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https://www.nugene.org/

Collects and stores genetic (DNA) samples along with associated healthcare information from patients of Northwestern-affiliated hospitals and clinics. This resource is available to scientists to conduct groundbreaking genetic research. The information and blood samples provided will be used by researchers to examine the role genes play in the development and treatment of common diseases. The NUgene Project seeks to increase the understanding of genetic mechanisms underlying common diseases, assist in the development of DNA-based technology for diagnosis and treatment of disease, and aid physicians and other healthcare providers in the application of genetics to the practice of medicine. NUgene participants are recruited throughout the Northwestern-affiliated healthcare community in order to create an ethnically and medically diverse population for research. Participants must be 18 years of age or older and receive their medical care from a Northwestern-affiliated provider, regardless of health status. Consenting individuals complete all aspects of enrollment in a single meeting with a research coordinator. The enrollment process includes the donation of a single sample of blood and the completion of a self-administered questionnaire. Participants also sign a consent form during this encounter. The NUgene Project is an interdisciplinary project that relies on the expertise of individuals working in a variety of fields, including science, medicine, clinical research, statistics, epidemiology, and computational biology. NUgene''s multidisciplinary approach has spurred collaborations within Northwestern-affiliated institutions and with other outside institutions. This collaboration of ideas is the future of genetics and genomic research., THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 16,2025.

Proper citation: NUgene Project (RRID:SCR_007426) Copy   

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http://www.stats.ox.ac.uk/~mcvean/LDhat/

Software package for the analysis of recombination rates from population genetic data (entry from Genetic Analysis Software)

Proper citation: LDHAT (RRID:SCR_006298) Copy   

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http://tvap.genome.wustl.edu/tools/varscan/

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on March 7,2024. Platform-independent, technology-independent software tool for identifying SNPs and indels in massively parallel sequencing of individual and pooled samples. Given data for a single sample, VarScan identifies and filters germline variants based on read counts, base quality, and allele frequency. Given data for a tumor-normal pair, VarScan also determines the somatic status of each variant (Germline, Somatic, or LOH) by comparing read counts between samples. (entry from Genetic Analysis Software), THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 16,2025.

Proper citation: VARSCAN (RRID:SCR_006849) Copy   

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http://ki.se/en/meb/twingene-and-genomeeutwin

In collaboration with GenomeEUtwin, the TwinGene project investigates the importance of quantitative trait loci and environmental factors for cardiovascular disease. It is well known that genetic factors are of considerable importance for some familial lipid syndromes and that Type A Behavior pattern and increased lipid levels infer increased risk for cardiovascular disease. It is furthermore known that genetic factors are of importance levels of blood lipid biomarkers. The interplay of genetic and environmental effects for these risk factors in a normal population is less well understood and virtually unknown for the elderly. In the TwinGene project twins born before 1958 are contacted to participate. Health and medication data are collected from self-reported questionnaires, and blood sampling material is mailed to the subject who then contacts a local health care center for blood sampling and a health check-up. In the simple health check-up, height, weight, circumference of waist and hip, and blood pressure are measured. Blood is sampled for DNA extraction, serum collection and clinical chemistry tests of C-reactive protein, total cholesterol, triglycerides, HDL and LDL cholesterol, apolipo��protein A1 and B, glucose and HbA1C. The TwinGene cohort contains more than 10000 of the expected final number of 16000 individuals. Molecular genetic techniques are being used to identify Quantitative Trait Loci (QTLs) for cardiovascular disease and biomarkers in the TwinGene participants. Genome-wide linkage and association studies are ongoing. DZ twins have been genome-scanned with 1000 STS markers and a subset of 300 MZ twins have been genome-scanned with Illumina 317K SNP platform. Association of positional candidate SNPs arising from these genomscans are planned. The TwinGene project is associated with the large European collaboration denoted GenomEUtwin (www.genomeutwin.org, see below) which since 2002 has aimed at gathering genetic data on twins in Europe and setting up the infrastructure needed to enable pooling of data and joint analyses. It has been the funding source for obtaining the genome scan data. Types of samples: * EDTA whole blood * DNA * Serum Number of sample donors: 12 044 (sample collection completed)

Proper citation: KI Biobank - TwinGene (RRID:SCR_006006) Copy   

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The record is no longer available at this source.

