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http://www-genome.wi.mit.edu/ftp/pub/software/rhmapper/

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on August 30, 2022. An interactive software program for radiation hybrid mapping (entry from Genetic Analysis Software)

Proper citation: RHMAPPER (RRID:SCR_000845) Copy   

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

Software application (entry from Genetic Analysis Software)

Proper citation: GS-EM (RRID:SCR_003992) Copy   

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http://titan.biotec.uiuc.edu/bee/honeybee_project.htm

A database integrating data from the bee brain EST sequencing project with data from sequencing and gene research projects from other organisms, primarily the fruit fly Drosophila melanogaster. The goal of Bee-ESTdb is to provide updated information on the genes of the honey bee, currently using annotation primarily from flies to suggest cellular roles, biological functions, and evolutionary relationships. The site allows searches by sequence ID, EST annotations, Gene Ontology terms, Contig ID and using BLAST. Very nice resource for those interested in comparative genomics of brain. A normalized unidirectional cDNA library was made in the laboratory of Prof. Bento Soares, University of Iowa. The library was subsequently subtracted. Over 20,000 cDNA clones were partially sequenced from the normalized and subtracted libraries at the Keck Center, resulting in 15,311 vector-trimmed, high-quality, sequences with an average read length of 494 bp. and average base-quality of 41. These sequences were assembled into 8966 putatively unique sequences, which were tested for similarity to sequences in the public databases with a variety of BLAST searches. The Clemson University Genomics Institute is the distributor of these public domain cDNA clones. For information on how to purchase an individual clone or the entire collection, please contact www.genome.clemson.edu/orders/ or generobi (at) life.uiuc.edu.

Proper citation: Honey Bee Brain EST Project (RRID:SCR_002389) Copy   

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http://www.preger.org/

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on January 14,2026. Sample collection of oocytes obtained from various sized antral follicles, and embryos obtained through a variety of different protocols. The PREGER makes it possible to undertake quantitative gene-expression studies in rhesus monkey oocytes and embryos through simple and cost-effective hybridization-based methods.

Proper citation: Primate Embryo Gene Expression Resource (RRID:SCR_002765) Copy   

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http://portal.ncibi.org/gateway/saga.html

SAGA (Substructure Index-based Approximate Graph Alignment) is a tool for querying a biological graph database to retrieve matches between subgraphs of molecular interactions and biological networks. SAGA implements an efficient approximate subgraph matching algorithm that can be used for a variety of biological graph matching problems such as the pathway matching SAGA uses to compare pathways in KEGG and Reactome. You can also use SAGA to find matches in literature databases that have been parsed into semantic graphs. In this use of SAGA, portions of PubMed have been parsed into graphs that have nodes representing gene names. A link is drawn between two genes if they are discussed in the same sentence (indicating there is potential association between the two genes). SAGA lets you match graphs between different databases even though the content is distinct and the databases organize pathways in different ways. This cross-database matching is achieved by SAGA's flexible approximate subgraph matching model that computes graph similarity, and allows for node gaps, node mismatches, and graph structural differences. Comparing pathways from different databases can be a useful precursor to pathway data integration. SAGA is very efficient for querying relatively small graphs, but becomes prohibitory expensive for querying large graphs. Large graph data sets are common in many emerging database applications, and most notably in large-scale scientific applications. To fully exploit the wealth of information encoded in graphs, effective and efficient graph matching tools are critical. Due to the noisy and incomplete nature of real graph datasets, approximate, rather than exact, graph matching is required. Furthermore, many modern applications need to query large graphs, each of which has hundreds to thousands of nodes and edges. TALE is an approximate subgraph matching tool for matching graph queries with a large number of nodes and edges. TALE employs a novel indexing technique that achieves a high pruning power and scales linearly with the database size.

