Tools   Select Another Resource Report Type

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   

•   Source: SciCrunch Registry

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   

•   Source: SciCrunch Registry

http://www.gene-regulation.com/pub/databases.html#transpath

Database on eukaryotic transcription factors, their experimentally-proven binding sites, consensus binding sequences (positional weight matrices) and regulated genes. Its broad compilation of binding sites allows the derivation of positional weight matrices. It can either be used as an encyclopedia, for both specific and general information on signal transduction, or can serve as a network analyzer. Cross-references to important sequence and signature databases such as EMBL/GenBank UniProt/Swiss-Prot InterPro or Ensembl EntrezGene RefSeq are provided. The database is equipped with the tools for data visualization and analysis. It has three modules: the first one is the data, which have been manually extracted, mostly from the primary literature; the second is PathwayBuilder, which provides several different types of network visualization and hence facilitates understanding; the third is ArrayAnalyzer, which is particularly suited to gene expression array interpretation, and is able to identify key molecules within signalling networks (potential drug targets). These key molecules could be responsible for the coordinated regulation of downstream events. Manual data extraction focuses on direct reactions between signalling molecules and the experimental evidence for them, including species of genes/proteins used in individual experiments, experimental systems, materials and methods. This combination of materials and methods is used in TRANSPATH to assign a quality value to each experimentally proven reaction, which reflects the probability that this reaction would happen under physiological conditions. Another important feature in TRANSPATH is the inclusion of transcription factor-gene relations, which are transferred from TRANSFAC, a database focused on transcription regulation and transcription factors. Since interactions between molecules are mainly direct, this allows a complete and stepwise pathway reconstruction from ligands to regulated genes.

Proper citation: TRANSPATH (RRID:SCR_005640) Copy   

•   Source: SciCrunch Registry

http://genemania.org/

Data analysis service to predict the function of your favorite genes and gene sets. Indexing 1,421 association networks containing 266,984,699 interactions mapped to 155,238 genes from 7 organisms. GeneMANIA interaction networks are available for download in plain text format. GeneMANIA finds other genes that are related to a set of input genes, using a very large set of functional association data. Association data include protein and genetic interactions, pathways, co-expression, co-localization and protein domain similarity. You can use GeneMANIA to find new members of a pathway or complex, find additional genes you may have missed in your screen or find new genes with a specific function, such as protein kinases. Your question is defined by the set of genes you input. If members of your gene list make up a protein complex, GeneMANIA will return more potential members of the protein complex. If you enter a gene list, GeneMANIA will return connections between your genes, within the selected datasets. GeneMANIA suggests annotations for genes based on Gene Ontology term enrichment of highly interacting genes with the gene of interest. GeneMANIA is also a gene recommendation system. GeneMANIA is also accessible via a Cytoscape plugin, designed for power users. Platform: Online tool, Windows compatible, Mac OS X compatible, Linux compatible, Unix compatible

Proper citation: GeneMANIA (RRID:SCR_005709) Copy   

•   Source: SciCrunch Registry

http://llama.mshri.on.ca/gofish/GoFishWelcome.html

Software program, available as a Java applet online or to download, allows the user to select a subset of Gene Ontology (GO) attributes, and ranks genes according to the probability of having all those attributes., THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 16,2025.

Proper citation: GoFish (RRID:SCR_005682) Copy   

•   Source: SciCrunch Registry

http://mango.adult-neurogenesis.de

Database of genes concerning adult neurogenesis mapped to cell types and processes that have been curated from the literature. In its present state, the database is restricted to neurogenesis in the hippocampus.

Proper citation: Mammalian Adult Neurogenesis Gene Ontology (RRID:SCR_006176) Copy   

•   Source: SciCrunch Registry

https://www.facebase.org/content/ocdm

To satisfy the need for standardized terminologies several ontologies, we are developing the Ontology of Craniofacial Development and Malformation. When complete, this ontology will describe several realms of anatomy and development relevant to FaceBase, including: * Human craniofacial anatomy, including developmental progressions * Craniofacial malformations * Mouse craniofacial anatomy * Mappings between mouse and human anatomy These ontologies are currently undergoing active development. As a result, these files should be considered very preliminary. They may not work correctly, and contents will almost certainly undergo significant change. Five (sub) ontologies in this zip archive correspond to the categories described above. * OCDM - Ontology of Craniofacial Development and Malformation: currently imports the CHO, CMO, and the CHMMO. * CHO - Craniofacial Human Ontoloogy: normal adult human craniofacial anatomy derived from the FMA. * CMO - Craniofacial Mouse Ontology: normal adult mouse craniofacial anatomy * CHMMO - Craniofacial Human-Mouse Mapping Ontology: mappings of classes in the * CHO to related (homologous) structures in the CMO. CFMO - Craniofacial Malformation Ontology: abnormal human anatomy, includes the CHO All ontologies are in Protege Frames format (requires Protege 3.x). Ontologies refer to other ontologies via the Protege include mechanism. The CHMMO includes the CHO and the CMO. The OCDM (which is the umbrella ontology) includes all of the rest. Future releases will include translations to the OWL language.