Software application that is part of the LINKAGE auxiliary programs (entry from Genetic Analysis Software)

Proper citation: LSP (RRID:SCR_007059) Copy   

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http://bios.ugr.es/~mabad/rTDT/index.html

Software application (entry from Genetic Analysis Software)

Proper citation: RTDT (RRID:SCR_007336) Copy   

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http://www.hpcf.upr.edu/~humberto/software/TkMap/

Software program for drawing genetic maps (entry from Genetic Analysis Software)

Proper citation: TKMAP (RRID:SCR_007457) Copy   

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http://www.people.fas.harvard.edu/~junliu/BEAM/

Software application that treats the disease-associated markers and their interactions via a bayesian partitioning model and computes, via Markov chain Monte Carlo, the posterior probability that each marker set is associated with the disease. (entry from Genetic Analysis Software)

Proper citation: BEAM (RRID:SCR_007258) Copy   

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http://droog.gs.washington.edu/ldSelect.html

Software program that analyzes patterns of linkage disequilibrium (LD) between polymorphic sites in a locus, and bins the SNPs on the basis of a threshold level of LD as measured by r2. (entry from Genetic Analysis Software), THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 16,2025.

Proper citation: LDSELECT (RRID:SCR_007010) Copy   

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http://ki.se/ki/jsp/polopoly.jsp?d=29332&a=23686&l=en

THIS RESOURCE IS NO LONGER IN SERVICE, documented August 22, 2016. The original aim of this study was to increase our understanding of the etiology of malignant lymphomas, especially in view of the increasing trend in incidence. Malignant lymphoma (including non-Hodgkin lymphoma, NHL, Hodgkin lymphoma, HL, and chronic lymphocytic leukemia, CLL) constitute a heterogeneous group of malignancies with regard to histology, molecular characteristics and clinical course. Etiological factors may also vary by lymphoma subtype. The incidence of NHL, the most common lymphoma group, has increased dramatically during the past decades in Sweden and in many other Western countries. The reasons for this increase as well as for the majority of all new cases is not well understood. Well established risk factors for lymphoma overall include hereditary and acquired disorders of strong immune dysfunction such as HIV/AIDS and organ transplantation, but they explain few new cases in the population. Approach: Population-based case-control study in Sweden and Denmark. The study includes in total 3740 patients and 3187 controls in both countries recruited during the period October 1999 to October 2002. Through a rapid case ascertainment system, the cases were identified shortly after diagnosis. The controls were randomly selected from national population registers and frequency-matched to the expected number of cases by sex and age group. Both cases and controls were interviewed by telephone based on a standardized questionnaire to obtain detailed information on potential risk factors for lymphoma such as medical history including infectious diseases, drug use and blood transfusions, socio-economic factors and life-style. Blood samples were also collected and stored as serum, plasma, DNA and live lymphocytes. In addition, written questionnaires about dietary habits or work exposures were sent out in Sweden. Tumor material from the cases was re-examined and uniformly classified according to the REAL classification. Status The data collection ended in 2002 and data analysis has been ongoing since then. We have primarily analyzed a range of environmental factors in relation risk of malignant lymphoma subgroups including sun exposure, body mass index, family history of hematopoietic cancer, allergy, autoimmune disorders and mononucleosis. We have also assessed specific genetic determinants in a subgroups of patients with follicular lymphoma and controls. Study results have so far been presented in 14 publications in peer-reviewed journals. In addition to new analyses on other environmental factors, we now also work to understand genetic susceptibility and gene-environmental interaction and risk of lymphoma. Also, prognostic studies have been initiated in collaboration with other research groups with regard to in CLL, HL and T-cell lymphoma.

Proper citation: SCALE - Scandinavian lymphoma etiology (RRID:SCR_006041) Copy   

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

THIS RESOURCE IS NO LONGER IN SERVCE, documented September 22, 2016. A web application and database schema for storing and interactively displaying genetic map data.

Proper citation: INTEGRAYEDMAP (RRID:SCR_007489) Copy   

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http://cedar.genetics.soton.ac.uk/pub/PROGRAMS/LDMAP

Software program for constructing linkage disequilibrium (LD) maps. (entry from Genetic Analysis Software)

Proper citation: LDMAP (RRID:SCR_006308) Copy   

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https://github.com/wtsi-npg/Illuminus

A fast and accurate algorithm for assigning single nucleotide polymorphism (SNP) genotypes to microarray data from the Illumina BeadArray technology.

Proper citation: ILLUMINUS (RRID:SCR_000388) Copy   

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http://www.broadinstitute.org/mpg/snap/

A computer program and web-based service for the rapid retrieval of linkage disequilibrium proxy single nucleotide polymorphism (SNP) results given input of one or more query SNPs and based on empirical observations from the International HapMap Project and the 1000 Genomes Project. A series of filters allow users to optionally retrieve results that are limited to specific combinations of genotyping platforms, above specified pairwise r2 thresholds, or up to a maximum distance between query and proxy SNPs. SNAP can also generate linkage disequilibrium plots

Proper citation: SNAP - SNP Annotation and Proxy Search (RRID:SCR_002127) Copy   

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http://www.yandell-lab.org/software/vaast.html

A probabilistic search tool for identifying damaged genes and their disease-causing variants in personal genome sequences. VAAST combines elements of phylogenetic conservation, amino acid substitution, and aggregative approaches to variant prioritization into a single unified likelihood-framework that allows users to accurately identify damaged genes and deleterious variants. The software can score both coding (SNV, indel and splice site) and non-coding variants (SNV), evaluating the cumulative impact of both types of variants simultaneously. It can identify rare variants causing rare genetic diseases and can also use both rare and common variants to identify genes responsible for common diseases.

