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http://www.broad.mit.edu/node/305

The Connectivity Map aims to generate a detailed map that links gene patterns associated with disease to corresponding patterns produced by drug candidates and a variety of genetic manipulations. The Connectivity Map is the most comprehensive effort yet for using genomics in a drug-discovery framework. It allows researchers to screen compounds against genome-wide disease signatures, rather than a pre-selected set of target genes. Drugs are paired with diseases using sophisticated pattern-matching methods with a high level of resolution and specificity. To build a Connectivity Map, the Broad Institute brings together molecular biologists, genomics specialists, computational scientists, pharmacologists, chemists and chemical biologists, as well as expertise from across the breadth and depth of medicine.Connectivity map is a large public database of signatures of drugs and genes, and pattern-matching tools to detect similarities among these signatures.The parent site for the Broad Institute at MIT has a software library of software applications developed for use in genetic analysis.

Proper citation: National Institute of Mental Health (NIMH) Human Genetics Initiative (RRID:SCR_007436) Copy   

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http://trans.nih.gov/CEHP/

Trans-NIH project to assess the state of longitudinal and epidemiological research on demographic, social and biologic determinants of cognitive and emotional health in aging adults and the pathways by which cognitive and emotional health may reciprocally influence each other. A database of large scale longitudinal study relevant to healthy aging in 4 domains was created based on responses of investigators conducting these studies and is available for query. The four domains are: * Cognitive Health * Emotional Health * Demographic and Social Factors * Biomedical and Physiologic Factors

Proper citation: Cognitive and Emotional Health Project: The Healthy Brain (RRID:SCR_007390) Copy   

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http://www.nntc.org/

Collects, stores, and distributes samples of nervous tissue, cerebrospinal fluid, blood, and other tissue from HIV-infected individuals. The NNTC mission is to bolster research on the effects of HIV infection on human brain by providing high-quality, well-characterized tissue samples from patients who died with HIV, and for whom comprehensive neuromedical and neuropsychiatric data were gathered antemortem. Researchers can request tissues from patients who have been characterized by: * degree of neurobehavioral impairment * neurological and other clinical diagnoses * history of drug use * antiretroviral treatments * blood and CSF viral load * neuropathological diagnosis The NNTC encourages external researchers to submit tissue requests for ancillary studies. The Specimen Query Tool is a web-based utility that allows researchers to quickly sort and identify appropriate NNTC specimens to support their research projects. The results generated by the tool reflect the inventory at a previous time. Actual availability at the local repositories may vary as specimens are added or distributed to other investigators.

Proper citation: National NeuroAIDS Tissue Consortium (RRID:SCR_007323) Copy   

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http://senselab.med.yale.edu/ordb/

Database of vertebrate olfactory receptors genes and proteins. It supports sequencing and analysis of these receptors by providing a comprehensive archive with search tools for this expanding family. The database also incorporates a broad range of chemosensory genes and proteins, including the taste papilla receptors (TPRs), vomeronasal organ receptors (VNRs), insect olfaction receptors (IORs), Caenorhabditis elegans chemosensory receptors (CeCRs), and fungal pheromone receptors (FPRs). ORDB currently houses chemosensory receptors for more than 50 organisms. ORDB contains public and private sections which provide tools for investigators to analyze the functions of these very large gene families of G protein-coupled receptors. It also provides links to a local cluster of databases of related information in SenseLab, and to other relevant databases worldwide. The database aims to house all of the known olfactory receptor and chemoreceptor sequences in both nucleotide and amino acid form and serves four main purposes: * It is a repository of olfactory receptor sequences. * It provides tools for sequence analysis. * It supports similarity searches (screens) which reduces duplicate work. * It provides links to other types of receptor information, e.g. 3D models. The database is accessible to two classes of users: * General public www users have full access to all the public sequences, models and resources in the database. * Source laboratories are the laboratories that clone olfactory receptors and submit sequences in the private or public database. They can search any sequence they deposited to the database against any private or public sequence in the database. This user level is suited for laboratories that are actively cloning olfactory receptors.

Proper citation: Olfactory Receptor DataBase (RRID:SCR_007830) Copy   

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https://github.com/hakyimlab/PrediXcan

Software tool to detect known and novel genes associated with disease traits and provide insights into the mechanism of these associations. Used to test the molecular mechanisms through which genetic variation affects phenotype.

