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http://www.nitrc.org/projects/penncnv

A free software tool for Copy Number Variation (CNV) detection from SNP genotyping arrays. Currently it can handle signal intensity data from Illumina and Affymetrix arrays. With appropriate preparation of file format, it can also handle other types of SNP arrays and oligonucleotide arrays. PennCNV implements a hidden Markov model (HMM) that integrates multiple sources of information to infer CNV calls for individual genotyped samples. It differs form segmentation-based algorithm in that it considered SNP allelic ratio distribution as well as other factors, in addition to signal intensity alone. In addition, PennCNV can optionally utilize family information to generate family-based CNV calls by several different algorithms. Furthermore, PennCNV can generate CNV calls given a specific set of candidate CNV regions, through a validation-calling algorithm.

Proper citation: PennCNV (RRID:SCR_002518) Copy   

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http://www.matrics.ucla.edu/index.html

Cognitive deficits -- including impairments in areas such as memory, attention, and executive function -- are a major determinant and predictor of long-term disability in schizophrenia. Unfortunately, available antipsychotic medications are relatively ineffective in improving cognition. Scientific discoveries during the past decade suggest that there may be opportunities for developing medications that will be effective for improving cognition in schizophrenia. The NIMH has identified obstacles that are likely to interfere with the development of pharmacological agents for treating cognition in schizophrenia. These include: (1) a lack of a consensus as to how cognition in schizophrenia should be measured; (2) differing opinions as to the pharmacological approaches that are most promising; (3) challenges in clinical trial design; (4) concerns in the pharmaceutical industry regarding the US Food and Drug Administration''s (FDA) approaches to drug approval for this indication; and (5) issues in developing a research infrastructure that can carry out clinical trials of promising drugs. The MATRICS program will bring together representatives of academia, industry, and government in a consensus process for addressing all of these obstacles. Specific goals of the NIMH MATRICS are: * To catalyze regulatory acceptance of cognition in schizophrenia as a target for drug registration. * To promote development of novel compounds to enhance cognition in schizophrenia. * Leverage economic research power of industry to focus on important but neglected clinical targets. * Identify lead compounds and if deemed feasible, support human proof of concept trials for cognition in schizophrenia.

Proper citation: MATRICS - Measurement And Treatment Research to Improve Cognition in Schizophrenia (RRID:SCR_005644) Copy   

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http://www.patternlabforproteomics.org/

THIS RESOURCE IS NO LONGER IN SERVICE. Documented July 5, 2018. Gene Ontology Explorer (GOEx) combines data from protein fold changes with GO over-representation statistics to help draw conclusions in proteomic experiments. It is tightly integrated within the PatternLab for Proteomics project and, thus, lies within a complete computational environment that provides parsers and pattern recognition tools designed for spectral counting. GOEx offers three independent methods to query data: an interactive directed acyclic graph, a specialist mode where key words can be searched, and an automatic search. A recent hack included in GOEx is to load the sparse matrix index file directly into GOEx, instead of going through the report generation using the AC/T-fold methods. This makes it easy for GOEx to analyze any list of proteins as long as the list follows the index file format (described in manuscript) . Please note that if using this alternative strategy, there will be no protein fold information. Platform: Windows compatible

Proper citation: GOEx - Gene Ontology Explorer (RRID:SCR_005779) Copy   

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http://neurogenetics.nia.nih.gov

A suite of web-based open source software programs for clinical and genetic study. The aims of this software development in the Laboratory of Neurogenetics, NIA, NIH are * Build retrievable clinical data repository * Set up genetic data bank * Eliminate redundant data entries * Alleviate experimental error due to sample mix-up and genotyping error. * Facilitate clinical and genetic data integration. * Automate data analysis pipelines * Facilitate data mining for genetic as well as environmental factors associated with a disease * Provide an uniformed data acquisition framework, regardless the type of a given disease * Accommodate the heterogeneity of different studies * Manage data flow, storage and access * Ensure patient privacy and data confidentiality/security. The GERON suite consists of several self contained and yet extensible modules. Currently implemented modules are GERON Clinical, Genotyping, and Tracking. More modules are planned to be added into the suite, in order to keep up with the dynamics of the research field. Each module can be used separately or together with others into a seamless pipeline. With each module special attention has been given in order to remain free and open to the academic/government user.