Proper citation: OCDM - Ontology of Craniofacial Development and Malformation (RRID:SCR_005999) Copy   

•   Source: SciCrunch Registry

https://pb.apf.edu.au/phenbank/homePage.html

The NHMRC Australian PhenomeBank (APB) is a non-profit repository of mouse strains used in Medical Research. The database allows you to search for murine strains, housed or archived in Australia, carrying mutations in particular genes, strains with transgenic alterations and for mice with particular phenotypes. 1876 publicly available strains, 922 genes, 439 transgenes The APB has two roles: Provide and maintain a central database of genetically modified mice held in Australia either live or as cryopreserved material; Establish and maintain a mouse strain archive. Strains are archived as cryopreserved sperm or embryos.

Proper citation: NHMRC Australian PhenomeBank (RRID:SCR_006149) Copy   

•   Source: SciCrunch Registry

http://www.fimre.org/

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on July 7, 2022. Federation of International Mouse Resources (FIMRe) is a collaborating group of Mouse Repository and Resource Centers worldwide whose collective goal is to archive and provide strains of mice as cryopreserved embryos and gametes, ES cell lines, and live breeding stock to the research community. Goals of the Federation of International Mouse Resources: * Coordinate repositories and resource centers to: ** archive valuable genetically defined mice and ES cell lines being created worldwide ** meet research demand for these genetically defined mice and ES cell lines * Establish consistent, highest quality animal health standards in all resource centers * Provide genetic verification and quality control for genetic background and mutations * Provide resource training to enhance user ability to utilize cryopreserved resources

Proper citation: Federation of International Mouse Resources (RRID:SCR_006137) Copy   

•   Source: SciCrunch Registry

http://www.mousephenotype.org/impress

Contains standardized phenotyping protocols essential for the characterization of mouse phenotypes. IMPReSS holds definitions of the phenotyping Pipelines and mandatory and optional Procedures and Parameters carried out and data collected by international mouse clinics following the protocols defined. This allows data to be comparable and shareable and ontological annotations permit interspecies comparison which may help in the identification of phenotypic mouse-models of human diseases. The IMPC (International Mouse Phenotyping Consortium) core pipeline describes the phenotype pipeline that has been agreed by the research institutions. IMPReSS has a SOAP web service machine interface. The WSDL can be accessed here: http://www.mousephenotype.org/impress/soap/server?wsdl

Proper citation: Impress (RRID:SCR_006160) Copy   

•   Source: SciCrunch Registry

http://www.brain-map.org/api/index.html

API and demo application for accessing the Allen Brain Atlas Mouse Brain data. Data available via the API includes download high resolution images, expression data from a 3D volume, 3D coordinates of the Allen Reference Atlas, and searching genes with similar gene expression profiles using NeuroBlast. Data made available includes: * High resolution images for gene expression, connectivity, and histology experiments, as well as annotated atlas images * 3-D expression summaries registered to a reference space for the Mouse Brain and Developing Mouse Brain * Primary microarray results for the Human Brain and Non-Human Primate * RNA sequencing results for the Developing Human Brain * MRI and DTI files for Human Brain The API consists of the following resources: * RESTful model access * Image download service * 3-D expression summary download service * Differential expression search services * NeuroBlast correlative searches * Image-to-image synchronization service * Structure graph download service

Proper citation: Allen Brain Atlas API (RRID:SCR_005984) Copy   

•   Source: SciCrunch Registry

https://syllabus.med.unc.edu/courseware/embryo_images/

Tutorial that uses scanning electron micrographs (SEMs) as the primary resource to teach mammalian embryology. The 3-D like quality of the micrographs coupled with selected line drawings and minimal text allow relatively easy understanding of the complex morphological changes that occur in utero. Because early human embryos are not readily available and because embryogenesis is very similar across mammalian species, the majority of micrographs that are utilized in this tutorial are of mouse embryos. The remainder are human. This tutorial is divided into units that may be studied in any order. All of the images have a legend that indicates the age of the embryo. If it is a mouse embryo, the approximate equivalent human age is indicated. To minimize labeling, color-coding is widely used. To view the micrographs without color, the cursor may be placed on the image. The SEMs used in this tutorial are from the Kathleen K. Sulik collection. The line drawings have been used with permission from Lippincott Williams & Wilkins and are from the 6th and 7th editions of Langman''s Medical Embryology by T.W. Sadler.

