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FlexProt detects the optimal flexible structural alignment of a pair of protein structures. The first structure is assumed to be rigid, while in the second structure potential flexible regions are automatically detected.

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• SciCrunch
• 17 years ago - by Anonymous

Organization portal for neuromuscular disease community and contains comprehensive listing of biological and clinical aspects of neuromuscular disorders.This knowledge base contains information on the physiology, structure of ion channels, neurotransmitters, neuroreceptors, and associated diseases. Major categories include DISORDERS & SYNDROMES, INDEXES, NEUROMUSCULAR EVALUATION, ANTIBODY TESTING and NEUROMUSCULAR DIVISION.

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• SciCrunch
• 17 years ago - by Anonymous

Imagebank consists of freely available images contributed by academics, researchers, Learned Societies, industry and individuals with rights cleared for educational purposes. :Users are able to search for images based on keywords or browse within a wide range of bioscience subject areas. Images are then downloadable along with informative descriptive text provided by the contributor. All images undergo a validation process by Centre for Bioscience staff with good subject knowledge. ImageBank also offers reviews of, and links to existing bioscience image databases.

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• SciCrunch
• 17 years ago - by Anonymous

Functional genomics data repository supporting MIAME-compliant data submissions. Tools are provided to help users query and download experiments and curated gene expression profiles. These data include microarray-based experiments measuring the abundance of mRNA, genomic DNA, and protein molecules, as well as non-array-based technologies such as serial analysis of gene expression (SAGE) and mass spectrometry proteomic technology. Array- and sequence-based data are accepted.

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• SciCrunch
• 17 years ago - by Anonymous

An open international project under the patronage of the Human Proteome Organisation (HUPO) that aims: To analyze the brain proteome of human as well as mouse models in healthy, neurodiseased and aged status with focus on Alzheimer's and Parkinson's Disease; To perform quantitative proteomics as well as complementary gene expression profiling on disease-related brain areas and bodily fluids; To advance knowledge of neurodiseases and aging in order to push new diagnostic approaches and medications; To exchange knowledge and data with other HUPO projects and national / international initiatives in the neuroproteomic field; To make neuroproteomic research and its results available in the scientific community and society. Recent work has shown that standards in proteomics and especially in bioinformatics are mandatory to allow comparable analyses, but still missing. To address this challenge, the HUPO BPP is closely working together with the HUPO Proteome Standards Initiative (HUPO PSI).

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• SciCrunch
• 17 years ago - by Anonymous

A web tool that allows you to use large reference epigenome datasets for your own analysis without complex scripting or laborious preprocessing.

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• SciCrunch
• 13 years ago - by Anonymous

THIS RESOURCE IS NO LONGER IN SERVICE, documented on July 16, 2013. The 7 database (or a7db) provides physiological, pharmacological and structural data pertaining to the 7 subunit of the nicotinic acetylcholine receptor. As well as the simple boolean-based query, there are several other ways to help you interrogate the database; * One page query builder * Query builder based on the category of data * Upload a prebuilt/previous query * Browse the database To gain insight into what sort of data can be queried, the one page or categorized query builders are recommended. Or you can just browse the database. The best way to navigate is to use the links on the left. Please be aware that we are presenting the raw data and that it is up to the user on how best to interpret that data. You can read more about the database in the recent article in BMC Neuroscience

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• SciCrunch
• 17 years ago - by Anonymous

THIS RESOURCE IS NO LONGER IN SERVICE, documented on June 08, 2011. This database contains gene expression data for various physiological and pathological processes in mouse brain. All the data have been obtained by adaptor-tagged competitive PCR, an advanced version of quantitative PCR. Brain Gene Expression Database (BGED) contains gene expression data for various physiological and pathological processes in mouse brain. All the data have been obtained by adaptor-tagged competitive PCR, an advanced version of quantitative PCR. Manual Download 1. Data retrieval Gene expression data can be retrieved either by ID numbers or by keywords representing functional annotations from this page. The ID numbers include GenBank, RefSeq, SwissProt, Gene Ontology, and BED (our own ID). The keyword search is based either on definition in GenBank, SwissProt and RefSeq, functional annotation of SwissProt database, or Gene Ontology terms. 2. Gene expression pattern display * Display of multiple gene expression patterns. Expression patterns of multiple genes selected by the keyword search can be displayed from the result page of the keyword search. * Gene expression pattern similarity search This function is available on the information page of each gene accessed through BED ID (in-house ID).

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• SciCrunch
• 17 years ago - by Anonymous

Series of MRI slices from 3 male Japanese snow monkeys (Macaca fuscata). Images are provided of 2 mm MRI slices of male monkey brain in the coronal (N = 5), horizontal (N =1) and sagittal (N =1) planes. Slices are presented according to distance from interaural line. The slices are low resolution and no annotations are provided. Would be suitable for low resolution 3D reconstruction of monkey brain, however. Also, there are datasets provided for 5 monkeys total, 3 m.fuscata and 2 m. mulatta.

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• SciCrunch
• 17 years ago - by Anonymous

THIS RESOURCE IS NO LONGER IN SERVICE, documented on September 06, 2013. Resource aimed at promoting awareness and facilitating access to online neuroscience databases.

