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http://www.jax.org/mmrrc/

Center for mutant mouse research and distribution. The objectives of the JAX MMRRC are to: identify and evaluate biomedically-significant mice, import/acquire and archive mouse strains, distribute mouse strains, and operate a control program to ensure genetic stability.

Proper citation: Mutant Mouse Resource and Research Center - Jackson Laboratory (RRID:SCR_016446) Copy   

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https://github.com/UMCU-RIBS/ALICE

Software tool for automatic localization of intra-cranial electrodes for clinical and high density grids. Software for coregistering high density ECoG grids to MRI anatomy.

Proper citation: ALICE (RRID:SCR_017463) Copy   

•   Source: SciCrunch Registry

https://github.com/rbutleriii/Clinotator

Software that performs clinical interpretation of ambiguous ClinVar annotations. This software takes batches of variants as input and queries NCBI eutilities to generate scoring metrics.

Proper citation: Clinotator (RRID:SCR_016054) Copy   

•   Source: SciCrunch Registry

http://www.ohsu.edu/xd/research/centers-institutes/neurology/alzheimers/research/data-tissue/biomarkers-genetics.cfm

A center that works with the Oregon Alzheimer's Disease Center's Data Core, and collects and stores tissue samples, family history and genotype data of various populations. These include samples and data from subjects from the following sources: OADC clinical studies, the Oregon Brain Aging Study, the Community Brain Donor Program, the Preventing Cognitive Decline with Alternative Therapies program (informally called the Dementia Prevention Study or DPS), the African American Dementia and Aging Project, and the Klamath Exceptional Aging Project. The collected data samples include genomic DNA, lymphoblast cell lines, genome-wide and candidate region SNP marker data, APOE, AD candidate gene markers.

Proper citation: Layton Center Biomarkers and Genetics (RRID:SCR_008824) Copy   

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http://www.framinghamheartstudy.org/

A longitudinal, epidemiologic study to identify the common risk factors or characteristics that contribute to cardiovascular disease by following its development over a long period of time in a large group of participants who had not yet developed overt symptoms or suffered a heart attack or stroke. Since that time the FHS has studied three generations of participants resulting in biological specimens and data from nearly 15,000 participants. Since 1994, two groups from minority populations, including related individuals have been added to the FHS. FHS welcomes proposals from outside investigators for data and biospecimens. The researchers recruited 5,209 men and women between the ages of 30 and 62 from the town of Framingham, Massachusetts, and began the first round of extensive physical examinations and lifestyle interviews that they would later analyze for common patterns related to CVD development. Since 1948, the subjects have continued to return to the study every two years for a detailed medical history, physical examination, and laboratory tests, and in 1971, the Study enrolled a second generation - 5,124 of the original participants'''' adult children and their spouses - to participate in similar examinations. In 1994, the need to establish a new study reflecting a more diverse community of Framingham was recognized, and the first Omni cohort of the Framingham Heart Study was enrolled. In April 2002 the Study entered a new phase, the enrollment of a third generation of participants, the grandchildren of the Original Cohort. In 2003, a second group of Omni participants was enrolled. Over the years, careful monitoring of the Framingham Study population has led to the identification of major CVD risk factors, as well as valuable information on the effects of these factors such as blood pressure, blood triglyceride and cholesterol levels, age, gender, and psychosocial issues. Risk factors for other physiological conditions such as dementia have been and continue to be investigated. In addition, the relationships between physical traits and genetic patterns are being studied. FHS clinical and research data is stored in the dbGaP and NHLBI Repository repositories and may be accessed by application. Please check the following repositories before applying for data through FHS. Investigators seeking data that is not available through dbGaP or BioLINCC or seeking biological specimens may submit a proposal through the FHS web-based research application. The FHS data repository may be accessed through this FHS website, under the For Researchers link, then Description of Data, in order to determine if and how the desired data is stored. Proposals may involve the use of existing data, the collection of new data, either directly from participants or from previously collected samples, images, or other materials (e.g., medical records). The FHS Repository also has biological specimens available for genetic and non-genetic research proposals. Specimens include urine, blood and blood products, as well as DNA.

Proper citation: Framingham Heart Study (RRID:SCR_008963) Copy   

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http://psychiatry.stanford.edu/alzheimer/files/gpkt.pdf

50 question test devised by Javaid Sheikh, M.D., and Jerome A. Yesavage, M.D., of the Department of Psychiatry and Behavioral Sciences at Stanford University School of Medicine, to test one''s knowledge of certain aspects of geriatric psychiatry, including five broad areas: psychodynamics and psychotherapy, cognitive assessment, psychosocial and developmental aspects, psychopharmacology, and clinical syndromes.