Proper citation: GERON (RRID:SCR_008531) Copy   

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http://www.nitrc.org/projects/efficient_pt

A Matlab implementation for efficient permutation testing by using matrix completion.

Proper citation: Efficient Permutation Testing (RRID:SCR_014104) Copy   

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http://dx.doi.org/10.5281/zenodo.21157

A graphical source code file used for an automated motion detection and reward system for animal training (see comment for full paper title). It was designed on the LabVIEW programming system. Running the program requires the appropriate LabVIEW runtime software from National Instruments Corporation.

Proper citation: Monkey Motion (RRID:SCR_014285) Copy   

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https://www.nitrc.org/projects/gmac_2012/

Open-source software toolbox implemented multivariate spectral Granger Causality Analysis for studying brain connectivity using fMRI data. Available features are: fMRI data importing, network nodes definition, time series preprocessing, multivariate autoregressive modeling, spectral Granger causality indexes estimation, statistical significance assessment using surrogate data, network analysis and visualization of connectivity results. All functions are integrated into a graphical user interface developed in Matlab environment. Dependencies: Matlab, BIOSIG, SPM, MarsBar.

Proper citation: GMAC: A Matlab toolbox for spectral Granger causality analysis of fMRI data (RRID:SCR_009581) Copy   

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http://lgsun.grc.nia.nih.gov/cDNA/cDNA.html

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 23,2022. Project portal housing NIA Mouse EST Project, NIA Mouse cDNA Clone Sets, a NIA Mouse Gene Index, NIA Mouse cDNA Database, and NIA Mouse Microarrays. Characteristics of NIA 15K Mouse cDNA Clone Set * ~15,000 unique cDNA clones were rearrayed among 52,374 ESTs from pre- and periimplantation embryos, E12.5 female gonad/mesonephros, and newborn ovary. * Up to 50% are derived from novel genes. * ~1.5 kb average insert size. * Clones were sequenced from 5' and 3' termini to obtain longer reads and verify sequence. Sequence information is available at this Web Site. Clone names are from H3001A01 to H3159G07. * Handling of NIA 15k cDNA Clone Set(June3, 2000) Characteristics of NIA mouse 7.4K cDNA Clone Set * ~7407 cDNA clones with no redundancy within the set or with NIA Mouse 15K. * ~1.5 kb average insert size for short insert clones and ~2.5-3.0 kb average insert size for long-insert enriched clones.. * Clones were sequenced from 5' and 3' termini to obtain longer reads and verify sequence. Sequence information is available at this Web Site. Clone names are from H4001A01 to H4079G07. * Handling of NIA mouse 7.4k cDNA Clone Set (similar to handling of NIA mouse 15K, to be updated) Individual Clones are available from ATCC and MRC geneservice, UK. To obtain Clone, search the database using either the rearrayed clone name or GenBank accession number at the Key Word Search page. Follow the link to the sequence information page for the rearrayed clone to obtain source clone ATCC number. Clicking the ATCC number will bring up the ATCC ordering page for the source clone. There is essentially no overlap between the two clone sets (7.4K and 15K) said Minoru S.H. Ko, M.D., Ph.D., head of the Developmental Genomics and Aging Section in the NIA's Laboratory of Genetics. In addition, all cDNA clones in the NIA 7.4K set were purified by single colony isolation and sequence-verified, and more than half were prepared by a new procedure that yields long full-length cDNAs (average size 3-4 kb). The NIA Mouse 15k and 7.4k Clone Set Data and Published Microarray Data are available for download. NIA Mouse Microarrays *Microarray Data Download * 60-mer Oligo Array Platform ** (A) NIA 22k Oligo Microarray Gene List (21939 gene features) ( Carter et al 2003 ) ** (B) Agilent Mouse Development Oligo Microarray Gene List ** ( Subset of Microarray (A): 20,280 gene features ) * Data Analysis Tools

Proper citation: NIA Mouse cDNA Project Home Page (RRID:SCR_001472) Copy   

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http://www.agre.org/index.cfm