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• SciCrunch
• 17 years ago - by Anonymous

HubMed provides an interface to PubMed. Quick access to searches with a Firefox search plugin or a HubMed bookmarklet (drag to your browser''s bookmarks toolbar). Export citations in RIS, BibTeX, RDF and MODS formats, or directly to RefWorks. Unzip HubMed''s import filter into Endnote''s Filters folder for direct import into Endnote, or install the RIS Export plugin for direct import into ProCite, RefMan and older versions of Endnote. Use the Citation Finder to convert reference lists from PDFs into search results. Create lists of closely related papers using Rank Relations, then visualise and browse clusters of related papers using TouchGraph (requires Java). Graph occurrences of keywords in published papers over time. Tag and store annotated metadata for articles of interest.

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• SciCrunch
• 17 years ago - by Anonymous

Anatomical atlas about structural anatomy of Caenorhabditis elegans. Provides simple interface allowing user to easily navigate through every anatomical structure of worm. Contains set of images which can be sorted by different characteristics: sex, genotype, age, body portion or tissue type. Includes links to other major worm websites and databases. Application for viewing and downloading thousands of unpublished electron micrographs and associated data. These images have been generated by several labs in the C. elegans community, including the MRC, the Hall lab (Center for C. elegans Anatomy), and the Culotti and Riddle labs.

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• SciCrunch
• 17 years ago - by Anonymous

An integrated resource for genomics and bioinformatics in vision research including expressed sequence tag (EST) data and sequence-verified cDNA clones for multiple eye tissues of several species, web-based access to human eye-specific SAGE data through EyeSAGE, and comprehensive, annotated databases of known human eye disease genes and candidate disease gene loci. All expression- and disease-related data are integrated in EyeBrowse, an eye-centric genome browser. NEIBank provides a comprehensive overview of current knowledge of the transcriptional repertoires of eye tissues and their relation to pathology. The data can be interrogated in several ways. Specific gene names can be entered into the search window. Alternatively, regions of the genome can be displayed. For example, entering two STS markers separated by a semicolon (e.g. RH18061;RH80175) allows the display of the entire chromosomal region associated with the mapping of a specific disease locus. ESTs for each tissue can then be displayed to help in the selection of candidate genes. In addition, sequences can be entered into a BLAST search and rapidly aligned on the genome, again showing eye derived ESTs for the same region. To see the same region at the full UCSC site, cut and paste the location from the position window of the genome browser. EyeBrowse includes a custom track display SAGE data for human eye tissues derived from the EyeSAGE project. The track shows the normalized sum of SAGE tag counts from all published eye-related SAGE datasets centered on the position of each identifiable Unigene cluster. This indicates relative activity of each gene locus in eye. Clicking on the vertical count bar for a particular location will bring up a display listing gene details and linking to specific SAGE counts for each eye SAGE library and comparisons with normalized sums for neural and non-neural tissues. To view or alter settings for the EyeSAGE track on EyeBrowse, click on the vertical gray bar at the left of the display. Other custom tracks display known eye disease genes and mapped intervals for candidate loci for retinal disease, cataract, myopia and cornea disease. These link back to further information at NEIBank.

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• SciCrunch
• 17 years ago - by Anonymous

Brain atlas in sagittal, coronal, and axial planes some from myelin stained sections, others from MRI. The structures are outlined and labeled on the zoomable images in the coronal series. The labels can also be used in quiz mode. Designed as part of of program for second year medical students studying neuroanatomy.

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• SciCrunch
• 17 years ago - by Anonymous

Interactive Matlab toolbox for processing continuous and event-related EEG, MEG and other electrophysiological data incorporating independent component analysis (ICA), time/frequency analysis, artifact rejection, event-related statistics, and several useful modes of visualization of the averaged and single-trial data. First developed on Matlab 5.3 under Linux, EEGLAB runs on Matlab v5 and higher under Linux, Unix, Windows, and Mac OS X (Matlab 7+ recommended). EEGLAB provides an interactive graphic user interface (GUI) allowing users to flexibly and interactively process their high-density EEG and other dynamic brain data using independent component analysis (ICA) and/or time/frequency analysis (TFA), as well as standard averaging methods. EEGLAB also incorporates extensive tutorial and help windows, plus a command history function that eases users'' transition from GUI-based data exploration to building and running batch or custom data analysis scripts. EEGLAB offers a wealth of methods for visualizing and modeling event-related brain dynamics, both at the level of individual EEGLAB ''datasets'' and/or across a collection of datasets brought together in an EEGLAB ''studyset.'' For experienced Matlab users, EEGLAB offers a structured programming environment for storing, accessing, measuring, manipulating and visualizing event-related EEG data. For creative research programmers and methods developers, EEGLAB offers an extensible, open-source platform through which they can share new methods with the world research community by publishing EEGLAB ''plug-in'' functions that appear automatically in the EEGLAB menu of users who download them. For example, novel EEGLAB plug-ins might be built and released to ''pick peaks'' in ERP or time/frequency results, or to perform specialized import/export, data visualization, or inverse source modeling of EEG, MEG, and/or ECOG data. EEGLAB Features * Graphic user interface * Multiformat data importing * High-density data scrolling * Defined EEG data structure * Open source plug-in facility * Interactive plotting functions * Semi-automated artifact removal * ICA & time/frequency transforms * Many advanced plug-in toolboxes * Event & channel location handling * Forward/inverse head/source modeling