Proper citation: Geriatric Psychiatry Knowledge Test (RRID:SCR_009029) Copy   

•   Source: SciCrunch Registry

http://www.chang-gung.com/

Stroke patient registry containing thousands of patient CT scans, primarily as well as follow up medical information.

Proper citation: Stroke Registry in Chang Gung Healthcare System (RRID:SCR_011083) Copy   

•   Source: SciCrunch Registry

http://www.gimias.org

A workflow-oriented environment focused on biomedical image computing and simulation. The open source framework is extensible through plug-ins and is focused on building research and clinical software prototypes. Gimias has been used to develop clinical prototypes in the fields of cardiac imaging and simulation, angiography imaging and simulation, and neurology.

Proper citation: GIMIAS (RRID:SCR_009545) Copy   

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http://hivdb.stanford.edu/

The Stanford University HIV Drug Resistance Database is a curated public database designed to represent, store, and analyze the different forms of data underlying HIVs drug resistance. HIVDB has three main types of content: (1) Database queries and references, (2) Interactive programs, and (3) Educational resources. Database queries are designed primarily for researchers studying HIV drug resistance. The interactive programs and educational resources are designed for both researchers and those wishing to learn more about HIV drug resistance. 1.DATABASE QUERY AND REFERENCE PAGES Genotype-Treatment Correlations This Genotype-Treatment section of the database links to 15 interactive query pages that explore the relationship between treatment with HIV-1 antiretroviral drugs (ARVs) and mutations in HIV reverse transcriptase (RT), protease, and integrase. There are five types of interactive query pages: Treatment Profiles (Protease and RT inhibitors) Mutation Profiles (Protease and RT mutations) Detailed Treatment Queries (Protease, RT, and integrase inhibitors) Detailed Mutation Queries (Protease, RT, and integrase mutations) Mutation Prevalence According to Subtype and Treatment Genotype-Phenotype Correlations The main page of the Genotype-Phenotype Correlations section links to four interactive query pages: three dynamically updated data summaries and one regularly updated downloadable dataset. Drug Resistance Positions Query for levels of resistance associated with known drug resistance mutations Detailed Phenotype Queries Queries for levels of resistance associated with individual mutations or mutation combinations at all positions of protease, RT, and integrase Patterns of Drug Resistance Mutations Downloadable Reference Dataset Genotype-Clinical Correlations This part of the database has two main sections: Clinical Trials Datasets Summaries of Clinical Studies References This part of the database has two main sections: one with summaries of the data from each of the references in HIVDB and one in which every primate immunodeficiency virus sequence in GenBank is annotated according to its presence or absence in HIVDB. Studies in HIVDB GenBank <=> HIVDB New Submissions Approximately every three months, the New Submissions section lists the studies that have been entered into HIVDB. The study title links to the introductory page of the study in the References section. Database Statistics (http://hivdb.stanford.edu/pages/HIVdbStatistics.html) 2. INTERACTIVE PROGRAMS HIVDB has seven main interactive programs. 1. HIVdb Program Mutation List Analysis Sequence Analysis HIVdb Output Sierra Web Service Release Notes Algorithm Specification Interface (ASI) 2. HIValg Program 3. HIVseq Program 4. Calibrated Population Resistance (CPR) tool 5. Mutation ARV Evidence Listing (MARVEL) 6. ART-AiDE 7. Rega HIV-1 Subtyping tool Three programs in the HIV Drug Resistance Database share a common code base: HIVseq, HIVdb, and HIValg. HIVseq accepts user-submitted protease, RT, and integrase sequences, compares them to the consensus subtype B reference sequence, and uses the differences as query parameters for interrogating the HIV Drug Resistance database (Shafer, D Jung, & B Betts, Nat Med 2000; Rhee SY et al. AIDS 