Proper citation: VAAST (RRID:SCR_002179) Copy   

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http://rgp.dna.affrc.go.jp/E/index.html

Rice Genome Research Program (RGP) is an integral part of the Japanese Ministry of Agriculture, Forestry and Fisheries (MAFF) Genome Research Project. RGP now aims to completely sequence the entire rice genome and subsequently to pursue integrated goals in functional genomics, genome informatics and applied genomics. It is jointly coordinated by the National Institute of Agrobiological Sciences (NIAS), a government research institute under MAFF and the Society for Techno-innovation of Agriculture, Forestry and Fisheries (STAFF), a semi-private research organization managed and supported by MAFF and a consortium of some twenty Japanese companies. The research is funded with yearly grants from MAFF and additional funds from the Japan Racing Association (JRA). It is now the leading member of the International Rice Genome Sequencing Project (IRGSP), a consortium of ten countries sharing the sequencing of the 12 rice chromosomes. The IRGSP adopts the clone-by-clone shotgun sequencing strategy so that each sequenced clone can be associated with a specific position on the genetic map and adheres to the policy of immediate release of the sequence data to the public domain. In December 2004, the IRGSP completed the sequencing of the rice genome. The high-quality and map-based sequence of the entire genome is now available in public databases.

Proper citation: Rice Genome Research Project (RRID:SCR_002268) Copy   

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http://www.broadinstitute.org/rat/public/index_main.html

Data set of pictures representing genetic linkage maps of the rat resulting from the integration of two F2 intercrosses (SHRSP x BN and FHH x ACI). Markers in common between the two crosses are connected by a line to define integration points. There are a total of 4,786 markers on these maps; 4375 WIBR/MIT CGR markers; 223 markers from the previously released Mit/Mgh rat maps and 188 markers from the National Institute of Arthritis and Musculoskeletal and Skin Diseases Arb rat maps. Pictures are drawn to a scale of 5cm (Kosombi) per inch. The changes in color of the backbone of the chromosome for each cross represents the space between any two framework loci. Markers in blue type are framework loci. Markers in green type are unique placement loci. Markers in black type are bouncy placement loci.

Proper citation: Genetic Maps of the Rat Genome (RRID:SCR_002266) Copy   

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

Database as central repository for both single base nucleotide substitutions and short deletion and insertion polymorphisms. Distinguishes report of how to assay SNP from use of that SNP with individuals and populations. This separation simplifies some issues of data representation. However, these initial reports describing how to assay SNP will often be accompanied by SNP experiments measuring allele occurrence in individuals and populations. Community can contribute to this resource.

Proper citation: dbSNP (RRID:SCR_002338) Copy   

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http://www.humgen.rwth-aachen.de/

Catalog of all changes detected in PKHD1 (Polycystic Kidney and Hepatic Disease 1) in a locus specific database. Investigators are invited to submit their novel data to this database. These data should be meaningful for clinical practice as well as of relevance for the reader interested in molecular aspects of polycystic kidney disease (PKD). There are also some links and information for ARPKD patients and their parents. Autosomal recessive polycystic kidney disease (ARPKD/PKHD1) is an important cause of renal-related and liver-related morbidity and mortality in childhood. This study reports mutation screening in 90 ARPKD patients and identifies mutations in 110 alleles making up a detection rate of 61%. Thirty-four of the detected mutations have not been reported previously. Two underlying mutations in 40 patients and one mutation in 30 cases are disclosed, and no mutation was detected on the remaining chromosomes. Mutations were found to be scattered throughout the gene without evidence of clustering at specific sites. PKHD1 mutation analysis is a powerful tool to establish the molecular cause of ARPKD in a given family. Direct identification of mutations allows an unequivocal diagnosis and accurate genetic counseling even in families displaying diagnostic challenges.

Proper citation: Autosomal Recessive Polycystic Kidney Disease Mutation Database (RRID:SCR_002290) Copy   

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https://www.genevestigator.com/gv/

A high performance search engine for gene expression that integrates thousands of manually curated public microarray and RNAseq experiments and nicely visualizes gene expression across different biological contexts (diseases, drugs, tissues, cancers, genotypes, etc.). There are two basic analysis approaches: # for a gene of interest, identify which conditions affect its expression. # for condition(s) of interest, identify which genes are specifically expressed in this/these conditions. Genevestigator builds on the deep integration of data, both at the level of data normalization and on the level of sample annotations. This deep integration allows scientists to ask new types of questions that cannot be addressed using conventional tools.

Proper citation: Genevestigator (RRID:SCR_002358) Copy   

•   Source: SciCrunch Registry