disorder. Rare chromosome disorders can be grouped as structural disorders, numerical disorders and other miscellaneous disorders. Unique: * acts as an international family support group * produces a newsletter three times each year * works to promote awareness of rare chromosome disorders * arranges for families to assist in research into rare chromosome disorders * links families whose children have similar clinical and/or practical problems * works to ensure that the public at large are aware of rare chromosome disorders * works to raise funds to support the group activities and produce literature to make others more aware of our children''''s conditions * assists relevant research projects and the centralisation of information, at all times observing the need for total confidentiality * sets up local groups throughout the UK for families affected by any rare chromosome disorders and to give support and encouragement to each other * develops and maintains a comprehensive computerised database detailing the life-time effects of specific chromosome disorders on affected members * aims to hold an annual conference where families and relevant specialists can meet and be informed of the latest medical, technical and practical developments * liaises and works in co-operation, with other similar support groups and professionals world-wide for the benefit of families and individuals affected by rare chromosome disorders * ensures that hospitals, doctors, health authorities, genetic clinics and other professionals are aware of the group so that we may have early contact with families where required Membership of Unique is free but the group receives no government funding and is heavily reliant on donations and fundraising to continue its work. Please help us in whatever way you can.

Proper citation: Unique (RRID:SCR_006492) Copy   

•   Source: SciCrunch Registry

http://www.informatics.jax.org/mgihome/GO/project.shtml

This resource is part of the Gene Ontology Consortium which seeks to provide controlled vocabularies for the description of the molecular function, biological process, and cellular component of gene products. These terms are to be used as attributes of gene products by collaborating databases, facilitating uniform queries across them. GO team members at MGI participate in ontology development, outreach, and functional curation of mouse gene products. The GO vocabularies have a hierarchical structure that permits a range of detail from high-level, broadly descriptive terms to very low level, highly specific terms. This broad range is useful both in annotating genes and in searching for gene information using these terms as search criteria. GO terms are defined, allowing all databases to use the terms consistently and properly. GO annotations in the databases additionally include the publication reference which allowed the association to be made and an evidence statement citing how the association was determined.

Proper citation: Mouse Genome Informatics: The Gene Ontology Project (RRID:SCR_006447) Copy   

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http://www.LOVD.nl/

Freely available tool for Gene-centered collection and display of DNA variations. It also provides patient-centered data storage and storage of Next Generation Sequencing (NGS) data, even of variants outside of genes. Please note that LOVD provides a system for storage of information on genes and allelic variants. To obtain information about any genes or variants, do not download the LOVD package. This information should be obtained from the respective databases, http://www.lovd.nl/2.0/index_list.php In total: 2,507,027 variants (2,208,937 unique) in 170,935 individuals in 62619 genes in 88 LOVD installations. (Aug. 2013) LOVD 3.0 shared installation, http://databases.lovd.nl/shared/genes To maintain a high quality of the data stored, LOVD connects with various resources, like HGNC, NCBI, EBI and Mutalyzer. You can download LOVD in ZIP and GZIPped TARball formats.

Proper citation: Leiden Open Variation Database (RRID:SCR_006566) Copy   

•   Source: SciCrunch Registry

http://gmd.mpimp-golm.mpg.de/

It facilitates the search for and dissemination of mass spectra from biologically active metabolites quantified using Gas chromatography (GC) coupled to mass spectrometry (MS). Use the Search Page to search for a compound of your interest, using the name, mass, formula, InChI etc. as query input. Additionally, a Library Search service enables the search of user submitted mass spectra within the GMD. In parallel to the library search, a prediction of chemical sub-groups is performed. This approach has reached beta level and a publication is currently under review. Using several sub-group specific Decision Trees (DTs), mass spectra are classified with respect to the presence of the chemical moieties within the linked (unknown) compound. Prediction of functional groups (ms analysis) facilitates the search of metabolites within the GMD by means of user submitted GC-MS spectra consisting of retention index (n-alkanes, if vailable) and mass intensities ratios. In addition, a functional group prediction will help to characterize those metabolites without available reference mass spectra included in the GMD so far. Instead, the unknown metabolite is characterized by predicted presence or absence of functional groups. For power users this functionality presented here is exposed as soap based web services. Functional group prediction of compounds by means of GC-EI-MS spectra using Microsoft analysis service decision trees All currently available trained decision trees and sub-structure predictions provided by the GMD interface. Table describes the functional group, optional use of an RI system, record date of the trained decision tree, number of MSTs with proportion of MSTs linked to metabolites with the functional group present for each tree. Average and standard deviation of the 50-fold CV error, namely the ratio false over correctly sorted MSTs in the trained DT, are listed. The GMD website offers a range of mass spectral reference libraries to academic users which can be downloaded free of charge in various electronic formats. The libraries are constituted by base peak normalized consensus spectra of single analytes and contain masses in the range 70 to 600 amu, while the ubiquitous mass fragments typically generated from compounds carrying a trimethylsilyl-moiety, namely the fragments at m/z 73, 74, 75, 147, 148, and 149, were excluded.