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on January 11, 2023. A private repository of clinical and genetic information on families with autism. Genetic and clinical data are obtained from families that have more than one family member diagnosed with an Autism Spectrum Disorder. The biological samples, along with the accompanying clinical data, are made available to AGRE-approved researchers worldwide. As they become available, additional family pedigrees will be posted in the online catalog. Cell lines have been established for the majority of families in this collection and serum/plasma is available on a subset of the subjects until stocks are depleted. The diagnosis of autism has been made using the standard Autism Diagnostic Interview-Revised (ADI-R) algorithm and the Autism Diagnostic Observation Scale (ADOS-G). Detailed birth and medical histories (including basic dysmorphology assessments) on children as well as family and medical information for parents and unaffected siblings, are available for nearly all families. DNA, cell lines, serum, plasma and clinical information are made available to AGRE-approved researchers for analysis.

Proper citation: Autism Genetic Resource Exchange (RRID:SCR_004403) Copy   

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http://ccr.coriell.org/Sections/Collections/NINDS/?SsId=10

Open resource of biological samples (DNA, cell lines, and other biospecimens) and corresponding phenotypic data to promote neurological research. Samples from more than 34,000 unique individuals with cerebrovascular disease, dystonia, epilepsy, Huntington's Disease, motor neuron disease, Parkinsonism, and Tourette Syndrome, as well as controls (population control and unaffected relatives) have been collected. The mission of the NINDS Repository is to provide 1) genetics support for scientists investigating pathogenesis in the central and peripheral nervous systems through submissions and distribution; 2) information support for patients, families, and advocates concerned with the living-side of neurological disease and stroke.

Proper citation: NINDS Repository (RRID:SCR_004520) Copy   

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http://cbl.uh.edu/ORION/research/software

ORION is our neuron reconstruction software package developed for the morphological reconstruction of neurons from confocal and multiphoton microscopy data. It accepts raw neuron stack data as input and it is capable of reconstructing the neuron structure, visualizing the output, and exporting the reconstruction in a variety of formats. We are developing tools that will enable Neuroscientists to explore single neuron function via sophisticated image analysis. Advanced optical imaging can produce both structural and functional data and is at the forefront of experimentally exploring the fast, small-scale dynamics of living neurons. Further, compartmental modeling of neuronal function enables rapid testing of hypotheses and estimating experimentally inaccessible parameters. Combining these two techniques will afford unprecedented capabilities in the study of single neuron function. Our software utility bridges the two Neuroscience techniques by rapidly, accurately, and robustly generating, from structural image data, a cylindrical morphology model suitable for simulating neuronal function.

Proper citation: ORION Software (RRID:SCR_004389) Copy   

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http://www.rand.org/labor/FLS/IFLS.html

A dataset of an on-going multi-level longitudinal survey in Indonesia that collects extensive information on socio-economic and demographic characteristics of respondents, as well as extremely comprehensive interviews with local leaders about community services and facilities. The survey is ideally suited for research on topics related to important dynamic aging processes such as the transition from self-sufficiency to dependency, the decline from robust health to frailty, labor force and earning dynamics, wealth accumulation and decumulation, living arrangements and intergenerational transfers. The first wave of IFLS was fielded in 1993 and collected information on over 30,000 individuals living in 7,200 households. The sample covers 321 communities in 13 provinces in Indonesia and is representative of about 83% of the population. These households were revisited in 1997 (IFLS2), 2000 (IFLS3), and 2007-8 (IFLS4). A 25% sub-sample of households was re-interviewed in 1998 (IFLS2+). Special attention is paid to the measurement of health, including the measurement of anthropometry, blood pressure, lung capacity, a mobility test and collection of dry blood spots by a nurse or doctor. In addition to comprehensive life history data on education, work, migration, marriage and child bearing, the survey collects very detailed information on economic status of individuals and households. Links with non co-resident family members are spelled out in conjunction with information on borrowing and transfers. Information is gathered on participation in community activities and in public assistance programs. Measurement of health is a major focus of the survey. In addition to detailed information about use of private and public health services along with insurance status, respondents provide a self-reported assessment of health status. Detailed information on the local economy and prices of goods and services are also collected. These data may be matched with the individual and household-level data. Considerable attention has been placed on minimizing attrition in IFLS. In each re-survey, about 95% of households have been re-contacted. Around 10-15% of respondents have moved from the location in which they were interviewed in the previous wave. In addition, individuals who split-off from the original households have been followed. They have added around 1,000 households to the sample in 1997 and about 3,000 households in 2000. Data Availability: IFLS1 data are available through ICPSR as study number 6706. Data from subsequent waves of the IFLS can be accessed from the RAND project Website. * Dates of Study: 1993-2008 * Study Features: Longitudinal, International, Anthropometric Measures, Biomarkers * Sample Size: ** 1993: 22,000 (IFLS1) ** 1997: 33,000 (IFLS2) ** 1998: 10,000 (IFLS2+) ** 2000: 37,000 (IFLS3) ** 2008: 44,103 (IFLS4) Links: * IFLS1 ICPSR: http://www.icpsr.umich.edu/icpsrweb/ICPSR/studies/06706 * IFLS ICPSR: http://www.icpsr.umich.edu/icpsrweb/ICPSR/studies/00184

