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http://brainstars.org

BrainStars (or B*) is a quantitative expression database of the adult mouse brain. The database has genome-wide expression profile at 51 adult mouse CNS regions. For 51 CNS regions, slices (0.5-mm thick) of mouse brain were cut on a Mouse Brain Matrix, frozen, and the specific regions were punched out bilaterally with a microdissecting needle (gauge 0.5 mm) under a stereomicroscope. For each region, we took samples every 4 hours, starting at ZT0 (Zeitgaber time 0; the time of lights on), for 24 hours (6 time-point samples for each region), and we pooled the samples from the different time points. We independently sampled each region twice (n=2). These samples were purified their RNA, and measured with Affymetrix GeneChip Mouse Genome 430 2.0 arrays. Expression values were then summarized with the RMA method. After several analysis with the expression data, the data and analysis results were stored in the BrainStars database. The database has a REST-like Web API interface for accessing from your Web applications. This document shows how to access the database via our Web API.

Proper citation: BrainStars (RRID:SCR_005810) Copy   

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http://www.garban.org/garban/home.php

THIS RESOURCE IS NO LONGER IN SERVICE, documented on July 12, 2012. GARBAN is a tool for analysis and rapid functional annotation of data arising from cDNA microarrays and proteomics techniques. GARBAN has been implemented with bioinformatic tools to rapidly compare, classify, and graphically represent multiple sets of data (genes/ESTs, or proteins), with the specific aim of facilitating the identification of molecular markers in pathological and pharmacological studies. GARBAN has links to the major genomic and proteomic databases (Ensembl, GeneBank, UniProt Knowledgebase, InterPro, etc.), and follows the criteria of the Gene Ontology Consortium (GO) for ontological classifications. Source may be shared: e-mail garban (at) ceit.es. Platform: Online tool

Proper citation: GARBAN (RRID:SCR_005778) Copy   

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http://www.bwfund.org/

The Burroughs Wellcome Fund is an independent private foundation dedicated to advancing the biomedical sciences by supporting research and other scientific and educational activities. Within this broad mission, BWF has two primary goals: * To help scientists early in their careers develop as independent investigators * To advance fields in the basic biomedical sciences that are undervalued or in need of particular encouragement BWF''s financial support is channeled primarily through competitive peer-reviewed award programs. * BWF''s endowment: $586.8 million at the end of FY 2009 * BWF approved $26.4 million in grants during FY 2009 BWF makes grants primarily to degree-granting institutions on behalf of individual researchers, who must be nominated by their institutions. To complement these competitive award programs, BWF also makes grants to nonprofit organizations conducting activities intended to improve the general environment for science. A Board of Directors comprising distinguished scientists and business leaders governs BWF. BWF was founded in 1955 as the corporate foundation of the pharmaceutical firm Burroughs Wellcome Co. In 1993, a generous gift from the Wellcome Trust in the United Kingdom, enabled BWF to become fully independent from the company, which was acquired by Glaxo in 1995. BWF has no affiliation with any corporation.

Proper citation: Burroughs Wellcome Fund (RRID:SCR_005772) Copy   

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http://corneliu.henegar.info/FunCluster.htm

FunCluster is a genomic data analysis algorithm which performs functional analysis of gene expression data obtained from cDNA microarray experiments. Besides automated functional annotation of gene expression data, FunCluster functional analysis aims to detect co-regulated biological processes through a specially designed clustering procedure involving biological annotations and gene expression data. FunCluster''''s functional analysis relies on Gene Ontology and KEGG annotations and is currently available for three organisms: Homo Sapiens, Mus Musculus and Saccharomyces Cerevisiae. FunCluster is provided as a standalone R package, which can be run on any operating system for which an R environment implementation is available (Windows, Mac OS, various flavors of Linux and Unix). Download it from the FunCluster website, or from the worldwide mirrors of CRAN. FunCluster is provided freely under the GNU General Public License 2.0. Platform: Windows compatible, Mac OS X compatible, Linux compatible, Unix compatible

Proper citation: FunCluster (RRID:SCR_005774) Copy   

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http://vibez.informatik.uni-freiburg.de/

An imaging and image analysis framework for virtual colocalization studies in larval zebrafish brains, currently available for 72hpf, 48hpf and 96hpf old larvae. ViBE-Z contains a database with precisely aligned gene expression patterns (1����m^3 resolution), an anatomical atlas, and a software. This software creates high-quality data sets by fusing multiple confocal microscopic image stacks, and aligns these data sets to the standard larva. The ViBE-Z database and atlas are stored in HDF5 file format. They are freely available for download. ViBE-Z provides a software that automatically maps gene expression data with cellular resolution to a 3D standard larval zebrafish (Danio rerio) brain. ViBE-Z enhances the data quality through fusion and attenuation correction of multiple confocal microscope stacks per specimen and uses a fluorescent stain of cell nuclei for image registration. It automatically detects 14 predefined anatomical landmarks for aligning new data with the reference brain. ViBE-Z performs colocalization analysis in expression databases for anatomical domains or subdomains defined by any specific pattern. The ViBE-Z database, atlas and software are provided via a web interface.