Proper citation: GMD (RRID:SCR_006625) Copy   

•   Source: SciCrunch Registry

http://dictybase.org/

Model organism database for the social amoeba Dictyostelium discoideum that provides the biomedical research community with integrated, high quality data and tools for Dictyostelium discoideum and related species. dictyBase houses the complete genome sequence, ESTs, and the entire body of literature relevant to Dictyostelium. This information is curated to provide accurate gene models and functional annotations, with the goal of fully annotating the genome to provide a ''''reference genome'''' in the Amoebozoa clade. They highlight several new features in the present update: (i) new annotations; (ii) improved interface with web 2.0 functionality; (iii) the initial steps towards a genome portal for the Amoebozoa; (iv) ortholog display; and (v) the complete integration of the Dicty Stock Center with dictyBase. The Dicty Stock Center currently holds over 1500 strains targeting over 930 different genes. There are over 100 different distinct amoebozoan species. In addition, the collection contains nearly 600 plasmids and other materials such as antibodies and cDNA libraries. The strain collection includes: * strain catalog * natural isolates * MNNG chemical mutants * tester strains for parasexual genetics * auxotroph strains * null mutants * GFP-labeled strains for cell biology * plasmid catalog The Dicty Stock Center can accept Dictyostelium strains, plasmids, and other materials relevant for research using Dictyostelium such as antibodies and cDNA or genomic libraries.

Proper citation: Dictyostelium discoideum genome database (RRID:SCR_006643) Copy   

•   Source: SciCrunch Registry

http://www.informatics.jax.org/mgihome/GXD/gxdgen.shtml

A unified resource that combines text-based and 3D graphical methods to store, display, and analyze mouse developmental gene expression information. The Mouse Gene Expression Information Resource resource will integrate the following components: * Gene Expression Database (GXD) - Integrates different types of expression data and provides links to many other resources to place the data into the larger biological and analytical context. * Anatomy Database - Provides the standard nomenclature for developmental anatomy. * 3D Atlas / Graphical Gene Expression Database - Provides a high-resolution digital representation of mouse anatomy reconstructed from serial sections of single embryos at each representative developmental stage enabling 3D graphical display and analysis of in situ expression data.

Proper citation: Mouse Genome Informatics: The Mouse Gene Expression Information Resource Project (RRID:SCR_006630) Copy   

•   Source: SciCrunch Registry

http://rice.plantbiology.msu.edu/

Database and resource that provides sequence and annotation data for the rice genome. This website provides genome sequence from the Nipponbare subspecies of rice and annotation of the 12 rice chromosomes. All structural and functional annotation is viewable through our Rice Genome Browser which currently supports 75 tracks of annotation. Enhanced data access is available through web interfaces, FTP downloads and a Data Extractor tool developed in order to support discrete dataset downloads. Rice is a model species for the monocotyledonous plants and the cereals which are the greatest source of food for the world''s population. While rice genome sequence is available through multiple sequencing projects, high quality, uniform annotation is required in order for genome sequence data to be fully utilized by researchers. The existence of a common gene set and uniform annotation allows researchers within the rice community to work from a common resource so that their results can be more easily interpreted by other scientists. The objective of this project has always been to provide high quality annotation for the rice genome. They generated, refined and updated gene models for the estimated 40,000-60,000 total rice genes, provided standardized annotation for each model, linked each model to functional annotation including expression data, gene ontologies, and tagged lines. They have provided a resource to extend the annotation of the rice genome to other plant species by providing comparative alignments to other plant species. Analysis/Tools are available including: BLAST, Locus Name Search, Functional Term Search, Protein Domain Search, Anatomy Expression Viewer, Highly Expressed Genes