Proper citation: Indonesia Family Life Survey (RRID:SCR_005695) Copy   

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http://lasurvey.rand.org/

A dataset of a panel study of a representative sample of all neighborhoods and households in Los Angeles County, with poor neighborhoods and families with children oversampled, for investigating the social and economic determinants of health and race and ethnic disparities. The study follows neighborhoods over time, as well as children and families. Two waves have been conducted to date, in 2000-2001 (L.A.FANS 1) and again beginning in 2006 through early 2009 (L.A. FANS 2). L.A.FANS-2 will significantly enhance the utility of the L.A.FANS data for studies of adult health disparities by: 1) Replicating self-reported health measures from L.A.FANS-1 and collecting new self-reports on treatment, health behaviors, functional limitations, quality and quantity of sleep, anxiety, health status vignettes, and changes in health status since the first interview; 2) Collecting physiological markers of disease and health status, including diabetes, hypertension, obesity, lung function, immune function, and cardiovascular disease; and 3) Expanding the data collected on adults'' work conditions, stressful experiences, and social ties. Wherever possible, L.A.FANS uses well-tested questions or sections from national surveys, such as the Health and Retirement Study (HRS), Panel Study of Income Dynamics (PSID), National Longitudinal Surveys (NLS), and National Health Interview Survey (NHIS), and other urban surveys, such as the Project on Human Development in Chicago Neighborhoods, to facilitate comparisons. Data Availability: Public use data, study design, and questionnaire content from L.A.FANS are available for downloading. Researchers can also apply for a restricted use version of the L.A.FANS-1 data that contain considerable contextual and geographically-referenced information. Application procedures are described at the project Website. L.A.FANS-2 fieldwork was completed at the end of 2008. The PIs anticipate L.A.FANS-2 public use data will be released in summer 2009. * Dates of Study: 2000-2008 * Study Features: Longitudinal, Minority Oversamples, Anthropometric Measures, Biospecimens * Sample Size: ** 2000-1: 2,548 (L.A.FANS 1) ** 2006-8: ~3,600 (L.A.FANS 2) Link: * ICPSR: http://www.icpsr.umich.edu/icpsrweb/ICPSR/studies/00172

Proper citation: Los Angeles Family and Neighborhood Survey (RRID:SCR_008923) Copy   

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http://www.alz.washington.edu/

A clinical research, neuropathological research and collaborative research database that uses data collected from 29 NIA-funded Alzheimer's Disease Centers (ADCs). The database consists of several datasets, and searches may be done on the entire database or on individual datasets. Any researcher, whether affiliated with an ADC or not, may request a data file for analysis or aggregate data tables. Requested aggregate data tables are produced and returned as soon as the queue allows (usually within 1-3 days depending on the complexity).

