Curator Dashboard

A virtual database of annotations made by 50 database providers (April 2014) - and growing (see below), that map data to publication information. All NIF Data Federation sources can be part of this virtual database as long as they indicate the publications that correspond to data records. The format that NIF accepts is the PubMed Identifier, category or type of data that is being linked to, and a data record identifier. A subset of this data is passed to NCBI, as LinkOuts (links at the bottom of PubMed abstracts), however due to NCBI policies the full data records are not currently associated with PubMed records. Database providers can use this mechanism to link to other NCBI databases including gene and protein, however these are not included in the current data set at this time. (To view databases available for linking see, http://www.ncbi.nlm.nih.gov/books/NBK3807/#files.Databases_Available_for_Linking ) The categories that NIF uses have been standardized to the following types: * Resource: Registry * Resource: Software * Reagent: Plasmid * Reagent: Antibodies * Data: Clinical Trials * Data: Gene Expression * Data: Drugs * Data: Taxonomy * Data: Images * Data: Animal Model * Data: Microarray * Data: Brain connectivity * Data: Volumetric observation * Data: Value observation * Data: Activation Foci * Data: Neuronal properties * Data: Neuronal reconstruction * Data: Chemosensory receptor * Data: Electrophysiology * Data: Computational model * Data: Brain anatomy * Data: Gene annotation * Data: Disease annotation * Data: Cell Model * Data: Chemical * Data: Pathways For more information refer to Create a LinkOut file, http://neuinfo.org/nif_components/disco/interoperation.shtm Participating resources ( http://disco.neuinfo.org/webportal/discoLinkoutServiceSummary.do?id=4 ): * Addgene http://www.addgene.org/pgvec1 * Animal Imaging Database http://aidb.crbs.ucsd.edu * Antibody Registry http://www.neuinfo.org/products/antibodyregistry/ * Avian Brain Circuitry Database http://www.behav.org/abcd/abcd.php * BAMS Connectivity http://brancusi.usc.edu/ * Beta Cell Biology Consortium http://www.betacell.org/ * bioDBcore http://biodbcore.org/ * BioGRID http://thebiogrid.org/ * BioNumbers http://bionumbers.hms.harvard.edu/ * Brain Architecture Management System http://brancusi.usc.edu/bkms/ * Brede Database http://hendrix.imm.dtu.dk/services/jerne/brede/ * Cell Centered Database http://ccdb.ucsd.edu * CellML Model Repository http://www.cellml.org/models * CHEBI http://www.ebi.ac.uk/chebi/ * Clinical Trials Network (CTN) Data Share http://www.ctndatashare.org/ * Comparative Toxicogenomics Database http://ctdbase.org/ * Coriell Cell Repositories http://ccr.coriell.org/ * CRCNS - Collaborative Research in Computational Neuroscience - Data sharing http://crcns.org * Drug Related Gene Database https://confluence.crbs.ucsd.edu/display/NIF/DRG * DrugBank http://www.drugbank.ca/ * FLYBASE http://flybase.org/ * Gene Expression Omnibus http://www.ncbi.nlm.nih.gov/geo/ * Gene Ontology Tools http://www.geneontology.org/GO.tools.shtml * Gene Weaver http://www.GeneWeaver.org * GeneDB http://www.genedb.org/Homepage * Glomerular Activity Response Archive http://gara.bio.uci.edu * GO http://www.geneontology.org/ * Internet Brain Volume Database http://www.cma.mgh.harvard.edu/ibvd/ * ModelDB http://senselab.med.yale.edu/modeldb/ * Mouse Genome Informatics Transgenes ftp://ftp.informatics.jax.org/pub/reports/MGI_PhenotypicAllele.rpt * NCBI Taxonomy Browser http://www.ncbi.nlm.nih.gov/Taxonomy/taxonomyhome.html * NeuroMorpho.Org http://neuromorpho.org/neuroMorpho * NeuronDB http://senselab.med.yale.edu/neurondb * SciCrunch Registry http://neuinfo.org/nif/nifgwt.html?tab=registry * NIF Registry Automated Crawl Data http://lucene1.neuinfo.org/nif_resource/current/ * NITRC http://www.nitrc.org/ * Nuclear Receptor Signaling Atlas http://www.nursa.org * Olfactory Receptor DataBase http://senselab.med.yale.edu/ordb/ * OMIM http://omim.org * OpenfMRI http://openfmri.org * PeptideAtlas http://www.peptideatlas.org * RGD http://rgd.mcw.edu * SFARI Gene: AutDB https://gene.sfari.org/autdb/Welcome.do * SumsDB http://sumsdb.wustl.edu/sums/ * Temporal-Lobe: Hippocampal - Parahippocampal Neuroanatomy of the Rat http://www.temporal-lobe.com/ * The Cell: An Image Library http://www.cellimagelibrary.org/ * Visiome Platform http://platform.visiome.neuroinf.jp/ * WormBase http://www.wormbase.org * YPED http://medicine.yale.edu/keck/nida/yped.aspx * ZFIN http://zfin.org