Proper citation: Rice Genome Annotation (RRID:SCR_006663) Copy   

•   Source: SciCrunch Registry

http://www.dpvweb.net/

DPVweb provides a central source of information about viruses, viroids and satellites of plants, fungi and protozoa. Comprehensive taxonomic information, including brief descriptions of each family and genus, and classified lists of virus sequences are provided. The database also holds detailed, curated, information for all sequences of viruses, viroids and satellites of plants, fungi and protozoa that are complete or that contain at least one complete gene. For comparative purposes, it also contains a single representative sequence of all other fully sequenced virus species with an RNA or single-stranded DNA genome. The start and end positions of each feature (gene, non-translated region and the like) have been recorded and checked for accuracy. As far as possible, nomenclature for genes and proteins are standardized within genera and families. Sequences of features (either as DNA or amino acid sequences) can be directly downloaded from the website in FASTA format. The sequence information can also be accessed via client software for PC computers (freely downloadable from the website) that enable users to make an easy selection of sequences and features of a chosen virus for further analyses. The public sequence databases contain vast amounts of data on virus genomes but accessing and comparing the data, except for relatively small sets of related viruses can be very time consuming. The procedure is made difficult because some of the sequences on these databases are incorrectly named, poorly annotated or redundant. The NCBI Reference Sequence project (1) provides a comprehensive, integrated, non-redundant set of sequences, including genomic DNA, transcript (RNA) and protein products, for major research organisms. This now includes curated information for a single sequence of each fully sequenced virus species. While this is a welcome development, it can only deal with complete sequences. An important feature of DPV is the opportunity to access genes (and other features) of multiple sequences quickly and accurately. Thus, for example, it is easy to obtain the nucleotide or amino acid sequences of all the available accessions of the coat protein gene of a given virus species or for a group of viruses. To increase its usefulness further, DPVweb also contains a single representative sequence of all other fully sequenced virus species with an RNA or single-stranded DNA (ssDNA) genome. Sponsors: This site is supported by the Association of Applied Biologists and the Zhejiang Academy of Agricultural Sciences, Hangzhou, People''s Republic of China.

Proper citation: Descriptions of Plant Viruses (RRID:SCR_006656) Copy   

•   Source: SciCrunch Registry

http://www.bioconductor.org/packages/devel/bioc/html/ChIPXpress.html

A R package designed to improve ChIP-seq and ChIP-chip target gene ranking using publicly available gene expression data. It takes as input predicted transcription factor (TF) bound genes from ChIPx data and uses a corresponding database of gene expression profiles downloaded from NCBI GEO to rank the TF bound targets in order of which gene is most likely to be functional TF target.

Proper citation: ChIPXpress (RRID:SCR_006653) Copy   

•   Source: SciCrunch Registry

http://www.nactem.ac.uk/GREC/

A semantically annotated corpus of 240 MEDLINE abstracts (167 on the subject of E. coli species and 73 on the subject of the Human species) intended for training information extraction (IE) systems and/or resources which are used to extract events from biomedical literature. The corpus has been manually annotated with events relating to gene regulation by biologists. Each event is centered on either a verb (e.g. transcribe) or nominalized verb (e.g. transcription) and annotation consists of identifying, as exhaustively as possible, the structurally-related arguments of the verb or nominalized verb within the same sentence. Each event argument is then assigned the following information: * A semantic role from a fixed set of 13 roles which are tailored to the biomedical domain. * A biomedical concept type (where appropriate). The corpus in available for download in 2 formats: * A standoff format, based on the BioNLP'09 Shared Task format * An XML format, based on the GENIA event annotation format

Proper citation: GREC Corpus (RRID:SCR_006719) Copy   

•   Source: SciCrunch Registry

http://www.innatedb.com

Publicly available database of the genes, proteins, experimentally-verified interactions and signaling pathways involved in the innate immune response of humans, mice and bovines to microbial infection. The database captures coverage of the innate immunity interactome by integrating known interactions and pathways from major public databases together with manually-curated data into a centralized resource. The database can be mined as a knowledgebase or used with the integrated bioinformatics and visualization tools for the systems level analysis of the innate immune response. Although InnateDB curation focuses on innate immunity-relevant interactions and pathways, it also incorporates detailed annotation on the entire human, mouse and bovine interactomes by integrating data (178,000+ interactions & 3,900+ pathways) from several of the major public interaction and pathway databases. InnateDB also has integrated human, mouse and bovine orthology predictions generated using Ortholgue software. Ortholgue uses a phylogenetic distance-based method to identify possible paralogs in high-throughput orthology predictions. Integrated human and mouse conserved gene order and synteny information has also been determined to provide further support for orthology predictions. InnateDB Capabilities: * View statistics for manually-curated innate immunity relevant molecular interactions. New manually curated interactions are submitted weekly. * Search for genes and proteins of interest. * Search for experimentally-verified molecular interactions by gene/protein name, interaction type, cell type, etc. * Search genes/interactions belonging to 3,900 pathways. * Visualize interactions using an intuitive subcellular localization-based layout in Cerebral. * Upload your own list of genes along with associated gene expression data (from up to 10 experimental conditions) to interactively analyze this data in a molecular interaction network context. Once you have uploaded your data, you will be able to interactively visualize interaction networks with expression data overlaid; carry out Pathway, Gene Ontology and Transcription Factor Binding Site over-representation analyses; construct orthologous interaction networks in other species; and much more. * Access curated interaction data via a dedicated PSICQUIC webservice.

