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https://www.humanbrainproject.eu/

Global, collaborative effort for neuroscience, medicine and computing to understand brain, its diseases and its computational capabilities. Goal is to obtain access to research, data sources, platforms and infrastructures offered by other organisations, and enabling organizations outside HBP to use HBP platforms to pursue their own research. Coordinating these activities is the responsibility of the European Research Programme.

Proper citation: Human Brain Project EU (RRID:SCR_002241) Copy   

•   Source: SciCrunch Registry

http://www.lead-dbs.org/

MATLAB toolbox for deep-brain-stimulation (DBS) electrode reconstructions and visualizations based on postoperative MRI and computed tomography (CT) imaging. The toolbox also facilitates visualization of localization results in 2D/3D, analysis of DBS-electrode placement's effects on clinical results, simulation of DBS stimulations, diffusion tensor imaging (DTI) based connectivity estimates, and fiber-tracking from the VAT to other brain regions (connectomic surgery).

Proper citation: LEAD-DBS (RRID:SCR_002915) Copy   

•   Source: SciCrunch Registry

http://www.bcgsc.ca/platform/bioinfo/software/alea

A computational software toolbox for allele-specific (AS) epigenomics analysis. It incorporates allelic variation data within existing resources, allowing for the identification of significant associations between epigenetic modifications and specific allelic variants in human and mouse cells. It provides a customizable pipeline of command line tools for AS analysis of next-generation sequencing data (ChIP-seq, RNA-seq, etc.) that takes the raw sequencing data and produces separate allelic tracks ready to be viewed on genome browsers. ALEA takes advantage of the available genomic resources for human (The 1000 Genomes Project Consortium) and mouse (The Mouse Genome Project) to reconstruct diploid in-silico genomes for human or hybrid mice under study. Then, for each accompanying ChIP-seq or RNA-seq dataset, it generates two Wiggle track format (WIG) files from short reads aligned differentially to each haplotype.

Proper citation: ALEA (RRID:SCR_006417) Copy   

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http://www.loni.usc.edu/

Biomedical technology resource center specializing in novel approaches and tools for neuroimaging. It develops novel strategies to investigate brain structure and function in their full multidimensional complexity. There is a rapidly growing need for brain models comprehensive enough to represent brain structure and function as they change across time in large populations, in different disease states, across imaging modalities, across age and sex, and even across species. International networks of collaborators are provided with a diverse array of tools to create, analyze, visualize, and interact with models of the brain. A major focus of these collaborations is to develop four-dimensional brain models that track and analyze complex patterns of dynamically changing brain structure in development and disease, expanding investigations of brain structure-function relations to four dimensions.

Proper citation: Laboratory of Neuro Imaging (RRID:SCR_001922) Copy   

•   Source: SciCrunch Registry

http://www.utsouthwestern.edu/education/medical-school/departments/pathology/index.html

The Department of Pathology at UT Southwestern Medical Center is committed to its missions in diagnostics, research, teaching, and resident and fellowship training. Our facilities include approximately 54,000 square feet of lab and office space. Our Department comprises more than 100 of the most outstanding faculty in the country and more than 50 residents and fellows. We are home to more than a dozen graduate students at any given time. The Department of Pathology offers comprehensive, in-depth training in all of the various pathology disciplines, as well as a complete array of subspecialty fellowship programs. It is our view that a strong academic environment with access to state-of-the-art and newly emerging diagnostic technologies is essential to the preparation of any pathologist for professional life in the 21st century, regardless of the ultimate practice setting. Therefore, basic training in our program is enhanced by extensive exposure to modern molecular diagnostics, advanced flow cytometric analysis, and molecular cytogenetics. The Department provides diagnostic services in a variety of clinical settings that include a large county hospital (Parkland Memorial Hospital), two private University Hospitals (Zale-Lipshy and St. Paul), a tertiary care private pediatric hospital (Children''s Medical Center), a large university outpatient clinic (Aston Clinic), and the Dallas VA Medical Center, exposing our residents, fellows, and faculty to the full spectrum of human adult and pediatric disease.

Proper citation: UT Southwestern Medical Center Department of Pathology (RRID:SCR_005713) Copy   

•   Source: SciCrunch Registry

http://www.alzheimers.org/clinicaltrials/

A database of Alzheimer's disease and dementia clinical trials currently in progress at centers throughout the U.S.