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• SciCrunch
• 17 years ago - by Anonymous

The FBIRN Federated Informatics Research Environment (FIRE) includes tools and methods for multi-site functional neuroimaging. This includes resources for data collection, storage, sharing and management, tracking, and analysis of large fMRI datasets. fBIRN is a national initiative to advance biomedical research through data sharing and online collaboration. BIRN provides data-sharing infrastructure, software tools, strategies and advisory services - all from a single source.

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• SciCrunch
• 17 years ago - by Anonymous

THIS RESOURCE IS NO LONGER IN SERVICE, documented on September 06, 2013. It was established to develop, implement and support the information infrastructure necessary to achieve large-scale data sharing among the test bed participants (function, morphometry and mouse birn). The BIRN-CC consists of a unique and well-established partnership between computer scientists, neuroscientists and engineers. This partnership addresses a large array of technical, policy, and architectural issues to fundamentally enable a new suite of information technology supported database and analysis tools that allow scientists to analyze and interpret significantly larger sets of data than is possible in the traditional single-institution study paradigm.

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• SciCrunch
• 17 years ago - by Anonymous

A controlled vocabulary of terms used in Computational Neurosciences to describe models of the nervous system. This first release of CNO is an alpha version and should be further aligned with other ontologies accessible on Bioportal and should be made compliant with the OBO foundry recommendations.

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• SciCrunch
• 13 years ago - by Anonymous

Mammalian protein-protein interaction database focusing on synaptic proteins. The Protein-Protein Interaction Database was originally a single-person's attempt to integrate a gamut of biological/bibliographical/molecular data and build a framework which might help understanding how cells orchestrate their protein content in order to become what they are: machines with a purpose. This is based on the simple paradigm that functionality like signal cascades are held together in a close space, thereby allowing specific events to occur without the necessity of passive diffusion and random events. The PPID database arose from the need to interpret Proteomic datasets, which were generated analysing the NMDA-receptor complex (see H. Husi, M. A. Ward, J. S. Choudhary, W. P. Blackstock and S. G. Grant (2000). Proteomic analysis of NMDA receptor-adhesion protein signaling complexes. Nat Neurosci 3, 661-669.). To study these clusters of proteins requires unavoidably the handling of large datasets, which PPID is generally aimed and tailored for. This database is unifying molecular entries across three species, namely human, rat and mouse and is is footed on sequence databases such as SwissProt, EMBL, TrEMBL (translated EMBL sequences) and Unigene and the literature database PubMed. A typical entry in PPID holds up to three general entries for the three species, all protein and gene accession numbers associated with them (assembled from Blast2 searches of the databases) and the OMIM entry as maintained by Johns Hopkins University. Furthermore protein sequence information is also included, together with known and novel splice-variants of each molecule as found by ClustalW sequence alignments. Entry points also include protein-binding information together with the literature reference. The whole database is curated manually to insure accuracy and quality. Querying the database will be possible by online browsing and batch-submission for large datasets holding accession number information, as can be generated using software like Mascot for mass-spectrometry. Cluster-analysis of the submitted datasets in the form of a graphical output will be developed as well as an easy-to-use web-interface. An interface is currently being built in collaboration with the Department of Informatics (T. Theodosiou and D. Armstrong) and will be deployed soon The current team of people collating and deploying the database are H. Husi (database mining and information gathering) and T. Theodosiou (web-interface and deployment). Please note that this database is not funded financially, and cannot survive without sponsorship.

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• SciCrunch
• 17 years ago - by Anonymous

OdorMapDB is designed to be a database to support the experimental analysis of the molecular and functional organization of the olfactory bulb and its basis for the perception of smell. It is primarily concerned with archiving, searching and analyzing maps of the olfactory bulb generated by different methods. The first aim is to facilitate comparison of activity patterns elicited by odor stimulation in the glomerular layer obtained by different methods in different species. It is further aimed at facilitating comparison of these maps with molecular maps of the projections of olfactory receptor neuron subsets to different glomeruli, especially for gene targeted animals and for antibody staining. The main maps archived here are based on original studies using 2-deoxyglucose and on current studies using high resolution fMRI in mouse and rat. Links are also provided to sites containing maps by other laboratories. OdorMapDB thus serves as a nodal point in a multilaboratory effort to construct consensus maps integrating data from different methodological approaches. OdorMapDB is integrated with two other databases in SenseLab: ORDB, a database of olfactory receptor genes and proteins, and OdorDB, a database of odor molecules that serve as ligands for the olfactory receptor proteins. The combined use of the three integrated databases allows the user to identify odor ligands that activate olfactory receptors that project to specific glomeruli that are involved in generating the odor activity maps.

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• SciCrunch
• 17 years ago - by Anonymous