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http://rarediseases.info.nih.gov/GARD/Default.aspx

Genetic and Rare Diseases Information Center (GARD) is a collaborative effort of two agencies of the National Institutes of Health, The Office of Rare Diseases Research (ORDR) and the National Human Genome Research Institute (NHGRI) to help people find useful information about genetic conditions and rare diseases. GARD provides timely access to experienced information specialists who can furnish current and accurate information about genetic and rare diseases. So far, GARD has responded to 27,635 inquiries on about 7,147 rare and genetic diseases. Requests come not only from patients and their families, but also from physicians, nurses and other health-care professionals. GARD also has proved useful to genetic counselors, occupational and physical therapists, social workers, and teachers who work with people with a genetic or rare disease. Even scientists who are studying a genetic or rare disease and who need information for their research have contacted GARD, as have people who are taking part in a clinical study. Community leaders looking to help people find resources for those with genetic or rare diseases and advocacy groups who want up-to-date disease information for their members have contacted GARD. And members of the media who are writing stories about genetic or rare diseases have found the information GARD has on hand useful, accurate and complete. GARD has information on: :- What is known about a genetic or rare disease. :- What research studies are being conducted. :- What genetic testing and genetic services are available. :- Which advocacy groups to contact for a specific genetic or rare disease. :- What has been written recently about a genetic or rare disease in medical journals. GARD information specialists get their information from: :- NIH resources. :- Medical textbooks. :- Journal articles. :- Web sites. :- Advocacy groups, and their literature and services. :- Medical databases.

Proper citation: Genetic and Rare Diseases Information Center (RRID:SCR_008695) Copy   

•   Source: SciCrunch Registry

http://www.roslin.ed.ac.uk/alan-archibald/porcine-genome-sequencing-project/

Map of identifyied genes controlling traits of economic and welfare significance in the pig. The project objectives were to produce a genetic map with markers spaced at approximately 20 centiMorgan intervals over at least 90% of the pig genome; to produce a physical map with at least one distal and one proximal landmark locus mapped on each porcine chromosome arm and also genetically mapped; to develop a flow karyotype for the pig based on FACS sorted chromosomes; to develop PCR based techniques to enable rapid genotyping for polymorphic markers; to evaluate synteny conservation between pigs, man, mice and cattle; to develop and evaluate the statistical techniques required to analyze data from QTL mapping experiments and to plan and initiate the mapping of QTLs in the pig; to map loci affecting traits of economic and biological significance in the pig; and to develop the molecular tools to allow the future identification and cloning of mapped loci. Animal breeders currently assume that economically important traits such as growth, carcass composition and reproductive performance are controlled by an infinite number of genes each of infinitessimal effect. Although this model is known to be unrealistic, it has successfully underpinned the genetic improvement of livestock, including pigs, over recent decades. A map of the pig genome would allow the development of more realistic models of the genetic control of economic traits and the ultimately the identification of the major trait genes. This would allow the development of more efficient marker assisted selection which may be of particular value for traits such as disease resistance and meat quality.

Proper citation: Pig Genome Mapping (RRID:SCR_012884) Copy   

•   Source: SciCrunch Registry

https://genomecenter.ucdavis.edu/core-facilities/

Genome Center uses technologies to understand how heritable genetic information of diverse organisms functions in health and disease. Provides research facilities, service cores, and staff for genomics research and training. Core facilities for Bioinformatics,DNA Technologies and Expression Analysis, Metabolomics, Proteomics,TILLING Core,Yeast One Hybrid Services Core.

Proper citation: UC Davis Genome Center Labs and Facilities (RRID:SCR_012480) Copy   

•   Source: SciCrunch Registry

http://healthybrain.umn.edu/

Research forum portal to address brain status by acquiring comprehensive, multimodal data from healthy humans across the lifespan to characterize brain status, assess its change over time, and associate composite descriptors of brain status. Specifically, the measurements are acquired noninvasively by existing neuroimaging technologies (structural MRI, functional MRI, magnetic resonance spectroscopy, diffusion MRI, and magnetoencephalography); in addition, genetic, cognitive, language, and lifestyle data are acquired. Goals: * Derive the Brain Health Index- An integrative assessment of brain status derived from multimodal measurements of brain structure, function, and chemistry. * Continue acquiring data to construct the first-ever databank on brain, cognitive, language and genetic measurements for healthy people across the lifespan. * Provide a novel and unique dataset by which to: characterize brain status, assess its change over time, and associate it with genetic makeup, cognitive function, and language abilities. * Forecast future brain health and disease based on current measurements and guide physicians towards new interventions and evaluate interventions as they develop. * Extend to siblings and other family members to further assess the genetic influences and inheritability.