Proper citation: Substructure Index-based Approximate Graph Alignment (RRID:SCR_003434) Copy   

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http://www.genomeutwin.org/index.htm

THIS RESOURCE IS NO LONGER IN SERVICE, documented August 29, 2016. Study of genetic and life-style risk factors associated with common diseases based on analysis of European twins. The population cohorts used in the Genomeutwin study consist of Danish, Finnish, Italian, Dutch, English, Australian and Swedish twins and the MORGAM population cohort. This project will apply and develop new molecular and statistical strategies to analyze unique European twin and other population cohorts to define and characterize the genetic, environmental and life-style components in the background of health problems like obesity, migraine, coronary heart disease and stroke, representing major health care problems worldwide. The participating 8 twin cohorts form a collection of over 0.6 million pairs of twins. Tens of thousands of DNA samples with informed consents for genetic studies of common diseases have already been stored from these population-based twin cohorts. Studies targeted to cardiovascular traits are now being undertaken in MORGAM, a prospective case-cohort study. MORGAM cohorts include approximately 6000 individuals, drawn from population-based cohorts consisting of more than 80 000 participants who have donated DNA samples.

Proper citation: GenomEUtwin (RRID:SCR_002843) Copy   

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http://www.geneticepi.com/Research/software/software.html

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on May 16,2023. Software application (entry from Genetic Analysis Software).

Proper citation: MILD (RRID:SCR_003335) Copy   

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http://linkage.rockefeller.edu/pawe3d/

Software application (entry from Genetic Analysis Software)

Proper citation: PAWE-3D (RRID:SCR_003326) Copy   

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http://microarray.ym.edu.tw:8080/tools/module/set/index.jsp?mode=home

A Java tool to evaluate and visualize the sample discrimination abilities of gene expression signatures. This tool provides a filtration function for signature identification and lies between clinical analyses and class prediction (or feature selection) tools.

Proper citation: SET (RRID:SCR_003605) Copy   

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http://www.ebi.ac.uk/Tools/

The European Bioinformatics Institute (EBI) toolbox area provides a comprehensive range of tools for the field of bioinformatics. These are subdivided into categories in the left menu for convenience. EBI has developed a large number of very useful bioinformatics tools. A few examples include: - Similarity & Homology - the BLAST or FASTA programs can be used to look for sequence similarity and infer homology. - Protein Functional Analysis - InterProScan can be used to search for motifs in your protein sequence. - Proteomic Services NEW - UniProt DAS server allows researchers to show their research results in the context of UniProtKB/Swiss-Prot annotation. - Sequence Analysis - ClustalW2 a sequence alignment tool. - Structural Analysis - MSDfold can be used to query your protein structure and compare it to those in the Protein Data Bank (PDB). - Web Services - provide programmatic access to the various databases and retrieval/analysis services EBI provides. - Tools Miscellaneous - Expression Profiler a set of tools for clustering, analysis and visualization of gene expression and other genomic data. Sponsors: This resource is sponsored by EBI.

Proper citation: Toolbox at the European Bioinformatics Institute (RRID:SCR_002872) Copy   

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http://ki.se/en/imm/eims-an-epidemiological-investigation-of-risk-factors-for-multiple-sclerosis

A multi-center population based epidemiological investigation of risk factors for Multiple Sclerosis (MS), where lifestyle- and environmental factors are examined systematically with concurrent genetic information. Newly diagnosed cases of MS in a geographically defined population and randomly chosen controls are identified and asked to answer a questionnaire on lifestyle, previous exposures at work, home and during spare time activities. For both cases and controls blood samples are taken for analysis of putative risk genes since environmental exposures probably contributes to disease only in individuals with certain genotypes. Exposures of interest are different sociodemographic factors, smoking, sunlight exposure, oral contraceptives / hormonal factors, butyrophilin (a milk protein), vaccinations, infections, atopic disease, organic solvents, mineral oils and a number of different psychosocial factors, such as critical lifetime events. Data from more than 1600 cases and 3200 controls are currently collected. (August 2014) The intention is to continue with the data collection over several years in order to analyse how genes and environment interact. The study is a collaboration between different institutions at Karolinska Institutet and neurological centers from 38 different hospitals in Sweden. Sample types * EDTA whole blood * DNA * Plasma * Serum