Proper citation: ViBE-Z (RRID:SCR_005895) Copy   

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http://www.cbs.dtu.dk/ws/ws.php?entry=BLASTatlas

The BLASTatlas is a tool that is useful for mapping and visualizing whole genome homology of genes and proteins within a reference strain compared to other strains or species of one or more prokaryotic organisms using either blastp, blastn, tblastn, or blastx. DNA structural information is also included in the atlas to visualize the DNA chromosomal context of regions. Additional information can be added to these plots. The tool is SOAP compliant and WSDL (web services description language) files are available with programming examples available in Perl. The resolution is per-residue or per nucleotide depending on the regime of the blast search: For each annotation in the reference genome, the best hit in the database genome is found using one of the above algorithms. Each matching or mismatching residue/nucleotide of the best hit (based on BLAST score) is then mapped back to the genome sequence, using the coordinates provided in the annotations. By providing an interoperable method to carry out whole genome visualization of homology, this service offers bioinformaticians as well as biologists an easy-to-adopt workflow that can be directly called from the programming language of the user, hence enabling automation of repeated tasks. This tool can be relevant in many pangenomic as well as in metagenomic studies, by giving a quick overview of clusters of insertion sites, genomic islands and overall homology between a reference sequence and a data set.

Proper citation: BLASTatlas - Mapping of whole genome homology (RRID:SCR_005891) Copy   

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http://great.stanford.edu/public/html/splash.php

Data analysis service that predicts functions of cis-regulatory regions identified by localized measurements of DNA binding events across an entire genome. Whereas previous methods took into account only binding proximal to genes, GREAT is able to properly incorporate distal binding sites and control for false positives using a binomial test over the input genomic regions. GREAT incorporates annotations from 20 ontologies and is available as a web application. The utility of GREAT extends to data generated for transcription-associated factors, open chromatin, localized epigenomic markers and similar functional data sets, and comparative genomics sets. Platform: Online tool

Proper citation: GREAT: Genomic Regions Enrichment of Annotations Tool (RRID:SCR_005807) Copy   

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http://www.ict.csiro.au/staff/stephen.wan/csibs/

A software tool designed to aid researchers in browsing through scientific literature. As one reads an online article and encounters a citation that looks important, CSIBS creates a preview summary of the cited document. The key innovation is the contextual tailoring of the automatically generated summaries using the citation and its surrounding text. As this context changes, so too does the citation-specific summary portion of the preview, which contains contextually-relevant sentences extracted from the cited document. The CSIBS preview presents relevant information required to appraise the citation, containing meta-data about the reference, the abstract and the citation-specific summary. Thus, CSIBS, alleviates information overload by enabling the reader to determine whether or not to invest time in exploring the cited article further. Reference, http://www.sciencedirect.com/science/article/pii/S1570826810000181

Proper citation: CSIBS (RRID:SCR_005889) Copy   

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http://www.curehunter.com/public/showTopPage.do

CureHunter is the only fully integrated scientific search, data retrieval and analysis engine on the web that can read the entire US National Library of Medicine Medline Archive and automatically extract and quantify the evidence for successful clinical outcomes of all known drugs for all known human diseases. * For patients we provide low-cost Summary PDF Reports with all drug evidence for all known cures or symptom improvement * For medical professionals CureHunter on-line access delivers decision support in 10-20 seconds of real clinical time to make an evidence check as SOP as a BP or Temp * For pharma research scientists we offer powerful data export functions that deliver over 1.5 million specific clinical outcome data points to new drug discovery software Use the CureHunter Research Interface: * Discover new potential off-label applications * Export data and apply custom analytics * 1-click drug performance meta-analyses * Keep up-to-date on the latest developments in your field * Optimize formularies with total evidence-based objectivity * RSS Feeds for Tracking Pharma Products

Proper citation: CureHunter (RRID:SCR_005804) Copy   

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http://www.hormone.org/

A portal for hormone-related health information for the public, physicians, allied health professionals and the media. It serves as a resource for the public by promoting the prevention, treatment and cure of hormone-related conditions through outreach and education. It provides free educational materials, public forums, physician referral service, and media education campaigns. It offers a library of educational materials and programs covering a wide range of endocrine topics, including adrenal disorders, breast cancer, diabetes, osteoporosis, stress, thyroid disease and cancer.