Proper citation: HBP: Healthy Brain Project (RRID:SCR_013137) Copy   

•   Source: SciCrunch Registry

https://atgu.mgh.harvard.edu/plinkseq/

An open-source C/C++ library for working with human genetic variation data. The specific focus is to provide a platform for analytic tool development for variation data from large-scale resequencing projects, particularly whole-exome and whole-genome studies. However, the library could in principle be applied to other types of genetic studies, including whole-genome association studies of common SNPs. (entry from Genetic Analysis Software)

Proper citation: PLINK/SEQ (RRID:SCR_013193) Copy   

•   Source: SciCrunch Registry

http://cerebrovascularportal.org

Portal enables browsing, searching, and analysis of human genetic information linked to cerebrovascular disease and related traits, while protecting the integrity and confidentiality of the underlying data.

Proper citation: Cerebrovascular Disease Knowledge Portal (RRID:SCR_015628) Copy   

•   Source: SciCrunch Registry

http://www.aevol.fr/

Simulation software for experimental evolution of microorganisms. Aevol is a digital genetics model for the study of structural variations of the genome (e.g. number of genes, synteny, proportion of coding sequences).

Proper citation: Aevol (RRID:SCR_015966) Copy   

•   Source: SciCrunch Registry

http://knhi.de/en/network/

Association of physicians, scientists, academics, research institutes and self-help groups that provides and nurtures interdisciplinary cooperation between research and primary, secondary and tertiary health care. Many internationally renowned heart failure researchers and working groups live and work in Germany. Nevertheless, there is insufficient cooperation of the respective working groups and research projects in this area. In order to remain internationally competitive in the heart failure research community, excellent implementation of large scale clinical and genetic trials is indispensable. Further, deficits in the effective presentation and transfer of research findings into clinical practice need to be addressed. An adequate translation of guidelines into practical, tangible instructions can facilitate clinical practice both in primary and tertiary care fundamentally. The need for action to address the research-practice-gap is obvious.

Proper citation: Competence Network Heart Failure (RRID:SCR_004979) Copy   

•   Source: SciCrunch Registry

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

Central location for voluntary submission of genetic test information by providers including the test''s purpose, methodology, validity, evidence of the test''s usefulness, and laboratory contacts and credentials. GTR aims to advance the public health and research into the genetic basis of health and disease. GTR is accepting registration of clinical tests for Mendelian disorders, complex tests and arrays, and pharmacogenetic tests. These tests may include multiple methods and may include multiple major method categories such as biochemical, cytogenetic, and molecular tests. GTR is not currently accepting registration of tests for somatic disorders, research tests or direct-to-consumer tests.

Proper citation: Genetic Testing Registry (RRID:SCR_005565) Copy   

•   Source: SciCrunch Registry

https://code.google.com/p/ontology-for-genetic-interval/

An ontology that formalized the genomic element by defining an upper class genetic interval using BFO as its framework. The definition of genetic interval is the spatial continuous physical entity which contains ordered genomic sets (DNA, RNA, Allele, Marker,etc.) between and including two points (Nucleic_Acid_Base_Residue) on a chromosome or RNA molecule which must have a liner primary sequence structure.

Proper citation: Ontology for Genetic Interval (RRID:SCR_003423) Copy   

•   Source: SciCrunch Registry

https://www.facebase.org/node/252

THIS RESOURCE IS NO LONGER IN SERVICE,documented on January,18, 2022. FaceBase Biorepository is now collecting biological samples from people with cleft lip/palate and their family members. Information for Prospective Cases: Clefts of the lip and/or palate can be caused by a wide range of genetic, environmental and other factors. The FaceBase Biorepository will serve as a common source of both biological samples and information that can be made available to investigators trying to determine the underlying cause of these common birth defects. Genetic studies, in particular, will benefit from both family history information and having samples from affected individuals as well as their family members. DNA is the information containing molecules found in all the cells of our body and can be easily obtained from material such as blood or saliva samples. As part of the FaceBase Biorepository, we are requesting families to submit biological samples from specific family members as well as information from other family members that might be affected with either the same condition or a similar condition. The medical and family history information that is collected includes other relevant information such as exposure to possible environmental causes during pregnancy. The biorepository is managed by Nichole Nidey, a research study coordinator, and Jeff Murray, a pediatric clinical geneticist and researcher. They are available to speak with family members regarding questions they may have, including providing information about the biorepository and making arrangements for the collection of samples for those who wish to participate. All participation is voluntary. Your name or other personally identifiable information (name, address, etc) will be removed before information is placed in the biorepository. Summary data to show how the database itself has been used overall as well as updates on whether specific findings might have been made using this database will be available on the FaceBase website at www.facebase.org. A newsletter containing this information will also be given to families and referring clinicians so that they may discuss the specifics with the families if there appears to be information that might be relevant in a particular case. Families will also need to sign a consent form that has been approved by the Institutional Review Board at the University of Iowa. Also, any submitted samples or data can also be removed from the database at any time should the family no longer wish to participate. Investigators interested in requesting DNA samples or for more information, please contact cleftresearch (at) uiowa.edu, Nichole Nidey, nichole-nidey (at) uiowa.edu or (319) 353-4365, or Jeff Murray, jeff-murray (at) uiowa.edu.