Proper citation: National Alzheimer's Coordinating Center (RRID:SCR_007327) Copy   

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http://www.nia.nih.gov/research/intramural-research-program/dynamics-health-aging-and-body-composition-health-abc

A study that characterizes the extent of change in body composition in older men and women, identifies clinical conditions accelerating these changes, and examines the health impact of these changes on strength, endurance, disability, and weight-related diseases of old age. The study population consists of 3,075 persons age 70-79 at baseline with about equal numbers of men and women. Thirty-three percent of the men are African-Americans as are 46% of the women. All persons in the study were selected to be free of disability in activities of daily living and free of functional limitation (defined as any difficulty walking a quarter of a mile or any difficulty walking up 10 steps without resting) at baseline. The core yearly examination for HEALTH ABC includes measurement of body composition by dual energy x-ray absorptio��������metry (DXA), walking ability, strength, an interview that includes self-report of limitations, a medication survey, and weight (Measurements in the Health ABC Study). Provision has been made for banking of blood specimens and extracted DNA (HealthABC repository). Study investigators are open to collaboration especially for measures focused on obesity and associated weight-related health conditions including osteoporosis, osteoarthritis, pulmonary function, cardiovascular disease, vascular disease, diabetes and glucose intolerance, and depression. The principal goals of the HEALTH ABC are: # To assess the association of baseline body weight, lean body mass, body fat, and bone mineral content, in relation to weight history, with: incident functional limitation; incidence and change in severity of weight-related health conditions; recovery of physical function after an acute event; baseline measures of strength, fitness and physical performance; gender, ethnicity and socioeconomic status # To access the contribution of episodes of severe acute illness in healthier older persons to changes in body weight, bone mineral content, lean body mass and body fat, and the relationship of these episodes to risk of functional limitation and recovery. # To assess the impact of weight-related co-morbid illness on the risk of functional limitation and recovery. # To assess the ways in which physiologic mediators of change in body composition influence and are influenced by changes in health in older adults and contribute to change in body composition; to understand how changes in body composition affect weight-related cardiovascular disease risk factors such as lipids, blood pressure and glucose tolerance. # To assess the interdependency of behavioral factors, such as nutrition and physical activity, co-morbid health conditions, and their association with change in body composition in old age. # To provide a firm scientific basis for understanding issues related to weight recommendations in old age through increased knowledge of the potential trade-offs between weight and risk of functional limitation, disability, morbidity and death; to provide information critical for developing effective strategies for the maintenance of health in older persons.

Proper citation: Dynamics of Health Aging and Body Composition (Health ABC) (RRID:SCR_008813) Copy   

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http://hrsonline.isr.umich.edu/

A data set of a longitudinal panel study of health, retirement, and aging that surveys a representative sample of more than 26,000 Americans over the age of 50 every two years. The HRS explores the changes in labor force participation and the health transitions that individuals undergo toward the end of their work lives and in the years that follow. The study captures a dynamic picture of an aging America''s physical and mental health, insurance coverage, financial status, family support systems, labor market status, and retirement planning. The sample in 2006 numbered over 22,000 persons in 13,100 households, with oversamples of Hispanics, Blacks and Florida residents. Beginning in 2006, half the sample received enhanced face-to-face follow-ups that included the collection of physical measures and biomarkers HRS provides a research data base that can simultaneously support continuous cross-sectional descriptions of the US population over the age of fifty-five, longitudinal studies of a given cohort over a substantial period of time (up to 18 years by 2010 for the original HRS cohort, following them from age 51-61 to age 69-79) and research on cross-cohort trends. By 2010 the HRS will be able to support cross-cohort comparisons of trajectories of health, labor supply, or wealth accumulation for persons who entered their 50s in 1992, 1998 and 2004. The HRS also has provided the sampling frame for targeted sub-studies. The Aging, Demographics, and Memory Study (ADAMS) supplement on dementia involved a field assessment of a sample of about 930 HRS panel members aged 75+ to clinically assess their dementia status and dementia severity. Special topics including consumption and time use, prescription drug use and the impact of Medicare Part D, parents'' human capital investments in children, and diabetes management by self-reported diabetics, have appeared on mail surveys that have used the HRS as a sampling frame. The HRS also can accommodate a number of experimental topics using Internet interviewing. The HRS is also characterized by links to a rich array of administrative data, including: Employer Pension Plans; National Death Index; Social Security Administration earnings and (projected) benefits data; W-2 self-employment data; and Medicare and Medicaid files. The HRS has actively collaborated with other longitudinal studies of aging in other countries (e.g., ELSA, SHARE, MHAS), providing both scientific and technical assistance. Data Availability: All publicly available data may be downloaded after registration. Early Release data files are typically available within three months of the end of each data collection, with the Final Release following at 24 months after the close of data collection activities. Files linked with administrative data are released only as restricted data through an application process, as outlined on the HRS website. * Dates of Study: 1992-present * Study Features: Longitudinal, Minority Oversamples, Anthropometric Measures, Biospecimens * Sample Size: 22,000+ Link * ICPSR: http://www.icpsr.umich.edu/icpsrweb/ICPSR/studies/06854