Proper citation: InnateDB (RRID:SCR_006714) Copy   

•   Source: SciCrunch Registry

http://inparanoid.sbc.su.se/cgi-bin/index.cgi

Collection of pairwise comparisons between 100 whole genomes generated by a fully automatic method for finding orthologs and in-paralogs between TWO species. Ortholog clusters in the InParanoid are seeded with a two-way best pairwise match, after which an algorithm for adding in-paralogs is applied. The method bypasses multiple alignments and phylogenetic trees, which can be slow and error-prone steps in classical ortholog detection. Still, it robustly detects complex orthologous relationships and assigns confidence values for in-paralogs. The original data sets can be downloaded.

Proper citation: InParanoid: Eukaryotic Ortholog Groups (RRID:SCR_006801) Copy   

•   Source: SciCrunch Registry

http://redfly.ccr.buffalo.edu

Curated collection of known Drosophila transcriptional cis-regulatory modules (CRMs) and transcription factor binding sites (TFBSs). Includes experimentally verified fly regulatory elements along with their DNA sequence, associated genes, and expression patterns they direct. Submission of experimentally verified cis-regulatory elements that are not included in REDfly database are welcome.

Proper citation: REDfly Regulatory Element Database for Drosophilia (RRID:SCR_006790) Copy   

•   Source: SciCrunch Registry

http://www.xpmutations.org

Interactive repository of mutations and other allelic variations of the genes involved in the DNA repair disorders, Xeroderma Pigmentosum (XP), Cockayne Syndrome (CS), Trichothiodystrophy (TTD), and other UV-sensitivity disorders. Any omitted data or new data may be submitted by using the on-line data submission form. There is a message board system to support discussions amongst those interested in XP and DNA Repair. RESOURCES * Educational module of the molecular biology of Nucleotide Excision Repair * Introduction to the DNA Repair disorders (XP, CS, TTD, UVs) * Background on each of the XP genes * A searchable database of mutations and sequence variations for the XP genes * Contact point for the submission of new mutation data * Discussion Forums and a Guest Book * Web Links to Additional Resources

Proper citation: Allelic Variations of The XP Genes (RRID:SCR_003376) Copy   

•   Source: SciCrunch Registry

http://www.loni.usc.edu/BIRN/Projects/Mouse/

Animal model data primarily focused on mice including high resolution MRI, light and electron microscopic data from normal and genetically modified mice. It also has atlases, and the Mouse BIRN Atlasing Toolkit (MBAT) which provides a 3D visual interface to spatially registered distributed brain data acquired across scales. The goal of the Mouse BIRN is to help scientists utilize model organism databases for analyzing experimental data. Mouse BIRN has ended. The next phase of this project is the Mouse Connectome Project (https://www.nitrc.org/projects/mcp/). The Mouse BIRN testbeds initially focused on mouse models of neurodegenerative diseases. Mouse BIRN testbed partners provide multi-modal, multi-scale reference image data of the mouse brain as well as genetic and genomic information linking genotype and brain phenotype. Researchers across six groups are pooling and analyzing multi-scale structural and functional data and integrating it with genomic and gene expression data acquired from the mouse brain. These correlated multi-scale analyses of data are providing a comprehensive basis upon which to interpret signals from the whole brain relative to the tissue and cellular alterations characteristic of the modeled disorder. BIRN's infrastructure is providing the collaborative tools to enable researchers with unique expertise and knowledge of the mouse an opportunity to work together on research relevant to pre-clinical mouse models of neurological disease. The Mouse BIRN also maintains a collaborative Web Wiki, which contains announcements, an FAQ, and much more.

Proper citation: Mouse Biomedical Informatics Research Network (RRID:SCR_003392) Copy   

•   Source: SciCrunch Registry

http://moma.dk/normfinder-software

Software for identifying the optimal normalization gene among a set of candidates. It ranks the set of candidate normalization genes according to their expression stability in a given sample set and given experimental design. It can analyze expression data obtained through any quantitative method e.g. real time RT-PCR and microarray based expression analysis. NormFinder.xla adds the NormFinder functionality directly to Excel. A version for R is also available.