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• SciCrunch
• 14 years ago - by Anonymous

THIS RESOURCE IS NO LONGER IN SERVICE, documented August 29, 2016. The aim of EXT (extinction) is to investigate the relation between specific genetic variations and cognitive control process in fear. Blood samples will be collected from about 300 healthy, young individuals (age 18-35).

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• SciCrunch
• 15 years ago - by Anonymous

The Max Planck Institute of Neurobiology was a research institute of the Max Planck Society located in Martinsried, a suburb of Munich in Germany. It existed between 1984 and 2022 and merged with the Max Planck Institute for Ornithology to the new, joint Max Planck Institute for Biological Intelligence in 2023. The institute is dedicated to basic research on topics in behavioral ecology, evolutionary biology and neuroscience.

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• SciCrunch
• 15 years ago - by Anonymous

Quick ASL Wrapper for preprocessing arterial spin labeled (ASL) Data and computing blood flow measurements using UPenn ASL toolbox.

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• SciCrunch
• 13 years ago - by Anonymous

genes2mind is a tool for rapid exploratory analysis of psychotropic drug-induced gene expression in the brain. We present here an open resource containing comparison of effects of various classes of psychotropic drugs on transcriptional alterations of ~20,000 genes in the mouse brain (C57BL/6J). Data stored in the database include raw gene expression values as well as results of drug comparison. * Genomic Signature Identification section allows for the identification of drug-specific genomic signatures. * Genomic Signature Analysis section allows for further inspection and visualization of the signatures using multidimensional data analysis (PCA), co-expression analysis and heatmaps. * Single Gene Inspection allows for brief review of expression of specific candidate genes using barplots.

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• SciCrunch
• 15 years ago - by Anonymous

APID Interactomes (Agile Protein Interactomes DataServer) provides information on the protein interactomes of numerous organisms, based on the integration of known experimentally validated protein-protein physical interactions (PPIs). The interactome data includes a report on quality levels and coverage over the proteomes for each organism included. APID integrates PPIs from primary databases of molecular interactions (BIND, BioGRID, DIP, HPRD, IntAct, MINT) and also from experimentally resolved 3D structures (PDB) where more than two distinct proteins have been identified. This collection references protein interactors, through a UniProt identifier.

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• SciCrunch
• 15 years ago - by Anonymous

The Generic GO Term Finder finds the significant GO terms shared among a list of genes from an organism, displaying the results in a table and as a graph (showing the terms and their ancestry). The user may optionally provide background information or a custom gene association file or filter evidence codes. This tool is capable of batch processing multiple queries at once. GO::TermFinder comprises a set of object-oriented Perl modules GO::TermFinder can be used on any system on which Perl can be run, either as a command line application, in single or batch mode, or as a web-based CGI script. This implementation, developed at the Lewis-Sigler Institute at Princeton, depends on the GO-TermFinder software written by Gavin Sherlock and Shuai Weng at Stanford University and the GO:View module written by Shuai Weng. It is made publicly available through the GMOD project. The full source code and documentation for GO:TermFinder are freely available from http://search.cpan.org/dist/GO-TermFinder/. Platform: Online tool, Windows compatible, Mac OS X compatible, Linux compatible, Unix compatible

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• SciCrunch
• 15 years ago - by Anonymous

Image library offers a collection of peer reviewed images relating to hematology available to the ASH members and the hematology community. Image Bank slides are downloadable and users may create unique collections called My Collections. The Image Bank contains over 2100 images, and new images will be added each month. ASH encourages new submissions that are subject to review by the Image Bank Editor.