Proper citation: FaceBase Biorepository (RRID:SCR_006001) Copy   

•   Source: SciCrunch Registry

http://cbl-gorilla.cs.technion.ac.il/

A tool for identifying and visualizing enriched GO terms in ranked lists of genes. It can be run in one of two modes: * Searching for enriched GO terms that appear densely at the top of a ranked list of genes or * Searching for enriched GO terms in a target list of genes compared to a background list of genes.

Proper citation: GOrilla: Gene Ontology Enrichment Analysis and Visualization Tool (RRID:SCR_006848) Copy   

•   Source: SciCrunch Registry

http://www.homepages.ed.ac.uk/pmckeigu/pooling/poolscore.htm

Software program for analysis of case-control genetic association studies using allele frequency measurements on DNA pools (entry from Genetic Analysis Software)

Proper citation: POOLSCORE (RRID:SCR_007514) Copy   

•   Source: SciCrunch Registry

http://chgr.mc.vanderbilt.edu/page/gist

Software package to test if a marker can account in part for the linkage signal in its region. There are two versions of the software: Windows and Linux/Unix.

Proper citation: Genotype-IBD Sharing Test (RRID:SCR_006257) Copy   

•   Source: SciCrunch Registry

http://www003.upp.so-net.ne.jp/pub/publications.html#sl

Software application for inkage disequilibrium grouping of single nucleotide polymorphisms (SNPs) reflecting haplotype phylogeny for efficient selection of tag SNPs. (entry from Genetic Analysis Software)

Proper citation: LDGROUP (RRID:SCR_006282) Copy   

•   Source: SciCrunch Registry

http://www.dkfz.de/en/epidemiologie-krebserkrankungen/software/software.html

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on May 24,2023. Software program that performs estimation of power and sample sizes required to detect genetic and environmental main, as well as gene-environment interaction (GxE) effects in indirect matched case-control studies (1:1 matching). When the hypothesis of GxE is tested, power/sample size will be estimated for the detection of GxE, as well as for the detection of genetic and environmental marginal effects. Furthermore, power estimation is implemented for the joint test of genetic marginal and GxE effects (Kraft P et al., 2007). Power and sample size estimations are based on Gauderman''s (2002) asymptotic approach for power and sample size estimations in direct studies of GxE. Hardy-Weinberg equilibrium and independence of genotypes and environmental exposures in the population are assumed. The estimates are based on genotypic codes (G=1 (G=0) for individuals who carry a (non-) risk genotype), which depend on the mode of inheritance (dominant, recessive, or multiplicative). A conditional logistic regression approach is used, which employs a likelihood-ratio test with respect to a biallelic candidate SNP, a binary environmental factor (E=1 (E=0) in (un)exposed individuals), and the interaction between these components. (entry from Genetic Analysis Software)

Proper citation: PIAGE (RRID:SCR_013124) Copy   

•   Source: SciCrunch Registry

http://bioinformatics.ust.hk/BOOST.html

Software application (entry from Genetic Analysis Software) for a method for detecting gene-gene interactions. It allows examining all pairwise interactions in genome-wide case-control studies.

Proper citation: BOOST (RRID:SCR_013133) Copy   

•   Source: SciCrunch Registry

http://folk.uio.no/thoree/FEST/

An R package for simulations and likelihood calculations of pair-wise family relationships using DNA marker data. (entry from Genetic Analysis Software)

Proper citation: R/FEST (RRID:SCR_013347) Copy   

•   Source: SciCrunch Registry

http://www.i2b2.org

i2b2 (Informatics for Integrating Biology and the Bedside) is an NIH-funded National Center for Biomedical Computing based at Partners HealthCare System. The i2b2 Center is developing a scalable informatics framework that will enable clinical researchers to use existing clinical data for discovery research and, when combined with IRB-approved genomic data, facilitate the design of targeted therapies for individual patients with diseases having genetic origin. For some resources (e.g. software) the use of the resource requires accepting a specific (e.g. OpenSource) license.

Proper citation: Informatics for Integrating Biology and the Bedside (RRID:SCR_013629) Copy   

•   Source: SciCrunch Registry

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

A repository of schizophrenia neuroimaging data collected from over 450 individuals with schizophrenia, healthy controls and their respective siblings, most with 2-year longitudinal follow-up. The data include neuroimaging data, cognitive data, clinical data, and genetic data.

Proper citation: Northwestern University Schizophrenia Data and Software Tool (NUSDAST) (RRID:SCR_014153) Copy   

•   Source: SciCrunch Registry