Proper citation: Hormone Health Network (RRID:SCR_005765) Copy   

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https://www.google.com/docs/about/

Authoring tool to create, share, and collaborate on the web with documents, spreadsheets, presentations, and more in real time. All your changes are saved automatically in Drive.

Proper citation: Google Docs (RRID:SCR_005886) Copy   

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http://manuals.bioinformatics.ucr.edu/home/R_BioCondManual#GOHyperGAll

To test a sample population of genes for overrepresentation of GO terms, the R/BioC function GOHyperGAll computes for all GO nodes a hypergeometric distribution test and returns the corresponding p-values. A subsequent filter function performs a GO Slim analysis using default or custom GO Slim categories. Basic knowledge about R and BioConductor is required for using this tool. Platform: Windows compatible, Mac OS X compatible, Linux compatible, Unix compatible, THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 16,2025.

Proper citation: GOHyperGAll (RRID:SCR_005766) Copy   

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https://edwardslab.bmcb.georgetown.edu/trac/GlycoPeptideSearch/#WelcometoGlycoPeptideSearch

GlycoPeptideSearch (GPS) simplifies data interpretation of N-glycopeptide CID MS/MS datasets by searching for glycopeptide results consistent with MS/MS spectra. Results are tabulated in Excel format. Accelerate and simplify interpretation of N-glycopeptide CID MS/MS spectra using GlycoPeptideSearch (GPS). This tool is designed for tandem mass-spectra acquired from proteolytic digests of purified glycoproteins modified with N-glycans and analyzed by LC-MS/MS and CID. The search yields an Excel spreadsheet of N-glycopeptide matches consistent with the spectra. GPS requires two files as input - an mzXML (or other open spectral format) file of glycopeptide CID tandem mass-spectra and a text file (.txt) of peptide sequences containing the N-linked glycosylation motif NXS/T. Spectral datafiles must be converted from raw vendor formats, such as .RAW or .wiff, to an open peak list format (mzXML preferred). In addition to these two input files, the user must specify one or more glycan databases (provided in the software package). The database(s) selected by the user will be used to match glycan structures in the glycopeptide spectra. The output is an Excel spreadsheet with one or more rows for spectra within the dataset that contain evidence of glycoprotein fragmentation, paired with one or more proposed glycopeptide matches for each spectrum. Glycopeptide matches consist of a peptide-glycan pair, with the peptide drawn from the user-supplied peptide file, and the glycan selected from a glycan database(s). The human subset of the GlycomeDB glycan database is provided, and N-linked glycans are automatically selected from it. GPS interprets glycopeptide CID MS/MS spectra by first requiring MS/MS spectra contain evidence of glycopeptide fragmentation - the oxonium ion peaks (m/z 204 - Hex, m/z 366 - HexNAc), and N-glycopeptide core specific peaks (peptide, peptide + HexNAc, peptide + HexNAc-HexNAc, peptide + HexNAc-HexNAc-Hex). For spectra that meet these initial criteria, for a particular peptide, a mass-based search of one or more glycan databases looks for glycans which capture the remaining mass of the spectral precursor. Additional spectral information may be used to narrow the number of matches, and equivalent glycan topologies may be collapsed to a single peptide-glycan pair. GPS also provides N-glycan compositions with the necessary additional mass, even if no glycan with the composition is present in the glycan database(s). GPS can either be run from the command-line or by using its graphical user interface. We recommend the msconvert (or MSConvertGUI) software from the ProteoWizard project to convert spectral datafiles from vendor formats such as .wiff and .RAW into mzXML.

Proper citation: GlycoPeptideSearch (RRID:SCR_005767) Copy   

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https://gene-atlas.brainminds.jp/

Database of gene expression in the marmoset brain.Comparative anatomy of marmoset and mouse cortex from genomic expression. Atlas comparing brain of neonatal marmoset with mouse using in situ hybridization.