Proper citation: PrediXcan (RRID:SCR_016739) Copy   

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http://pklab.med.harvard.edu/scde/pagoda.links.html

Software tool for analyzing transcriptional heterogeneity to detect statistically significant ways in which measured cells can be classified. Used to resolve multiple, potentially overlapping aspects of transcriptional heterogeneity by testing gene sets for coordinated variability among measured cells.

Proper citation: PAGODA (RRID:SCR_017099) Copy   

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https://github.com/FeeLab/seqNMF

Software tool for unsupervised discovery of sequential structure. Used to detect sequences in neural data generated by internal behaviors, such as animal thinking or sleeping. Used for unsupervised discovery of temporal sequences in high dimensional datasets in neuroscience without reference to external markers.

Proper citation: seqNMF (RRID:SCR_017068) Copy   

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https://github.com/YosefLab/FastProject

Software Python tool for low dimensional analysis of single-cell RNA-Seq data. Software package for two dimensional visualization of single cell data. Analyzes gene expression matrix and produces output report in which two-dimensional of data can be explored.

Proper citation: FastProject (RRID:SCR_017462) Copy   

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http://www.jwatcher.ucla.edu

Software Java tool for quantitative analysis of behavior. Used to address any theoretical problem that requires complex sequence of actions to be scored by human observer. Runs on microcomputer providing Java Virtual Machine[TM] and has been tested on Windows[TM] and Macintosh[TM] systems. Legacy version (version 0.9) works on older systems (Macintosh OS-9 and Windows-98), while Version 1.0 works well on Macintosh OS-X and Windows XP systems. JWatcher Video works best on Windows XP systems and has reduced functionality running in Macintosh OS-X. JWatcher-Palm can be used to acquire data on Palm OS[TM] equipped device and analyze it on your main computer.

Proper citation: JWatcher (RRID:SCR_017595) Copy   

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https://github.com/epurdom/clusterExperiment

Software open source R package for executing, evaluating and visualizing different clusterings of experimental data, including data from single cell RNA-Seq studies. Software for running and comparing different clusterings of single cell sequencing data.

Proper citation: clusterExperiment (RRID:SCR_017439) Copy   

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http://neuroproteomics.scs.illinois.edu/microMS.htm

Software Python platform for image guided Mass Spectrometry profiling. Provides graphical user interface for automatic cell finding and point based registration from whole slide images. Simplifies single cell analysis with feature rich image processing.

Proper citation: microMS (RRID:SCR_017443) Copy   

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https://www.ncbi.nlm.nih.gov/pubmed/28653482

Software tool to facilitate tractography based deep brain stimulation (DBS) electrode targeting within patient specific stereotactic coordinate system used in operating room.

Proper citation: StimVision (RRID:SCR_017457) Copy   

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http://afni.nimh.nih.gov/afni/

Set of (mostly) C programs that run on X11+Unix-based platforms (Linux, Mac OS X, Solaris, etc.) for processing, analyzing, and displaying functional MRI (FMRI) data defined over 3D volumes and over 2D cortical surface meshes. AFNI is freely distributed as source code plus some precompiled binaries.

Proper citation: Analysis of Functional NeuroImages (RRID:SCR_005927) Copy   

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https://www.braintest.org/brain_test/BrainTest

A portal of online studies that encourage community participation to tackle the most challenging problems in neuropsychiatry, including attention-deficit / hyperactivity disorder, schizophrenia, and bipolar disorder. Our approach is to engage the community and try to recruit tens of thousands of people to spend an hour of their time on our site. You folks will provide data in both brain tests and questionnaires, as well as DNA, and in return, we will provide some information about your brain and behavior. You will also be entered to win amazon.com gift cards. While large collaborative efforts were made in genetics in order to discover the secrets of the human genome, there are still many mysteries about the behaviors that are seen in complex neuropsychiatric syndromes and the underlying biology that gives rise to these behaviors. We know that it will require studying tens of thousands of people to begin to answer these questions. Having you, the public, as a research partner is the only way to achieve that kind of investment. This site will try to reach that goal, by combining high-throughput behavioral assessment using questionnaires and game-like cognitive tests. You provide the data and then we will provide information and feedback about why you should help us achieve our goals and how it benefits everyone in the world. We believe that through this online study, we can better understand memory and attention behaviors in the general population and their genetic basis, which will in turn allow us to better characterize how these behaviors go awry in people who suffer from mental illness. In the end, we hope this will provide better, more personalized treatment options, and ultimately prevention of these widespread and extremely debilitating brain diseases. We will use the data we collect to try to identify the genetic basis for memory and impulse control, for example. If we can achieve this goal, maybe we can then do more targeted research to understand how the biology goes awry in people who have problems with cognition, including memory and impulse control, like those diagnosed with ADHD, Schizophrenia, Bipolar Disorder, and Autism Spectrum Disorders. By participating in our research, you can learn about mental illness and health and help researchers tackle these complex problems. We can''t do it without your help.