Proper citation: AD Clinical Trials Database (RRID:SCR_005863) Copy   

•   Source: SciCrunch Registry

http://www.ddduk.org/

The Deciphering Developmental Disorders (DDD) study aims to find out if using new genetic technologies can help doctors understand why patients get developmental disorders. To do this we have brought together doctors in the 23 NHS Regional Genetics Services throughout the UK and scientists at the Wellcome Trust Sanger Institute, a charitably funded research institute which played a world-leading role in sequencing (reading) the human genome. The DDD study involves experts in clinical, molecular and statistical genetics, as well as ethics and social science. It has a Scientific Advisory Board consisting of scientists, doctors, a lawyer and patient representative, and has received National ethical approval in the UK. Over the next few years, we are aiming to collect DNA and clinical information from 12,000 undiagnosed children in the UK with developmental disorders and their parents. The results of the DDD study will provide a unique, online catalogue of genetic changes linked to clinical features that will enable clinicians to diagnose developmental disorders. Furthermore, the study will enable the design of more efficient and cheaper diagnostic assays for relevant genetic testing to be offered to all such patients in the UK and so transform clinical practice for children with developmental disorders. Over time, the work will also improve understanding of how genetic changes cause developmental disorders and why the severity of the disease varies in individuals. The Sanger Institute will contribute to the DDD study by performing genetic analysis of DNA samples from patients with developmental disorders, and their parents, recruited into the study through the Regional Genetics Services. Using microarray technology and the latest DNA sequencing methods, research teams will probe genetic information to identify mutations (DNA errors or rearrangements) and establish if these mutations play a role in the developmental disorders observed in patients. The DDD initiative grew out of the groundbreaking DECIPHER database, a global partnership of clinical genetics centres set up in 2004, which allows researchers and clinicians to share clinical and genomic data from patients worldwide. The DDD study aims to transform the power of DECIPHER as a diagnostic tool for use by clinicians. As well as improving patient care, the DDD team will empower researchers in the field by making the data generated securely available to other research teams around the world. By assembling a solid resource of high-quality, high-resolution and consistent genomic data, the leaders of the DDD study hope to extend the reach of DECIPHER across a broader spectrum of disorders than is currently possible.

Proper citation: Deciphering Developmental Disorders (RRID:SCR_006171) 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

http://www.birncommunity.org/tools-catalog/human-imaging-database-hid/

THIS RESOURCE IS NO LONGER IN SERVICE. Documented October 5, 2017.

Database management system developed to handle the increasingly large and diverse datasets collected as part of the MBIRN and FBIRN collaboratories and throughout clinical imaging communities at large. The HID can be extended to contain relevant information concerning experimental subjects, assessments of subjects, the experimental data collected, the experimental protocols, and other metadata normally included with experiments.

Proper citation: Human Imaging Database (RRID:SCR_006126) Copy   

•   Source: SciCrunch Registry

http://www-bionet.sscc.ru/sitex/

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on August 19,2019. Analyzing protein structure projection on exon-intron structure of corresponding gene through years led to several fundamental conclusions about structural and functional organization of the protein. According to these results we decided to map the protein functional sites. So we created the database SitEx that keep the information about this mapping and included the BLAST search and 3D similar structure search using PDB3DScan for the polypeptide encoded by one exon, participating in organizing the functional site. This will help: # to study the positions of the functional sites in exon structure; # to make the complex analysis of the protein function; # to exposure the exons that took part in exon shuffling and came from bacterial genomes; # to study the peculiarities of coding the polypeptide structures. Currently, SitEx contains information about 9994 functional sites presented in 2021 proteins described in proteomes of 17 organisms.

Proper citation: SitEx (RRID:SCR_006122) Copy   

•   Source: SciCrunch Registry

http://bishopw.loni.ucla.edu/AIR5/

A tool for automated registration of 3D (and 2D) images within and across subjects and within and sometimes across imaging modalities. The AIR library can easily incorporate automated image registration into site specific programs adapted to your particular needs.

Proper citation: Automated Image Registration (RRID:SCR_005944) Copy   

•   Source: SciCrunch Registry

http://bio-bigdata.hrbmu.edu.cn/diseasemeth/

Human disease methylation database. DiseaseMeth version 2.0 is focused on aberrant methylomes of human diseases. Used for understanding of DNA methylation driven human diseases.