Proper citation: KI Biobank - EIMS (RRID:SCR_005898) Copy   

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http://www.geisinger.org/research/centers_departments/genomics/mycode/mycode.html

By collecting and analyzing blood samples from Geisinger''s large patient population, MyCode will help unlock the mysteries of some of the most devastating and debilitating diseases. Blood samples are obtained from patients of certain Geisinger specialty clinics to study specific conditions, such as obesity and cardiovascular disease, and also from patients of Geisinger primary care clinics to provide a representative sample of the regional population. More than 60,000 samples from over 23,000 Geisinger patients have been collected so far, and sample collection is ongoing. MyCode researchers use the blood samples to study the genetic causes of diseases and certain disease-related molecular mediators. Knowledge gained from these studies will allow researchers to pursue innovative approaches to disease prevention, diagnosis and treatment. To be of value for Genomic Medicine research, bio-banked samples must be connected to clinical data: MyCode allows genetic and molecular data about the samples to be connected to medical data in a way that protects patient identity. When a patient agrees to participate in MyCode, blood samples for the MyCode Project are collected during blood draws ordered as part of the patient''s routine medical care. After the sample is drawn and labeled, a staff member from the Weis Center for Research transports the blood to the Geisinger Clinic Genomics Core (GCGC) where it is processed for storage. At this stage, all personal identification markers are removed and the samples are assigned a randomly-selected identification number. A secure key is maintained that allows approved researchers to connect the samples to the clinical data for genomic studies in a way that ensures confidentiality of the information. To maintain confidentiality of MyCode data the code linking the research numbers and the electronic health records are kept in a password-protected files accessible only to MyCode team members. Additionally, all results generated from the samples are reported as a group so that individuals are not identified. The samples are stored indefinitely.

Proper citation: Geisinger Biobank (RRID:SCR_005652) Copy   

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http://www.geenivaramu.ee/en/

The Estonian Biobank is the population-based biobank of the EGCUT. The project is conducted in accordance with the Estonian Genes Research Act and all participants have signed a broad informed consent form (www.biobank.ee and Metspalu 2004, Drug Dev. Res.). As of December 2011, the biobank contains 51,515 participants (gene donors). The database of genotypic, phenotypic, health and genealogical information represents about 5% of Estonia''s adult population, and is the largest cohort ever gathered in Estonia. The age, sex and geographical distribution of this cohort reflect the structure of the adult population in Estonia. The database enables to conduct research in order to find links between genes, environmental factors, lifestyles and complex diseases or other traits. Active use of the biobank has started and although the first users are researchers all over the world with hundreds of different projects currently underway, industry is also interested. At the international level, the EGCUT will join the BBMRI follow-up program (ERIC) and through this channel provide service (biobanking, genotyping, sequencing and data analysis) for the centers in Europe who need it. Currently, the first follow-up study is underway and the molecular information of the cohort will be increased. For example, we have over 12 000 DNA samples analyzed by high density genotyping arrays and over 10 000 plasma samples analyzed by NMR scans, over 1000 individuals with RNA expression arrays, 2000 individuals with clinical laboratory analysis (over 40 tests) and over 60 full genomes are under deep sequencing. The infrastructure of the EGCUT includes a laboratory for DNA genotyping and next generation sequencing all based on Illumina platforms (HiScanSQ, HiSeq2000 and robotics), an IT unit (databases) with required computing power and storage space (1.2PB), data analysis team (bioinformatics and statistical genetics) and last but not least, a patient recruitment unit (health records, lifestyle and environmental information and biological samples ����?����������?? DNA, plasma and WBC from all 51515 gene donors). This is all located on 1000m2 in a brand new laboratory building, Riia str 23, Tartu, Estonia.

Proper citation: Estonian Genome Center (RRID:SCR_004467) Copy   

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http://www.well.ox.ac.uk/platypus

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on May 16,2023. Software tool designed for efficient and accurate variant detection in high throughput sequencing data. Haplotype based variant caller for next generation sequence data.

