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http://www-gene.cimr.cam.ac.uk/clayton/software/

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on May 12,2023. Software application that tests for association between genetic marker and disease by examining the transmission of markers from parents to affected offspring. The main features which differ from other similar programs are: (1) It can deal with transmission of multi-locus haplotypes, even if phase is unknown, and (2) Parental genotypes may be unknown. (entry from Genetic Analysis Software)

Proper citation: TRANSMIT (RRID:SCR_007571) Copy   

•   Source: SciCrunch Registry

http://www.hays.co.uk/index.htm

UK and Australia scientific job board.

Proper citation: Hays (RRID:SCR_008781) 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   

•   Source: SciCrunch Registry

http://aspex.sourceforge.net/

A set of programs for performing multipoint exclusion mapping of affected sibling pair data for discrete traits. (entry from Genetic Analysis Software)

Proper citation: ASPEX (RRID:SCR_008414) Copy   

•   Source: SciCrunch Registry

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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https://github.com/gaow/genetic-analysis-software/blob/master/pages/MAPMAKER%26SIBS.md

THIS RESOURCE IS NO LONGER IN SERVCE, documented September 22, 2016. Data analysis software for complete multipoint analysis.

Proper citation: MAPMAKER/SIBS (RRID:SCR_008012) 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., THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 16,2025.

Proper citation: GERON (RRID:SCR_008531) Copy   

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http://www.pierroton.inra.fr/genetics/labo/Software/Famoz/index.html

Software application that uses likelihood calculation and simulation to perform parentage studies with codominant, dominant, cytoplasmic markers or combinations of the different types (entry from Genetic Analysis Software)

Proper citation: FAMOZ (RRID:SCR_007477) Copy   

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http://www.mds.qmw.ac.uk/statgen/dcurtis/software.html

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on May 5th,2023. Software application that uses Monte Carlo method for assessing significance of a case-control association study with multi-allelic marker. (entry from Genetic Analysis Software).

Proper citation: CLUMP (RRID:SCR_007476) Copy   

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http://www.cristudy.org/Chronic-Kidney-Disease/Chronic-Renal-Insufficiency-Cohort-Study/

A prospective observational national cohort study poised to make fundamental insights into the epidemiology, management, and outcomes of chronic kidney disease (CKD) in adults with intended long-term follow up. The major goals of the CRIC Study are to answer two important questions: * Why does kidney disease get worse in some people, but not in others? * Why do persons with kidney disease commonly experience heart disease and stroke? The CRIC Scientific and Data Coordinating Center at Penn receives data and provides ongoing support for a number of Ancillary Studies approved by the CRIC Cohort utilizing both data collected about CRIC study participants as well as their biological samples. The CRIC Study has enrolled over 3900 men and women with CKD from 13 recruitment sites throughout the country. Following this group of individuals over the past 10 years has contributed to the knowledge of kidney disease, its treatment, and preventing its complications. The NIDDKwill be extending the study for an additional 5 years, through 2018. An extensive set of study data is collected from CRIC Study participants. With varying frequency, data are collected in the domains of medical history, physical measures, psychometrics and behaviors, biomarkers, genomics/metabolomics, as well as renal, cardiovascular and other outcomes. Measurements include creatinine clearance and iothalamate measured glomerular filtration rate. Cardiovascular measures include blood pressure, ECG, ABI, ECHO, and EBCT. Clinical CV outcomes include MI, ischemic heart disease-related death, acute coronary syndromes, congestive heart failure, cerebrovascular disease, peripheral vascular disease, and composite outcomes. The CRIC Study has delivered in excess of 150,000 bio-samples and a dataset characterizing all 3939 CRIC participants at the time of study entry to the NIDDKnational repository. The CRIC Study will also be delivering a dataset to NCBI''''s Database for Genotypes and Phenotypes.

Proper citation: Chronic Renal Insufficiency Cohort Study (RRID:SCR_009016) Copy   

•   Source: SciCrunch Registry

http://www.fetk.org/

The Finite Element ToolKit (FETK) is a collaboratively developed, evolving collection of adaptive finite element method (AFEM) software libraries and tools for solving coupled systems of nonlinear geometric partial differential equations (PDE). The FETK libraries and tools are written in an object-oriented form of ANSI-C and in C , and include a common portability layer (MALOC) for all of FETK, a collection of standard numerical libraries (PUNC), a stand-alone high-quality surface and volume simplex mesh generator (GAMer), a stand-alone networked polygon display tool (SG), a general nonlinear finite element modeling kernel (MC), and a MATLAB toolkit (MCLite) for protyping finite element methods and examining simplex meshes using MATLAB. The entire FETK Suite of tools is highly portable (from iPhone to Blue Gene/L), thanks to use of a small abstraction layer (MALOC) and heavy use of the GNU Autoconf infrastructure. The primary FETK ANSI-C software libraries are: :- MALOC is a Minimal Abstraction Layer for Object-oriented C/C programs. :- PUNC is Portable Understructure for Numerical Computing (requires MALOC). :- GAMer is a Geometry-preserving Adaptive MeshER (requires MALOC). :- SG is a Socket Graphics tool for displaying polygons (requires MALOC). :- MC is a 2D/3D AFEM code for nonlinear geometric PDE (requires MALOC; optionally uses PUNC GAMER SG). Application-specific software designed for use with the FETK software libraries is: :- GPDE is a Geometric Partial Differential Equation solver (requires MALOC PUNC MC; optionally uses GAMER SG). :- APBS is an Adaptive Poisson-Boltzmann Equation Solver (requires MALOC PUNC MC; optionally uses GAMER SG). :- SMOL is a Smoluchowki Equation Solver solver (requires MALOC PUNC MC; optionally uses GAMER SG). MATLAB toolkits designed for use with MC and SG or as standalone packages: :- MCLite is a simple 2D MATLAB version of MC designed for teaching. :- FEtkLAB is a sophisticated 2D MATLAB adaptive PDE solver built on top of MCLite. Related packages developed and maintained by FETK developers (included in PUNC above): :- PMG is a Parallel Algebraic MultiGrid code for general semilinear elliptic equatons. :- CgCode is a package of Conjugate gradient Codes for large sparse linear systems. Sponsors: This resource is developed and supported by the MCP Research Group at the UCSD Center for Computational Mathematics.