Proper citation: DiseaseMeth (RRID:SCR_005942) Copy   

•   Source: SciCrunch Registry

https://compbio.dfci.harvard.edu/predictivenetworks//

A flexible, open-source, web-based application and data services framework that enables the integration, navigation, visualization and analysis of gene interaction networks. The primary goal of PN is to allow biomedical researchers to evaluate experimentally derived gene lists in the context of large-scale gene interaction networks. The PN analytical pipeline involves two key steps. The first is the collection of a comprehensive set of known gene interactions derived from a variety of publicly available sources. The second is to use these ''known'' interactions together with gene expression data to infer robust gene networks. The regression-based network inference algorithm creates a graph of gene interactions in which cycles may be present (but no self-loops). Based on information-theoretic techniques, a causal gene interaction network is inferred from both prior knowledge (interactions extracted from biomedical literature and structured biological databases) and gene expression data. A prediction model is fitted for each gene, given its parents, enabling assessment of the predictive ability of the network model.

Proper citation: Predictive Networks (RRID:SCR_006110) Copy   

•   Source: SciCrunch Registry

http://jjwanglab.org:8080/gwasdb/

Combines collections of genetic variants (GVs) from GWAS and their comprehensive functional annotations, as well as disease classifications. Used to maximize utilility of GWAS data to gain biological insights through integrative, multi-dimensional functional annotation portal. In addition to all GVs annotated in NHGRI GWAS Catalog, we manually curate GVs that are marginally significant (P value < 10-3) by looking into supplementary materials of each original publication and provide extensive functional annotations for these GVs. GVs are manually classified by diseases according to Disease Ontology Lite and HPO (Human Phenotype Ontology) for easy access. Database can also conduct gene based pathway enrichment and PPI network association analysis for those diseases with sufficient variants. SOAP services are available. You may Download GWASdb SNP. (This file contains all of the significant SNP in GWASdb. In the pvalue column, 0 means this P-value is not reported in the study but it is significant SNP. In the source column, GWAS:A represents the original data in GWAS catalog, while GWAS:B is our curation data which P-value < 10-3)

Proper citation: GWASdb (RRID:SCR_006015) Copy   

•   Source: SciCrunch Registry

http://web.mit.edu/swg/software.htm

Toolbox for post-processing fMRI data. Includes software for comprehensive analysis of sources of artifacts in timeseries data including spiking and motion. Most compatible with SPM processing, but adaptable for FSL as well. * Operating System: MacOS, Windows, Linux * Programming Language: MATLAB * Supported Data Format: ANALYZE

Proper citation: Artifact Detection Tools (RRID:SCR_005994) Copy   

•   Source: SciCrunch Registry

http://www.cafevariome.org/

Clearinghouse and exchange portal for gene variant (mutation) data produced by diagnostics laboratories, offering users a portal through which to announce, discover and acquire a comprehensive listing of observed neutral and disease-causing gene variants in patients and unaffected individuals. Cafe Variome is not a ''''database'''' for the hosting/display/release of data, but a shop window for finding data. As such, it holds only core info for each record, and uses this merely to enable holistic searching across resources. Diagnostics laboratories routinely assess DNA samples from patients with various inherited disorders, and so produce a great wealth of data on the genetic basis of disease. Unfortunately, those data are not usually shared with others. To address this gross deficiency, a novel system has been developed that aims to facilitate the automated transfer of diagnostic laboratory data to the wider community, via an internet based Cafe for routinely exchanging genetic variation data. The flow of research data concerning the genetic basis of health and disease is critical to understanding and developing treatments for a range of genetic diseases. Overall, the project aims to lower the barriers and provide incentives for a willing community to share data, and thereby facilitate the broader exploitation of diagnostic laboratory data. Cafe Variome aims to address the above data flow problems by: # Minimizing the effort required to publish variant data # Ensuring attribution for data creators working in diagnostic laboratories Key elements of the project strategy are: * Data publication will be automated by endowing standard analysis tools used by laboratories with an online data submission function. Submissions will be received by a central Internet depot, which will serve as a place where published datasets are advertised, and subsequently discovered by diverse 3rd parties. * Each dataset will be unambiguously linked with the data submitter''''s identity, and systems devised to facilitate citation of published variant datasets so they can be cited in the literature. Data creators will thus be credited for their contributions. Data submitters can use Cafe Variome to simply announce or publicize their data to the world. To enable this, only core, non-identifiable data is submitted to the central repository, enabling users to search and discover records of interest in the source repository. The data are not automatically handed on to the user (unless intended by the submitters). Hence, the concept is used to deal with the challenge of maximally sharing data whilst fully respecting ethico-legal considerations.