Proper citation: Embryo Images Normal and Abnormal Mammalian Development (RRID:SCR_006297) Copy   

•   Source: SciCrunch Registry

http://www.alzforum.org/

A community building portal dedicated to understanding Alzheimer's disease and related disorders, it reports on the latest scientific findings from basic research to clinical trials, creates and maintains public databases of essential research data and reagents, and produces discussion forums to promote debate, speed the dissemination of new ideas, and break down barriers across disciplines.

Proper citation: Alzheimer's Research Forum (RRID:SCR_006416) Copy   

•   Source: SciCrunch Registry

http://www.stembase.ca

A publicly accessible database containing data on Affymetrix DNA microarray experiments, and Serial Analysis of Gene Expression, mostly on human and mouse stem cell samples and their derivatives to facilitate the discovery of gene functions relevant to stem cell control and differentiation. It has grown in both size and scope into a system with analysis tools that examine either the whole database at once, or slices of data, based on tissue type, cell type or gene of interest. There is currently more than 210 stem cell samples in 60 different experiments, with more being added regularly. The samples were originated by researchers of the Stem Cell Network and processed at the Core Facility of Stemcore Laboratories under the management of Ms. Pearl Campbell in the frame of the Stem Cell Genomics Project. Periodically, new expression data is submitted to the Gene Expression Omnibus (GEO) repository at the National Center for Biotechnological Information, in order to allow researchers to compare the data deposited in StemBase to a large amount of gene expression data sets. StemBase is different from GEO in both focus and scope. StemBase is concerned exclusively with stem cell related data. we are focused in Stem Cell research. We have made a significant effort to ensure the quality and consistency of the data included. This allows us to offer more specialized analysis tools related to Stem Cell data. GEO is intended as a large scale public archive. Deposition in a public repository such as GEO is required by most important scientific journals and it is advantageous for a further diffusion of the data since GEO is more broadly used than StemBase.

Proper citation: StemBase (RRID:SCR_006252) Copy   

•   Source: SciCrunch Registry

http://purl.bioontology.org/ontology/EMAP

A structured controlled vocabulary of stage-specific anatomical structures of the mouse (Mus).

Proper citation: Mouse Gross Anatomy and Development Ontology (RRID:SCR_003891) Copy   

•   Source: SciCrunch Registry

http://purl.bioontology.org/ontology/MPATH

A structured controlled vocabulary of mutant and transgenic mouse pathology phenotypes

Proper citation: Mouse Pathology Ontology (RRID:SCR_003950) Copy   

•   Source: SciCrunch Registry

http://openconnectomeproject.org/

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on January 9, 2023. Connectomes repository to facilitate the analysis of connectome data by providing a unified front for connectomics research. With a focus on Electron Microscopy (EM) data and various forms of Magnetic Resonance (MR) data, the project aims to make state-of-the-art neuroscience open to anybody with computer access, regardless of knowledge, training, background, etc. Open science means open to view, play, analyze, contribute, anything. Access to high resolution neuroanatomical images that can be used to explore connectomes and programmatic access to this data for human and machine annotation are provided, with a long-term goal of reconstructing the neural circuits comprising an entire brain. This project aims to bring the most state-of-the-art scientific data in the world to the hands of anybody with internet access, so collectively, we can begin to unravel connectomes. Services: * Data Hosting - Their Bruster (brain-cluster) is large enough to store nearly any modern connectome data set. Contact them to make your data available to others for any purpose, including gaining access to state-of-the-art analysis and machine vision pipelines. * Web Viewing - Collaborative Annotation Toolkit for Massive Amounts of Image Data (CATMAID) is designed to navigate, share and collaboratively annotate massive image data sets of biological specimens. The interface is inspired by Google Maps, enhanced to allow the exploration of 3D image data. View the fork of the code or go directly to view the data. * Volume Cutout Service - RESTful API that enables you to select any arbitrary volume of the 3d database (3ddb), and receive a link to download an HDF5 file (for matlab, C, C++, or C#) or a NumPy pickle (for python). Use some other programming language? Just let them know. * Annotation Database - Spatially co-registered volumetric annotations are compactly stored for efficient queries such as: find all synapses, or which neurons synapse onto this one. Create your own annotations or browse others. *Sample Downloads - In addition to being able to select arbitrary downloads from the datasets, they have also collected a few choice volumes of interest. * Volume Viewer - A web and GPU enabled stand-alone app for viewing volumes at arbitrary cutting planes and zoom levels. The code and program can be downloaded. * Machine Vision Pipeline - They are building a machine vision pipeline that pulls volumes from the 3ddb and outputs neural circuits. - a work in progress. As soon as we have a stable version, it will be released. * Mr. Cap - The Magnetic Resonance Connectome Automated Pipeline (Mr. Cap) is built on JIST/MIPAV for high-throughput estimation of connectomes from diffusion and structural imaging data. * Graph Invariant Computation - Upload your graphs or streamlines, and download some invariants. * iPad App - WholeSlide is an iPad app that accesses utilizes our open data and API to serve images on the go.