2006). The query result provides users with the prevalence of protease, RT and integrase mutations according to subtype and PI, nucleoside RT inhibitor (NRTI), non-nucleoside RT inhibitor (NNRTI), and integrase inhibitor (INI) exposure. This allows users to detect unusual sequence results immediately so that the person doing the sequencing can check the primary sequence output while it is still on the desktop. In addition, unexpected associations between sequences or isolates can be discovered by immediately retrieving data on isolates sharing one or more mutations with the sequence. There are three ways in which the HIVdb program can be used: (i) entering a list of protease and RT mutations, (ii) entering a complete sequence containing protease, RT, and/or integrase, and (iii) using a Web Service. HIVdb is an expert system that accepts user-submitted HIV-1 pol sequences and returns inferred levels of resistance to 20 FDA-approved ARV drugs including 8 PIs, 7 NRTIs, 4 NNRTIs, and - with this update - one INI. In the HIVdb system, each HIV-1 drug resistance mutation is assigned a drug penalty score and a comment; the total score for a drug is derived by adding the scores of each mutation associated with resistance to that drug. Using the total drug score, the program reports one of the following levels of inferred drug resistance: susceptible, potential low-level resistance, low-level resistance, intermediate resistance, and high-level resistance. HIValg is designed for users interested in comparing the results of different algorithms or who are interested in comparing and evaluating existing and newly developed algorithms. The ability to develop new algorithms that can be run on the HIV Drug Resistance Database depends on the Algorithm Specific Interface (ASI) compiler (Shafer & Betts JCM 2003). Submission of Sequences and Mutations For each of the three programs, sequences can be entered using either the Sequence Analysis Form or the Mutation List form. 3. EDUCATIONAL RESOURCES HIVDB contains several regularly updated sections summarizing data linking RT, protease, and integrase mutations and antiretroviral drugs (ARVs). These sections include (i) tabular summaries of the major mutations associated with each ARV class, (ii) detailed summaries of the major, minor, and accessory mutations associated with each ARV, (iii) the comments used by the HIVdb program, (iv) the scores used by the HIVdb program, (v) clinical studies in which baseline drug resistance mutations have been correlated with the virological response (clinical outcome) to a specific ARV, (vi) mutations that can be used for drug resistance surveillance, and (vii) a two-page PDF handout. 1. Drug Resistance Summaries Tabular Drug Resistance Summaries by ARV Class Detailed Drug Resistance Summaries by ARV Drug Resistance Mutation Comments Used by the HIVdb Program Drug Resistance Mutation Scores Used by the HIVdb Program Genotype-Clinical Outcome Correlation Studies 2. Surveillance Drug-Resistance Mutation List Section 3. PDF Handout Grant Support 1. National Institute for Allergy and Infectious Diseases (NIAID, NIH): Online HIV Drug Resistance Database (PI: Robert W. Shafer, MD, 1R01AI68581-01A1), 04/01/06 - 3/31/11 2. National Institute for Allergy and Infectious Diseases (NIAID, NIH) supplement to the grant Identification of Multidrug-Resistant HIV-1 Isolates (PI: Robert W. Shafer, MD, AI46148-01): Supplement provided 1999-2005. 3. NIH/NIGMS Program Project on AIDS Structural Biology Program Project: Targeting Ensembles of Drug Resistant Protease Variants (PI: Celia Schiffer, PhD, University of Massachusetts): 2002-2007 4. University-wide AIDS Research Program (CR03-ST-524). Community collaborative award: Optimizing Clinical HIV Genotypic Resistance Interpretation: Principal Investigators: Robert W. Shafer, MD and W. Jeffrey Fessel MD (Kaiser Permanente Medical Care Program): 2004-2005 5. Stanford University Bio-X Interdisciplinary Initiative: HIV Gene Sequence Analysis for Drug Resistance Studies: A Pharmacogenetic Challenge Principal Investigators: Robert W. Shafer, MD and Daphne Koller, Ph.D. (Computer Science): 2000-2002