Proper citation: cafe variome (RRID:SCR_006162) 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

http://www.unc.edu/~grwu/Software.html

A software plugin for 3D Slicer that matches morphological signatures of medical images automatically. HAMMER is an acronym for Hierarchical Attribute Matching Mechanism for Elastic Registration (Dinggang Shen, Christos Davatzikos, HAMMER: Hierarchical Attribute Matching Mechanism for Elastic Registration, IEEE Trans. on Medical Imaging, 21(11):1421-1439, Nov 2002) - an elastic registration algorithm for medical images, matching morphological signatures of images in a hierarchical multi-scale regime. White matter lesion (WML) segmentation is a novel multi-spectral WML segmentation protocol via incorporating information from T1-w, T2-w, PD-w and FLAIR MR brain images. (Zhiqiang Lao, Dinggang Shen, Dengfeng Liu, Abbas F Jawad, Elias R Melhem, Lenore J Launer, Nick R Bryan, Christos Davatzikos, Computer-Assisted Segmentation of White Matter Lesions in 3D MR images, Using Pattern Recognition, Academic Radiology, 15(3):300-313, March 2008).

Proper citation: Hammer And WML Modules for 3D Slicer (RRID:SCR_005980) Copy   

•   Source: SciCrunch Registry

http://www.chictr.org/en/

National clinical trial registry by Ministry of Health of China to join World Health Organization International Clinical Trial Registration Platform (WHO ICTRP Primary Registry), and the approved Primary Registry of WHO ICTRP. It registers both Chinese and global clinical trials, receives data from Partner Registers certified by the WHO ICTRP, and submits data to the WHO ICTRP Central Repository for global search. Moreover, based upon the talent and technical platform, consisting of Chinese Evidence-based Medicine Centre of Ministry of Health of China, Virtual Research Centre of Evidence-Based Medicine of Ministry of Education of China, Chinese Cochrane Centre, UK Cochrane Centre and International Clinical Epidemiology Network Resource and Training Centre in West China Hospital, Sichuan University (INCLEN CERTC), ChiCTR is responsible for providing consultations on trial design, central randomization service, guidance on the writing of clinical trial reports and relevant training. WHO takes the lead in establishing the global clinical trial registration system, which is agreed upon by governments from all over the world. There are both ethical and scientific reasons for clinical trial registration. Trial participants expect that their contributions to biomedical knowledge will be used to improve health care for everyone. Open access to information about ongoing and completed trials meets the ethical duty to trial participants, and promotes greater trust and public confidence in clinical research. Furthermore, trial registration ensures that the results of all trials can be tracked down and should help to reduce unnecessary duplication of research through greater awareness of existing trials and results. The mission of ChiCTR is to Unite clinicians, clinical epidemiologists, biostatisticians, epidemiologists and health care managers both at home and abroad, to manage clinical trials in a strict and scientific manner, and to promote their quality in China, so as to provide reliable evidences from clinical trials for health care workers, consumers and medical policy decision makers, and also to use medical resources more effectively to provide better service for Chinese people and all human beings. Any trial performed in human beings is considered as a clinical trial, and should be registered before its implementation. All the registered clinical trials will be granted a unique registration number by WHO ICTRP.

Proper citation: ChiCTR - Chinese Clinical Trial Registry (RRID:SCR_006037) Copy   

•   Source: SciCrunch Registry

http://www.biogrid.org.au

A federated data sharing platform and infrastructure that provides access to real-time clinical, imaging and biospecimen data across jurisdictions, institutions and diseases. The web-based platform provides a secure infrastructure that advances health research by linking privacy-protected and ethically approved data among a wide network of health collaborators. Access to de-identified health records data is granted to authorized researchers after an application process so patient privacy and intellectual property are protected. BioGrid Australia''s approved researchers are provided access to multiple institutional databases, via the BioGrid interface, preventing gaps in patient records and research analysis. This legal and ethical arrangement with participating collaborators allows BioGrid to connect data through a common platform where data governance and access is managed by a highly skilled team. Data governance, security and ethics are at the core of BioGrid''s federated data sharing platform that securely links patient level clinical, biospecimen, genetic and imaging data sets across multiple sites and diseases for the purpose of medical research. BioGrid''s infrastructure and data management strategies address the increasing need by authorized researchers to dynamically extract and analyze data from multiple sources whilst protecting patient privacy. BioGrid has the capability to link data with other datasets, produce tailored reports for auditing and reporting and provide statistical analysis tools to conduct more advanced research analysis. In the health sector, BioGrid is a trusted independent virtual real-time data repository. Government investment in BioGrid has facilitated a combination of technology, collaboration and ethics approval processes for data sharing that exist nowhere else in the world.

Proper citation: BioGrid Australia (RRID:SCR_006334) Copy   

•   Source: SciCrunch Registry