Proper citation: Brain Test (RRID:SCR_006212) Copy   

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http://intramural.nimh.nih.gov/gcap/index.htm

Schizophrenia related portal that aims to solve the mystery of genetic predisposition to psychosis, develop new methods for early diagnosis and prevention, and discover new treatments that will cure people suffering from it. Our objectives are to fully characterize: # neurobiological mechanisms related to susceptibility genes for schizophrenia and related clinical disorders; # genetic variation in aspects of cognition and emotionality associated with schizophrenia; and # small molecular targets for novel therapies. A unique feature of this Program is that its diverse scientific resources will be focused on a highly specific scientific agenda, that is to acquire the critical biological information about the susceptibility genes associated with schizophrenia and related illnesses. Our mission and goal, to understand the basic mechanisms of serious mental illness, has again guided us into new areas of research and to new insights. We have found evidence of new genes implicated in the cause of schizophrenia and involved in brain functions related to cognition and emotion and we have begun to explore how genes interact with each other and with the environment to individualize risk for these conditions. We are working now with over 20 genes related to schizophrenia. One of the key developments in our research over the past year has been the emergence of some targets for the development of novel therapeutics. We have discovered a new schizophrenia susceptibility gene, KCNH2, which represents the first clear target for the development of novel treatments. Just in this past year, for example, we published the first extensive statistical analysis of how schizophrenia genes may vary in their risk effects based on different genetic background (Nicodemus et al Hum Gen 2006), the first studies of schizophrenia genes interacting in effecting gene expression in brain (Lipska et al Hum Mol Genetics 2006a, Lipska et al Hum Mol Gen 2006 b); the first evidence that the mechanism of genetic association of NRG1 with schizophrenia involves a novel isoform of the gene in human brain (Law et al PNAS 2006), and the first evidence that MAOA may be linked to mood and impulse control because it effects critical mood regulatory neural networks (Meyer-Lindenberg et al PNAS 2006).

Proper citation: Genes Cognition and Psychosis Program (RRID:SCR_006292) Copy   

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http://vinovia.ncl.ac.uk/emagewebapp/pages/eadhb_home.jsf

Database of a set of standard 3D virtual models at different stages of development from Carnegie Stages (CS) 12-23 (approximately 26-56 days post conception) in which various anatomical regions have been defined with a set of anatomical terms at various stages of development (known as an ontology). Experimental data is captured and converted to digital format and then mapped to the appropriate 3D model. The ontology is used to define sites of gene expression using a set of standard descriptions and to link the expression data to an ''''anatomical tree''''. Human data from stages CS12 to CS23 can be submitted to the HUDSEN Gene Expression Database. The anatomy ontology currently being used is based on the Edinburgh Human Developmental Anatomy Database which encompasses all developing structures from CS1 to CS20 but is not detailed for developing brain structures. The ontology is being extended and refined (by Prof Luis Puelles, University of Murcia, Spain) and will be incorporated into the HUDSEN database as it is developed. Expression data is annotated using two methods to denote sites of expression in the embryo: spatial annotation and text annotation. Additionally, many aspects of the detection reagent and specimen are also annotated during this process (assignment of IDs, nucleotide sequences for probes etc). There are currently two main ways to search HUDSEN - using a gene/protein name or a named anatomical structure as the query term. The entire contents of the database can be browsed using the data browser. Results may be saved. The data in HUDSEN is generated from both from researchers within the HUDSEN project, and from the wider scientific community. The HUDSEN human gene expression spatial database is a collaboration between the Institute of Human Genetics in Newcastle, UK, and the MRC Human Genetics Unit in Edinburgh, UK, and was developed as part of the Electronic Atlas of the Developing Human Brain (EADHB) project (funded by the NIH Human Brain Project). The database is based on the Edinburgh Mouse Atlas gene expression database (EMAGE), and is designed to be an openly available resource to the research community holding gene expression patterns during early human development.