Proper citation: Stanford University HIV Drug Resistance Database (RRID:SCR_006631) Copy   

•   Source: SciCrunch Registry

http://www.research.va.gov/programs/tissue_banking/als/

A human tissue bank that collects, processes, stores and gives out research specimens for future scientific studies. Presently, the VABBB is obtaining neurologic tissue specimens from Veterans who suffer from amyotrophic lateral sclerosis (ALS) and other illnesses that affect Veterans, along with relevant clinical data, essential for research. Currently, neither the cause nor prevention of ALS is known. Medical researchers are currently examining environmental, toxic, genetic, traumatic, medical, and occupational influences as possible contributors to the development and progression of ALS. Veterans have a higher risk of developing ALS compared with non-Veterans; however, the reasons for this higher risk are currently unknown. Any Veteran with ALS in the U.S. may enroll in the VABBB.

Proper citation: VA Biorepository Brain Bank (RRID:SCR_006546) Copy   

•   Source: SciCrunch Registry

https://www.iscaconsortium.org/

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on June 22, 2022. A rapidly growing group of clinical cytogenetics and molecular genetics laboratories committed to improving quality of patient care related to clinical genetic testing using new molecular cytogenetic technologies including array comparative genomic hybridization (aCGH) and quantitative SNP analysis by microarrays or bead chip technology. They improve clinical care by providing a large publicly available database and forum where clinicians and researchers can share knowledge to expedite the understanding of copy number variation (CNV) in an abnormal population. The ISCA database contains whole genome array data from a subset of the ISCA Consortium clinical diagnostic laboratories. Array analysis was carried out on individuals with phenotypes including intellectual disability, autism, and developmental delay. Efforts of the Consortium include: # Clinical Utility: The ISCA Consortium has made recommendations regarding the appropriate clinical indications for cytogenetic array testing (Miller et al. AJHG 2010, PMID: 20466091). Currently, discussions are focused on pediatric applications for children with unexplained developmental delay, intellectual disability, autism and other developmental disabilities. A separate committee has been developed to address appropriate cancer genetic applications (http://www.urmc.rochester.edu/ccmc/). # Evidence-based standards for cytogenomic array design: The Consortium will develop recommendations for standards for the design, resolution and content of cytogenomic arrays using an evidence-based process and an international panel of experts in clinical genetics, clinical laboratory genetics (cytogenetics and molecular genetics), genomics and bioinformatics. This design is intended to be platform and vendor-neutral (common denominator is genome sequence coordinates), and is a dynamic process with input from the broader genetics community and evidence-based review by the expert panel (which will evolve into a Standing Committee with international representation). # Public Database for clinical and research community: It is essential that publicly available databases be created and maintained for cytogenetic array data generated in clinical testing laboratories. The ISCA data will be held in dbGaP and dbVar at NCBI/NIH and curated by a committee of clinical genetics laboratory experts. The very high quality of copy number data (i.e., deletions and duplications) coming from clinical laboratories combined with expert curation will produce an invaluable resource to the clinical and research communities. # Standards for interpretation of cytogenetic array results: Using the ISCA Database, along with other genomic and genetics databases, the Consortium will develop recommendations for the interpretation and reporting of pathogenic vs. benign copy number changes as well as imbalances of unknown clinical significance.

Proper citation: ISCA Consortium (RRID:SCR_006168) Copy   

•   Source: SciCrunch Registry

http://www.ncbi.nlm.nih.gov/clinvar/

Archive of aggregated information about sequence variation and its relationship to human health. Provides reports of relationships among human variations and phenotypes along with supporting evidence. Submissions from clinical testing labs, research labs, locus-specific databases, expert panels and professional societies are welcome. Collects reports of variants found in patient samples, assertions made regarding their clinical significance, information about submitter, and other supporting data. Alleles described in submissions are mapped to reference sequences, and reported according to HGVS standard.

Proper citation: ClinVar (RRID:SCR_006169) Copy   

•   Source: SciCrunch Registry

http://cahub.cancer.gov/about/

THIS RESOURCE IS NO LONGER IN SERVICE. Documented July 5, 2018. A national center for biospecimen science and standards to advance cancer research and treatment. It was created in response to the critical and growing need for high-quality, well-documented biospecimens for cancer research. The initiative builds on resources already developed by the NCI, including the Biospecimen Research Network and the NCI Best Practices for Biospecimen Resources, both of which were developed to address challenges around standardization of the collection and dissemination of quality biospecimens. caHUB will develop the infrastructure for collaborative biospecimen research and the production of evidence-based biospecimen standard operating procedures.

Proper citation: caHUB (RRID:SCR_009657) Copy   

•   Source: SciCrunch Registry

http://code.google.com/p/neurological-disease-ontology/

An ontology for the representation of the range of clinical and basic science aspects of neurological diseases. ND has a broad scope that includes neurological diseases as well as their associated signs, symptoms, diagnoses, pathologies, etiologies, processes, treatments, and any other aspect of a neurological disease that is or can be encountered in the course of clinical practice or medical research. ND is being built in accordance with the OBO Foundry principles. It is an extension of the Ontology for General Medical Science (OGMS) as well as the Basic Formal Ontology (BFO). ND aims to develop classes utilizing both textual and axiomatized definitions to describe and formalize relations between instances of classes both within the ontology itself as well as between ND and external ontologies such as the: Gene Ontology (GO), Cell Ontology (CL), Protein Ontology (PRO), Chemical Entities of Biological Interest (ChEBI), and Ontology for Biomedical Investigations (OBI).