Proper citation: Open Connectome Project (RRID:SCR_004232) Copy   

•   Source: SciCrunch Registry

http://brainarchitecture.org/

Evolving portal that will provide interactive tools and resources to allow researchers, clinicians, and students to discover, analyze, and visualize what is known about the brain's organization, and what the evidence is for that knowledge. This project has a current experimental focus: creating the first brainwide mesoscopic connectivity diagram in the mouse. Related efforts for the human brain currently focus on literature mining and an Online Brain Atlas Reconciliation Tool. The primary goal of the Brain Architecture Project is to assemble available knowledge about the structure of the nervous system, with an ultimate emphasis on the human CNS. Such information is currently scattered in research articles, textbooks, electronic databases and datasets, and even as samples on laboratory shelves. Pooling the knowledge across these heterogeneous materials - even simply getting to know what we know - is a complex challenge that requires an interdisciplinary approach and the contributions and support of the greater community. Their approach can be divided into 4 major thrusts: * Literature Curation and Text Mining * Computational Analysis * Resource Development * Experimental Efforts

Proper citation: Brain Architecture Project (RRID:SCR_004283) Copy   

•   Source: SciCrunch Registry

http://stemcellcommons.org/

Open source environment for sharing, processing and analyzing stem cell data bringing together stem cell data sets with tools for curation, dissemination and analysis. Standardization of the analytical approaches will enable researchers to directly compare and integrate their results with experiments and disease models in the Commons. Key features of the Stem Cell Commons * Contains stem cell related experiments * Includes microarray and Next-Generation Sequencing (NGS) data from human, mouse, rat and zebrafish * Data from multiple cell types and disease models * Carefully curated experimental metadata using controlled vocabularies * Export in the Investigation-Study-Assay tabular format (ISA-Tab) that is used by over 30 organizations worldwide * A community oriented resource with public data sets and freely available code in public code repositories such as GitHub Currently in development * Development of Refinery, a novel analysis platform that links Commons data to the Galaxy analytical engine * ChIP-seq analysis pipeline (additional pipelines in development) * Integration of experimental metadata and data files with Galaxy to guide users to choose workflows, parameters, and data sources Stem Cell Commons is based on open source software and is available for download and development.

Proper citation: Stem Cell Commons (RRID:SCR_004415) Copy   

•   Source: SciCrunch Registry

http://www.brainarchitecture.org/mouse-home

An atlas project whose goal is to enerate brainwide maps of inter-regional neural connectivity that specify the inputs and outputs of every brain region, at a "mesoscopic" level of analysis. A 3D injection viewer is used to view the mouse brain. To determine the outputs of a brain region, anterograde tracers are used which are taken up by neurons locally ("the input"), then transported actively down the axons to the "output regions." The whole brain is then sliced thinly, and each slice is digitally imaged. These 2-D images are reconstructed in 3D. The majority of the resulting 3-D brain image is unlabeled. Only the injected region and its output regions have tracer in them, allowing for identification of this small fraction of the connectivity map. This procedure is repeated identically, to account for individual variability. To determine the inputs to the same brain region as above, a retrograde tracer is injected in the same stereotaxic location ("the input"), and the process is repeated. In order to accumulate data from different mice (each of whom has a slightly different brain shape and size), 3-D spatial normalization is performed using registration algorithms. These gigapixel images of whole-brain sections can be zoomed to show individual neurons and their processes, providing a "virtual microscope." Each sampled brain is represented in about 500 images, each image showing an optical section through a 20 micron-thick slice of brain tissue. A multi-resolution viewer permits users to journey through each brain, following the pathways taken through three-dimensional brain space by tracer-labeled neuronal pathways. A key point is that at the mid-range "mesoscopic" scale, the team expects to assemble a picture of connections that are stereotypical and probably genetically determined in a species-specific manner. By dividing the volume of a hemisphere of the mouse brain into 250 equidistant, predefined grid-points, and administering four different kinds of tracer injections at each grid point -- in different animals of the same sex and age a complete wiring diagram that will be stitched together in "shotgun" fashion from the full dataset.

Proper citation: Mouse Brain Architecture Project (RRID:SCR_004683) Copy   

•   Source: SciCrunch Registry