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• SciCrunch
• 15 years ago - by Anonymous

THIS RESOURCE IS NO LONGER IN SERVICE, documented on August 16, 2019. Multi-database system providing large-scale lists of user-classified sequence identifiers with genome-scale biological information and functional profiles biologically characterizing the different gene classes in the list. GFINDer automatically retrieves updated annotations of several functional categories from different sources, identifies the categories enriched in each class of a user-classified gene list, and calculates statistical significance values for each category. Moreover, GFINDer enables to functionally classify genes according to mined functional categories and to statistically analyze the obtained classifications, aiding in better interpreting microarray experiment results.

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• SciCrunch
• 15 years ago - by Anonymous

An algorithm to estimate isoforms expression from RNA-seq data based on a model that doesn''t assume uniform distribution of count within transcripts.

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• SciCrunch
• 13 years ago - by Anonymous

The primary purpose is to assess the importance of environmental factors for Parkinson's Disease (PD) in a population-based sample of Swedish twins. In PD discordant twin pairs, what are the environmental factors that contribute to the disease in the affected twin and or protect the unaffected twin? Second, we want to investigate whether the earlier reports of low heritability for elderly male twins can be confirmed for female pairs. All twins 55 years of age and older in the Swedish Twin Registry have been screened for most complex diseases. 626 twins have screened positive for PD and most pairs are discordant. To establish diagnosis, a physician will examine all potential cases and their co-twins and their medical records will be reviewed. Environmental factors will be studied through the use of discordant pairs, where genetic susceptibility to the disease can be controlled. Environmental exposures are being secured with telephone interviews and from a questionnaire collected 30 years ago. Recent results indicate that genetic factors play a very small role. A better understanding of the etiology of PD is important for the possibility of delaying onset or even preventing the disease, as well as for providing guidance for molecular biology studies. Types of samples * DNA Number of sample donors: 333 (sample collection completed)

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• SciCrunch
• 15 years ago - by Anonymous

OrChem is an extension for the Oracle 11G database that adds registration and indexing of chemical structures to support fast substructure and similarity searching. The cheminformatics functionality is provided by the Chemistry Development Kit. OrChem provides similarity searching with response times in the order of seconds for databases with millions of compounds, depending on a given similarity cut-off. For substructure searching, it can make use of multiple processor cores on today''s powerful database servers to provide fast response times in equally large data sets. OrChem is an Oracle chemistry plug-in using the Chemistry Development Kit (CDK). The CDK is an open source Java library for Chemoinformatics and Bioinformatics. OrChem is maintained by the chemoinformatics and metabolism team of the European Bioinformatics Institute. Oracle Data cartridges extend the capabilities of the Oracle server. For chemistry various commercial cartridges exist that facilitate searching and analyzing chemical data. OrChem also provides functionality like this, but is not a cartridge. It doesn''t need Oracle''s extensibility architecture because its Java components run as Java stored procedures inside the Oracle standard JVM (Aurora). OrChem is suitable for Oracle 11G and onwards. Starting with Oracle 11g release 1 (11.1) there is a just-in-time(JIT) compiler for Oracle JVM environment. A JIT compiler for Oracle JVM enables much faster execution because it manages the invalidation, recompilation, and storage of code without an external mechanism. This new Oracle feature makes Java classes perform better than before.