Proper citation: Expression Atlas of the Marmoset (RRID:SCR_005760) Copy   

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http://www.cancer.fi/syoparekisteri/en/

The Finnish Cancer Registry maintains a nation-wide database on all cancer cases in Finland going back to 1953. It is also an internationally active institute for statistical and epidemiological cancer research. The Mass Screening Registry is a department of the Finnish Cancer Registry, and is responsible of planning and evaluating national cancer screening programs in Finland. The site contains information on cancer research and up to date statistics on the prevalence of different types of cancer in Finland, the Nordic countries and on a global level. The web pages include information for participants in cancer screening and for professionals involved in organizing such screening.

Proper citation: Finnish Cancer Registry (RRID:SCR_005881) Copy   

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http://snp-magma.sourceforge.net

Software that utilizes a multiobjective evolutionary algorithm for genetic mapping. It is based on a the ECJ evolutionary software package written by Sean Luke and includes the Strength Pareto Evoluationary Algorithm Version 2 changes for multiobjective analysis. The code runs on any platform with Java Version 2. A genetic mapping project, typically implemented during a search for genes responsible for a disease, requires the acquisition of a set of data from each of a large number of individuals. This data set includes the values of multiple genetic markers. These genetic markers occur at discrete positions along the genome, which is a collection of one or more linear chromosomes. Typing the value of a marker in an individual carries a cost; one seeks to minimize the number of markers typed without excessively jeopardizing the probability of detecting an association between a marker and a disease phenotype. MAGMA is a project which employ''s a multiobjective evolutionary algorithm to solve this problem.

Proper citation: MAGMA (RRID:SCR_005757) Copy   

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http://www.utsouthwestern.edu/education/medical-school/departments/neurology/programs/traumatic-brain-injury/index.html

The 16 affiliated Model System centers throughout the United States are responsible for gathering and submitting the core data set to the national database as well as conducting research studies on traumatic brain injury (TBI) both in collaboration with the other centers and within our own site. Through our research we hope to learn more about TBI and about the issues and concerns of people with TBI. Our goals are to improve the outcome and quality of life for people who have had brain injuries and for those who are caring for the person with a TBI. The North Texas Traumatic Brain Injury Model System (NT-TBIMS) pools the efforts and talents of individuals from the Departments of Neurosurgery, Neurology, Physical Medicine and Rehabilitation, Psychiatry (Neuropsychiatry), and Neuroradiology of the two leading medical institutions in the North Texas region. To be a patient involved in the research being conducted by the North Texas Traumatic Brain Injury Model System you must have suffered a TBI, be at least 16 years of age, have received initial treatment for the TBI at either Parkland Health and Hospital System or Baylor University Medical Center and then have received rehabilitative care at either Parkland, University Hospital Zale-Lipshy, or Baylor Institute for Rehabilitation. The patient must also be able to understand and sign an informed consent to participate or, if unable, have a family member or a legal guardian who understands the form sign the informed consent for the patient.

Proper citation: North Texas Traumatic Brain Injury Model System (RRID:SCR_005879) Copy   

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http://thedata.org/citation/standard

Citation standard that offers proper recognition to authors as well as permanent identification through the use of global, persistent identifiers in place of URLs, which can change frequently. Use of universal numerical fingerprints (UNFs) guarantees to the scholarly community that future researchers will be able to verify that data retrieved is identical to that used in a publication decades earlier, even if it has changed storage media, operating systems, hardware, and statistical program format.

Proper citation: Universal Numerical Fingerprint (RRID:SCR_005912) Copy   

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https://adaptivedisclosure.wordpress.com/aida/

A generic set of components that can perform a variety of tasks, such as learn new pattern recognition models, perform specialized search on resource collections, and store knowledge in a repository. W3C standards are used to make data accessible and manageable with semantic web technologies such as OWL, RDF(S), and SKOS. The AIDA Toolkit is directed at groups of knowledge workers that cooperatively search, annotate, interpret, and enrich large collections of heterogeneous documents from diverse locations. The server offers services for: text indexing and statistics, metadata storage and querying, thesaurus reasoning, annotation, text retrieval, spelling correction, synonym detection, and model learning.

Proper citation: AIDA Toolkit (RRID:SCR_005914) Copy   

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http://icahn.mssm.edu/

Icahn School of Medicine at Mount Sinai, formerly Mount Sinai School of Medicine, is graduate medical school in Manhattan, New York City. Leader in medical and scientific training and education, biomedical research and patient care.

Proper citation: Icahn School of Medicine at Mount Sinai; New York; USA (RRID:SCR_005793) Copy   

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