Proper citation: Health and Retirement Study (RRID:SCR_008930) Copy   

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http://ccr.coriell.org/nia/

A cell repository containing cells and DNA for studies of aging and the degenerative processes associated with it. Scientists use the highly-characterized, viable, and contaminant-free cell cultures from this collection for research on such diseases as Alzheimer's disease, progeria, Parkinson's disease, Werner syndrome, and Cockayne syndrome. The collections of the Repository include DNA and cell cultures from individuals with premature aging disorders, as well as DNA from individuals of advanced age from the the Baltimore Longitudinal Study of Aging at the Gerontology Research Center and other Longevity Collections. The Repository also includes samples from an Adolescent Study of Obesity, Apparently Healthy Controls, Animal Models of Aging, and both human and animal differentiated cell types. The cells in this resource have been collected over the past three decades using strict diagnostic criteria and banked under the highest quality standards of cell culture. Scientists can use the highly-characterized, viable, and contaminant-free cell cultures from this collection for genetic and cell biology research.

Proper citation: Aging Cell Repository (RRID:SCR_007320) Copy   

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http://dsarm.niapublications.org/

THIS RESOURCE IS NO LONGER IN SERVICE, documented on February 18, 2014.

A networking site for investigators using animal models to study aging, developed to provide a venue for sharing information about research models for aging studies. If you have tissue or data from animal models relevant to aging research that you are willing to share with other investigators, D-SARM allows you to identify the model and provides a secure, blinded email contact for investigators who would like to contact you about acquiring tissue or related resources. Investigators looking for resources from a particular model enter search terms describing the model of interest and then use the provided link to send emails to the contacts (names blinded) listed in the search results to initiate dialog about tissue or resources available for sharing. The database is housed on a secure server and admission to the network is moderated by the NIA Project Officer and limited to investigators at academic, government and non-profit research institutions. The goal is to provide a secure environment for sharing information about models used in aging research, promoting the sharing of resources, facilitating new research on aging in model systems, and increasing the return on the investment in research models.

Proper citation: Database for Sharing Aging Research Models (RRID:SCR_008691) Copy   

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https://adrc.mc.duke.edu/index.php/research/brain-bank

A research repository of human brains with neurological disorders and normal controls, recruited through the Autopsy and Brain Donation Program coordinator. The Kathleen Price Bryan Brain Bank contains brains from patients with Alzheimer's disease, Parkinson's disease, Amyotrophic Lateral Sclerosis, Huntington's disease, Muscular Dystrophy, and other neurological and dementing disorders. The brain tissue is subjected to a detailed neuropathological evaluation and then stored as fixed and frozen hemispheres, paraffin blocks and histological slides. After receipt of an IRB approved request, tissue is supplied to investigators at Duke University, major medical centers and pharmaceutical companies across the United States and worldwide.

Proper citation: Duke University Kathleen Price Bryan Brain Bank (RRID:SCR_005022) Copy   

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http://health.usf.edu/byrd/adrc/index.htm

A statewide consortium dedicated to Alzheimer's disease research to better understand the disease and related memory disorders. It includes Alzheimer's researchers and clinicians from institutions across Florida such as USF Health, Mayo Clinic Jacksonville, and Mount Sinai Medical Center. The purpose of the ADRC is to assist institutions in developing an infrastructure (cores) that can be used for various research projects with the goal of better understanding Alzheimer's disease and related disorders. The Florida ADRC is comprised of six cores, three projects and three pilot projects among other collaborations that utilize these cores.

Proper citation: Florida Alzheimer's Disease Research Center (RRID:SCR_004940) Copy   

•   Source: SciCrunch Registry