Proper citation: NormFinder (RRID:SCR_003387) Copy   

•   Source: SciCrunch Registry

http://bbid.irp.nia.nih.gov/

Database of images of putative biological pathways, macromolecular structures, gene families, and cellular relationships. It is of use to those who are working with large sets of genes or proteins using cDNA arrays, functional genomics, or proteomics. The rationale for this collection is that: # Except in a few cases, information on most biological pathways in higher eukaryotes is non-existent, incomplete, or conflicting. # Similar biological pathways differ by tissue context, developmental stages, stimulatory events, or for other complex reasons. This database allows comparisons of different variations of pathways that can be tested empirically. # The goal of this database is to use images created directly by biomedical scientists who are specialists in a particular biological system. It is specifically designed to NOT use average, idealized or redrawn pathways. It does NOT use pathways defined by computer algorithm or information search approaches. # Information on biological pathways in higher eukaryotes generally resides in the images and text of review papers. Much of this information is not easily accessible by current medical reference search engines. # All images are attributable to the original authors. All pathways or other biological systems described are graphic representations of natural systems. Each pathway is to be considered a work in progress. Each carries some degree of error or incompleteness. The end user has the ultimate responsibility to determine the scientific correctness and validity in their particular biological system. Image/pathway submissions are welcome.

Proper citation: Biological Biochemical Image Database (RRID:SCR_003474) Copy   

•   Source: SciCrunch Registry

http://mimi.ncibi.org/MimiWeb/main-page.jsp

MiMi Web gives you an easy to use interface to a rich NCIBI data repository for conducting your systems biology analyses. This repository includes the MiMI database, PubMed resources updated nightly, and text mined from biomedical research literature. The MiMI database comprehensively includes protein interaction information that has been integrated and merged from diverse protein interaction databases and other biological sources. With MiMI, you get one point of entry for querying, exploring, and analyzing all these data. MiMI provides access to the knowledge and data merged and integrated from numerous protein interactions databases and augments this information from many other biological sources. MiMI merges data from these sources with deep integration into its single database with one point of entry for querying, exploring, and analyzing all these data. MiMI allows you to query all data, whether corroborative or contradictory, and specify which sources to utilize. MiMI displays results of your queries in easy-to-browse interfaces and provides you with workspaces to explore and analyze the results. Among these workspaces is an interactive network of protein-protein interactions displayed in Cytoscape and accessed through MiMI via a MiMI Cytoscape plug-in. MiMI gives you access to more information than you can get from any one protein interaction source such as: * Vetted data on genes, attributes, interactions, literature citations, compounds, and annotated text extracts through natural language processing (NLP) * Linkouts to integrated NCIBI tools to: analyze overrepresented MeSH terms for genes of interest, read additional NLP-mined text passages, and explore interactive graphics of networks of interactions * Linkouts to PubMed and NCIBI's MiSearch interface to PubMed for better relevance rankings * Querying by keywords, genes, lists or interactions * Provenance tracking * Quick views of missing information across databases. Data Sources include: BIND, BioGRID, CCSB at Harvard, cPath, DIP, GO (Gene Ontology), HPRD, IntAct, InterPro, IPI, KEGG, Max Delbreuck Center, MiBLAST, NCBI Gene, Organelle DB, OrthoMCL DB, PFam, ProtoNet, PubMed, PubMed NLP Mining, Reactome, MINT, and Finley Lab. The data integration service is supplied under the conditions of the original data sources and the specific terms of use for MiMI. Access to this website is provided free of charge. The MiMI data is queryable through a web services api. The MiMI data is available in PSI-MITAB Format. These files represent a subset of the data available in MiMI. Only UniProt and RefSeq identifiers are included for each interactor, pathways and metabolomics data is not included, and provenance is not included for each interaction. If you need access to the full MiMI dataset please send an email to mimi-help (at) umich.edu.

Proper citation: Michigan Molecular Interactions (RRID:SCR_003521) Copy   

•   Source: SciCrunch Registry

http://rgd.mcw.edu/tools/ontology/ont_search.cgi

Ontology that defines hierarchical display of different rat strains as derived from parental strains. Ontology Browser allows to retrieve all genes, QTLs, strains and homologs annotated to particular term. Covers all types of biological pathways including altered and disease pathways, and to capture relationships between them within hierarchical structure. Five nodes of ontology include classic metabolic, regulatory, signaling, drug and disease pathways. Ontology allows for standardized annotation of rat. Serves as vehicle to connect between genes and ontology reports, between reports and interactive pathway diagrams, between pathways that directly connect to one another within diagram or between pathways that in some fashion are globally related in pathway suites and suite networks.

Proper citation: Rat Strain Ontology (RRID:SCR_003449) Copy   

•   Source: SciCrunch Registry