Proper citation: Neurological disease ontology (RRID:SCR_010284) Copy   

•   Source: SciCrunch Registry

http://code.google.com/p/ogms/

An ontology based on the papers Toward an Ontological Treatment of Disease and Diagnosis and On Carcinomas and Other Pathological Entities to address some of the issues raised at the Workshop on Ontology of Diseases (Dallas, TX) and the Signs, Symptoms, and Findings Workshop (Milan, Italy). OGMS was formerly called the clinical phenotype ontology. Terms from OGMS hang from the Basic Formal Ontology.

Proper citation: Ontology for General Medical Science (RRID:SCR_010384) Copy   

•   Source: SciCrunch Registry

http://www.project-redcap.org/

Web application that allows users to build and manage online surveys and databases. Using REDCap's stream-lined process for rapidly developing projects, you may create and design projects using 1) the online method from your web browser using the Online Designer; and/or 2) the offline method by constructing a "data dictionary" template file in Microsoft Excel, which can be later uploaded into REDCap. Both surveys and databases (or a mixture of the two) can be built using these methods. REDCap provides audit trails for tracking data manipulation and user activity, as well as automated export procedures for seamless data downloads to Excel, PDF, and common statistical packages (SPSS, SAS, Stata, R). Also included are a built-in project calendar, a scheduling module, ad hoc reporting tools, and advanced features, such as branching logic, file uploading, and calculated fields. REDCap has a quick and easy software installation process, so that you can get REDCap running and fully functional in a matter of minutes. Several language translations have already been compiled for REDCap (e.g. Chinese, French, German, Portuguese), and it is anticipated that other languages will be available in full versions of REDCap soon. The REDCap Shared Library is a repository for REDCap data collection instruments and forms that can be downloaded and used by researchers at REDCap partner institutions.

Proper citation: REDCap (RRID:SCR_003445) Copy   

•   Source: SciCrunch Registry

http://www.cvm.ncsu.edu/ccmtr/

The mission of the CCMTR is to promote scientific discovery and facilitate its clinical application to achieve the goal of improving the health of animals and humans. The needs of the patients direct the emphasis of basic research, patient samples provide the critical resource to investigate the basis of disease, and patient participation in clinical studies is required to generate the evidence needed to apply new drugs, vaccines and technology to the broader patient population. Initiatives at the Center are designed to develop the multidisciplinary teams necessary to bring an idea from the lab to the patient. The Center is home to service cores that provide advanced technology, collect and store clinical patient samples, and perform clinical trials to validate new medical interventions. North Carolina State University''s College of Veterinary Medicine (CVM) is a dynamic community whose members are dedicated to preparing veterinarians and veterinarian scientists while advancing animal and human health from the cellular level through entire ecosystems.

Proper citation: Center for Comparative Medicine and Translational Research (RRID:SCR_008299) Copy   

•   Source: SciCrunch Registry

http://www.rad.upenn.edu/sbia/

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on June 2, 2023. A section of the Penn department of radiology, it is devoted to the development of computer-based image analysis methods and their application to clinical research studies. Image analysis methodologies include image registration, segmentation, population-based statistical analysis, biophysical modeling of anatomical deformations, and high-dimensional pattern classification. Clinical research studies spans a variety of clinical areas and organs, and they include brain diseases such as Alzheimer's disease and schizophrenia, evaluation of treatment effects in large clinical trials, diagnosis of cardiac diseases, and diagnosis prostate, breast and brain cancer. SBIA also performs small animal imaging research aiming to understand brain development in mouse models. It has multiple resources which can be accessed by researcher.

Proper citation: SBIA (RRID:SCR_013628) Copy   

•   Source: SciCrunch Registry

http://sourceforge.net/projects/cnv-webstore

Integrated platform to analyse, store, visualise and interpret CopyNumber Variation data. Analysis is supported for Illumina data, all CNV-reports and raw data can be imported after third-party analysis. Platform to streamline processing and downstream interpretation of microarray data in clinical context. Analysis tools include CNV analysis, parent of origin and uniparental disomy detection. Interpretation tools include data visualisation, gene prioritisation, automated PubMed searching, linking data to several genome browsers and annotation of CNVs based on several public databases.

Proper citation: CNV webstore (RRID:SCR_018007) Copy   

•   Source: SciCrunch Registry

https://med.inria.fr/

Software tool as multi platform medical image processing and visualization software. Functionalities include 2D/3D/4D image visualization, image registration, diffusion MR processing and tractography, filtering.

Proper citation: medInria (RRID:SCR_001462) Copy   

•   Source: SciCrunch Registry