Proper citation: Finite Element Toolkit (RRID:SCR_008682) Copy   

•   Source: SciCrunch Registry

http://liweilab.genetics.ac.cn/tm/

Web-based tool used to mine human protein-protein interactions (PPIs) from PubMed abstracts based on their co-occurrences and interaction words, followed by evidencs in human PPI databases and shared terms in GO database.

Proper citation: Human Protein-Protein Interaction Mining Tool (RRID:SCR_008040) Copy   

•   Source: SciCrunch Registry

http://www.centreducancer.be/en/show/index/section/8/page/34

When a patient suffering or thought to be suffering from cancer is cared for, samples are often taken to determine the precise diagnosis and to determine any treatment necessary. After this essential stage of the patient''s care, unused biological material is sometimes left over. This material is an essential and precious tool for research into cancer. For this reason, patients can decide to make the material available to researchers the world over who study either the development mechanism of cancer or the new treatments available. Residual samples are centralized and stored in the Tumor Bank at the Cliniques Universitaires Saint-Luc Cancer Centre. The research carried out on this material primarily benefits cancer patients. It can help improve existing treatments or discover new drugs, and also allows new diagnostic tools to be tested. Any financial profits obtained from assessing the results obtained are entirely reinvested in the work of the Cancer Centre''s Tumour Bank and in new research projects at the Catholic University of Louvain. Using and sharing material, and verification and retrospective analysis of clinical data, all comply with strict rules. As with donations of blood, marrow or organs, an Ethics Committee oversees the operations of the Tumour Bank and research projects. This committee is responsible for ensuring compliance with current Belgian and legal texts, especially those concerning the protection of patient privacy and rights.

Proper citation: Saint-Luc Tumour Bank (RRID:SCR_008714) Copy   

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http://research.nhgri.nih.gov/software/TRAP/

Software tool for determining a regression model of quantitative or binary trait variation when the number of possible genetic predictors is very large, considering only a moderate number of predictors at one time, using unrelated or family data. (entry from Genetic Analysis Software)

Proper citation: TRAP (RRID:SCR_009002) Copy   

•   Source: SciCrunch Registry

https://github.com/gaow/genetic-analysis-software/blob/master/pages/2SNP.md

THIS RESOURCE IS NO LONGER IN SERVCE, documented September 22, 2016. An algorithm resource for scalable phasing method for trios and unrelated individuals.

Proper citation: 2SNP (RRID:SCR_009038) Copy   

•   Source: SciCrunch Registry

http://csg.sph.umich.edu/boehnke/sibmed.php

Software application that identifies likely genotyping errors and mutations for a sib pair in the context of multipoint mapping. (entry from Genetic Analysis Software)

Proper citation: SIBMED (RRID:SCR_007495) Copy   

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http://www.stat.washington.edu/thompson/Genepi/Albert/albert.shtml

Software application that estimates genotype relative risks, genotyping error rates and population risk allele frequencies from marker genotype data in case-parent trios. ALBERT uses the distribution of trio marker genotypes to compute maximum likelihood estimates for the parameters. (entry from Genetic Analysis Software)

Proper citation: ALBERT (RRID:SCR_009037) Copy   

•   Source: SciCrunch Registry

http://www-gene.cimr.cam.ac.uk/clayton/software/

Software program for estimating frequencies of haplotypes of large numbers of diallelic markers from unphased genotype data from unrelated subjects (entry from Genetic Analysis Software)

Proper citation: SNPHAP (RRID:SCR_008456) Copy   

•   Source: SciCrunch Registry

http://www.phenote.org/

Phenote is both a complete piece of software and a software toolkit designed to facilitate the annotation of biological phenotypes using ontologies. It provides an interface and infrastructure to record genotype-phenotype pairs, together with the provenance for the annotation. Typical users of Phenote include literature curators, laboratory researchers, and clinicians looking for a method to record data in a user-friendly and computable way. Features of Phenote include the use of any OBO-format ontology, ontology navigation and term information display, bulk sort, copy, edit, and delete of phenotype-genotype character entries, and a variety of export formats. Phenote is a project of the Berkeley Bioinformatics Open-Source Projects (BBOP).

Proper citation: Phenote: A Phenotype Annotation Tool using Ontologies (RRID:SCR_008334) Copy   

•   Source: SciCrunch Registry

http://cmpg.unibe.ch/software/simcoal/

Software application (entry from Genetic Analysis Software)

Proper citation: SIMCOAL (RRID:SCR_008450) Copy   

•   Source: SciCrunch Registry