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• SciCrunch
• 15 years ago - by Anonymous

Gene Ontology for Motifs (GOMO) is an alignment- and threshold-free comparative genomics approach for assigning functional roles to DNA regulatory motifs from DNA sequence. The algorithm detects associations between a user-specified DNA regulatory motif (expressed as a position weight matrix; PWM) and Gene Ontology terms. The original method for predicting the roles of transcription factors (TFs starts with a PWM motif describing the DNA-binding affinity of the TF. GOMO uses the PWM to score the promoter region of each gene in the genome for its likelihood to be bound by the TF. The resulting ''''affinity'''' scores are then used to test each term in the Gene Ontology for association with high-scoring genes. The algorithm was subsequently extended to leverage conserved signals using multiple, related species in a comparative approach, which greatly improves the resulting annotations. Platform: Online tool, Windows compatible, Mac OS X compatible, Linux compatible, Unix compatible

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• SciCrunch
• 15 years ago - by Anonymous

A tool for extraction and collaboration for data curation related to biology. CBioC runs as a web browser extension and allows unobtrusive use of the system during the regular course of research in PubMed. It can also be accessed directly (without having to install a plug-in). Automated text extraction is used as a starting point to bootstrap the database, but then it is up to biologists improve upon the extracted data, ironing out inconsistencies by subsequent edits on a massive scale. * After install, it loads when you visit PubMed. * Gets interactions from PubMed abstracts. * Allows you to vote and modify extracted data. * Also shows data from BIND, DIP, MINT, GRID, IntAct.

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• SciCrunch
• 15 years ago - by Anonymous

CUDASW++ is a bioinformatics software for Smith-Waterman protein database searches that takes advantage of the massively parallel CUDA architecture of NVIDIA Tesla GPUs to perform sequence searches 10x-50x faster than NCBI BLAST. In this algorithm, we deeply explore the SIMT (Single Instruction, Multiple Thread) and virtualized SIMD (Single Instruction, Multiple Data) abstractions to achieve fast speed. This algorithm has been fully tested on Tesla C1060, Tesla C2050, GeForce GTX 280 and GTX 295 graphics cards, and has been incorporated to NVIDIA Tesla Bio Workbench. * Operating System: Linux * Programming language: CUDA and C * Other requirements: CUDA SDK and Toolkits 2.0 or higher

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• SciCrunch
• 15 years ago - by Anonymous

THIS RESOURCE IS NO LONGER IN SERVICE, documented on July 16, 2013. A built-in toolbox for the tracing and analysis of neuroanatomy from nanoscale (high-resolution) imaging. It is a project under ongoing development. The name is originating by merging the words Neuron + reconstruct. The working concept is organized in filters applied successively on the image stack to be processed (pipeline). Currently, the focus of the software is the extraction of detailed neuroanatomical profiles from nanoscale imaging techniques, such as the Serial Block-Face Scanning Electron Microscopy (SBFSEM). The techniques applied, however, may be used to analyze data from various imaging methods and neuronal versatility. The underlying idea of Neurostruct is the use of slim interfaces/filters allowing an efficient use of new libraries and data streaming. The image processing follows in voxel pipelines by using the CUDA programming model and all filters are programmed in a datasize-independent fashion. Thus Neurostruct exploits efficiency and datasize-independence in an optimal way. Neurostruct is based on the following main principles: * Image processing in voxel pipelines using the general purpose graphics processing units (GPGPU) programming model. * Efficient implementation of these interfaces. Programming model and image streaming that guarantees a minimal performance penalty. * Datasize-independent programming model enabling independence from the processed image stack. * Management of the filters and IO data through shell scripts. The executables (filters) are currently managed through shell scripts. The application focuses currently in the tracing of single-biocytin filled cells using SBFSEM imaging. : * Extraction of neuroanatomical profiles: 3D reconstrution and 1D skeletons of the imaged neuronal structure. * Complete tracing: Recognition of the full neuronal structure using envelope techniques, thereby remedying the problem of spines with thin necks of an internal diameter approaching the SBFSEM resolution. * Separation (Coloring) of subcellular structures: Algorithms for the separation of spines from their root dendritic stem. * Evaluation and analysis of the imaged neuroanatomy: Calculation of the dendritic and spine membrane''s surface, spine density and variation, models of dendrites and spines

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• SciCrunch
• 15 years ago - by Anonymous