Proper citation: HUDSEN Human Gene Expression Spatial Database (RRID:SCR_006325) Copy   

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http://www.mitre.org/news/digest/archives/2002/neuroinformatics.html

This resource''s long-term goal is to develop informatics methodologies and tools that will increase the creativity and productivity of neuroscience investigators, as they work together to use shared human brain mapping data to generate and test ideas far beyond those pursued by the data''s originators. This resource currently has four major projects supporting this goal: * Database tools: The goal of the NeuroServ project is to provide neuroscience researchers with automated information management tools that reduce the effort required to manage, analyze, query, view, and share their imaging data. It currently manages both structural magnetic resonance image (MRI) datasets and diffusion tensor image (DTI) datasets. NeuroServ is fully web-enabled: data entry, query, processing, reporting, and administrative functions are performed by qualified users through a web browser. It can be used as a local laboratory repository, to share data on the web, or to support a large distributed consortium. NeuroServ is based on an industrial-quality query middleware engine MRALD. NeuroServ includes a specialized neuroimaging schema and over 40 custom Java Server Pages supporting data entry, query, and reporting to help manage and explore stored images. NeuroServ is written in Java for platform independence; it also utilizes several open source components * Data sharing: DataQuest is a collaborative forum to facilitate the sharing of neuroimaging data within the neuroscience community. By publishing summaries of existing datasets, DataQuest enables researchers to: # Discover what data is available for collaborative research # Advertise your data to other researchers for potential collaborations # Discover which researchers may have the data you need # Discover which researchers are interested in your data. * Image quality: The approach to assessing the inherent quality of an image is to measure how distorted the image is. Using what are referred to as no-reference or blind metrics, one can measure the degree to which an image is distorted. * Content-based image retrieval: NIRV (NeuroImagery Retrieval & Visualization) is a work environment for advanced querying over imagery. NIRV will have a Java-based front-end for users to issue queries, run processing algorithms, review results, visualize imagery and assess image quality. NIRV interacts with an image repository such as NeuroServ. Users can also register images and will soon be able to filter searches based on image quality.

Proper citation: MITRE Neuroinformatics (RRID:SCR_006508) Copy   

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https://www.phenxtoolkit.org/

Set of measures intended for use in large-scale genomic studies. Facilitate replication and validation across studies. Includes links to standards and resources in effort to facilitate data harmonization to legacy data. Measurement protocols that address wide range of research domains. Information about each protocol to ensure consistent data collection.Collections of protocols that add depth to Toolkit in specific areas.Tools to help investigators implement measurement protocols.

Proper citation: Phenotypes and eXposures Toolkit (RRID:SCR_006532) Copy   

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https://sites.google.com/site/functionalconnectivitytoolbox/

MATLAB toolbox for performing functional connectivity analyses includes many of the most commonly-used approaches researchers have utilized to date for the identification of condition-dependent functional interactions between fMRI time-series obtained from two or more brain regions. The approaches are either bivariate or multivariate methods defined in time or frequency domains that emphasize distinct features of relationships among the time-series.

Proper citation: Functional Connectivity Toolbox (RRID:SCR_006394) Copy   

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http://users.loni.ucla.edu/~shattuck/brainsuite/

Suite of image analysis tools designed to process magnetic resonance images (MRI) of the human head. BrainSuite provides an automatic sequence to extract genus-zero cortical surface mesh models from the MRI. It also provides a set of viewing tools for exploring image and surface data. The latest release includes graphical user interface and command line versions of the tools. BrainSuite was specifically designed to guide its users through the process of cortical surface extraction. NITRC has written the software to require minimal user interaction and with the goal of completing the entire process of extracting a topologically spherical cortical surface from a raw MR volume within several minutes on a modern workstation. The individual components of BrainSuite may also be used for soft tissue, skull and scalp segmentation and for surface analysis and visualization. BrainSuite was written in Microsoft Visual C using the Microsoft Foundation Classes for its graphical user interface and the OpenGL library for rendering. BrainSuite runs under the Windows 2000 and Windows XP Professional operating systems. BrainSuite features include: * Sophisticated visualization tools, such as MRI visualization in 3 orthogonal views (either separately or in 3D view), and overlayed surface visualization of cortex, skull, and scalp * Cortical surface extraction, using a multi-stage user friendly approach. * Tools including brain surface extraction, bias field correction, voxel classification, cerebellum removal, and surface generation * Topological correction of cortical surfaces, which uses a graph-based approach to remove topological defects (handles and holes) and ensure a tessellation with spherical topology * Parameterization of generated cortical surfaces, minimizing a harmonic energy functional in the p-norm * Skull and scalp surface extraction

Proper citation: BrainSuite (RRID:SCR_006623) Copy   

•   Source: SciCrunch Registry