The Edwards lab conducts research in various aspects of computational biology and bioinformatics, particularly proteomics and mass spectrometry informatics and DNA and protein based signatures for pathogen detection. Some tools provided by Edwards Lab are the PepArML Meta-Search Engine, PeptideMapper Web-Service, Peptide Sequence Databases, Rapid Microorganism Identification Database (RMIDb), and GlycoPeptideSearch. Our primary area of research is the analysis of mass spectrometry experiments for proteomics. Proteomics, the qualitative and quantitative analysis of the expressed proteins of a cell, makes it possible to detect and compare the protein abundance profiles of different samples. Proteins observed to be under or over expressed in disease samples can lead to diagnostic markers or drug targets. The observation of mutated or alternatively spliced protein isoforms may provide domain experts with clues to the mechanisms by which a disease operates. The detection of proteins by mass spectrometry can even signal the presence of airborne microorganisms, such as anthrax, in the detect-to-protect time-frame. Recent research has focused on the discovery of novel peptides in proteomics datasets, improving the sensitivity and specificity of peptide identification using spectral matching with hidden Markov models, and unsupervised machine-learning based peptide identification result combining. Outside of proteomics, we work on computational tools for the design of highly specific oligonucleotides useful for pathogen signatures and PCR assay design. Recent research has focused on precomputing all human oligos of length 20 that are unique up to 4 string edits; and all bacterial 20-mer oligos that are species specific up to 4 string edits.

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• SciCrunch
• 15 years ago - by Anonymous

The Frey Lab develops techniques that use large scale datasets to derive predictive models of how genes and many other genomic features act in combination to produce genetic messages that control cellular activities. We have most recently focused on how organisms use alternative splicing to generate a tremendous level of biological complexity that cannot be explained by gene expression alone (Nature, 2010). Some of the tools, software and databases provided by the Frey Lab are affinity propagation, splicing prediction, PTMClust - A Post-translational Modification Refinement Algorithm, the ''epitome'': A new model of patterns, transformation invariant clustering and subspaces, learning flexible sprites from images and videos, phase unwrapping by loopy belief propagation, useful Matlab scripts, bioinformatics links, and SeedSearcher: A motif finder.

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• SciCrunch
• 15 years ago - by Anonymous

The Spotfire Gene Ontology Advantage Application integrates GO annotations with gene expression analysis in Spotfire DecisionSite for Functional Genomics. Researchers can select a subset of genes in DecisionSite visualizations and display their distribution in the Gene Ontology hierarchy. Similarly, selection of any process, function or cellular location in the Gene Ontology hierarchy automatically marks the corresponding genes in DecisionSite visualizations. Platform: Windows compatible

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• SciCrunch
• 15 years ago - by Anonymous

The UCSF Helen Diller Family Comprehensive Cancer Center combines basic science, clinical research, epidemiology/cancer control, and patient care throughout the University of California, San Francisco. UCSF''s long tradition of excellence in cancer research includes, notably, the Nobel Prize-winning work of J. Michael Bishop and Harold Varmus, who discovered cancer-causing oncogenes. Their work opened new doors for exploring genetic mistakes that cause cancer, and formed the basis for some of the most important cancer research happening today. * Basic Scientific Research: From understanding normal cellular processes and replication to discovering the underlying molecular and genetic causes of cancer when these processes go awry, UCSF researchers are committed to moving scientific insights beyond model systems and pursuing their relevance for clinical oncology and cancer prevention. * Clinical Research: Clinical scientists explore how greater understanding of fundamental biological events can be transformed into clinically relevant tools. New forms of cancer treatment, as well as innovations in diagnosis and prognosis, undergo rigorous evaluation for safety and efficacytranslating into improved patient outcomes and hope for the future. * Patient Care: The Helen Diller Family Comprehensive Cancer Center provides superlative cancer patient care at four San Francisco medical centers: UCSF Medical Center at Mount Zion; UCSF Medical Center at Parnassus; San Francisco General Hospital; and the San Francisco Veterans Affairs Medical Center. * Population Science: Cancer population sciences at UCSF includes a broad range of research on the causes of new cancers and the sickness and death due to the disease in order to develop ways to improve the prevention and early detection of cancer as well as the quality of life following diagnosis and treatment for all of Northern California''s diverse populations.

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• SciCrunch
• 15 years ago - by Anonymous