Proper citation: Platypus (RRID:SCR_005389) Copy   

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http://www.uni-bonn.de/~umt70e/soft.htm

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on May 5th,2023. Software application that calculates the sample size required for obtaining a prescribed power against a specified alternative for TDT. (entry from Genetic Analysis Software)

Proper citation: TDTPOWER (RRID:SCR_005021) Copy   

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http://www3.marshfieldclinic.org/chg/pages/default.aspx?page=chg_pers_med_res_prj

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on January 9, 2023. A large collection of biological samples and health information collected for the Personalized Medicine Research Project (PMRP) for use in biological research. Genetic information from 20,000 participants forms a database enabling scientists to study which genes cause disease, which genes predict reactions to drugs, and how environment and genes work together to cause disease. The goal of this project is to learn how to apply genetic science to human health. This knowledge will help researchers develop new medications and diagnostic tests, and will enable physicians to prescribe medications that work best for a particular person. Marshfield Clinic Personalized Medicine Research Project (PMRP) resources currently available: DNA, plasma, serum, questionnaire, electronic medical records to construct phenotypes; ability to recontact subjects for additional information (where they have given consent for recontact); stored pathology specimens collected for clinical purposes; 51 clinically relevant polymorphisms; Illumina 660 quad for ~4200 subjects aged 50+.

Proper citation: Marshfield Clinic Biobank (RRID:SCR_004368) Copy   

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http://epi.helmholtz-muenchen.de/kora-gen/index_e.php

KORA-gen is infrastructure to provide phenotypes, genotypes and biosamples for collaborative genetic epidemiological research. From all four surveys that have been conducted so far, the following biological material is on hand: genomic DNA, blood serum, blood plasma and EBV immortalized cell lines (form KORA S4 only). These have been extracted from blood samples and are stored in nitrogen tanks and -80 degrees C refrigerators. Genomic DNA from more than 18.000 adult subjects from Augsburg and the surrounding counties is available at present. So far, EBV immortalized cell lines from 1.600 participants are cultivated. To meet the manifold demands of researchers with genetic and molecular questions KORA-gen fulfills the following prerequisites for successful genetic-epidemiological research: * representative samples from the general population, * well characterized disease phenotypes and intermediate phenotypes, * information on environmental factors, * availability of genomic DNA, serum, plasma and urine, as well as EBV immortalized cell lines. In total, four population based health surveys have been conducted between 1984 and 2000 with 18000 participants in the age range of 25 to 74 years, and a biological specimen bank was established in order to enable scientists to perform epidemiologic research with respect to molecular and genetic questions. The KORA study center conducts regular follow-up investigations and has collected a wealth of information on sociodemography, general medical history, environmental factors, smoking, nutrition, alcohol consumption, and various laboratory parameters. This unique resource will be increased further by follow-up studies of the cohort. The assessment of statistical questions covers the definition of the study design and the calculation of statistical power. Furthermore, we offer assistance in data analysis. Kora-gen can be used by external partners. Interested parties can inform themselves interactively via internet about the available data and rules of access. The genotypic data base is a common resource to all partners.

Proper citation: KORA-gen (RRID:SCR_004510) Copy   

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http://genetics.agrsci.dk/~bg/popgen/

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on January 11, 2023. Software application that calculates a number of different genetic identities, phylogeny reconstructing measures, and distance reconstructing measures (entry from Genetic Analysis Software)

Proper citation: POPDIST (RRID:SCR_004904) Copy   

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http://bioinf.wehi.edu.au/folders/melanie/haploclusters.html

Software program designed to detect excess haplotypes sharing in datasets consisting of case and control haplotypes. Excess haplotype sharing can be seen around disease loci in case samples since LD persists longer here than in the controls where LD is persisting only according to the relatedness of the individuals in the population, i.e. the age of the population. (entry from Genetic Analysis Software)

Proper citation: HAPLOCLUSTERS (RRID:SCR_007439) Copy   

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http://gaow.github.io/genetic-analysis-software/l-1.html#ldsupport

Software application (entry from Genetic Analysis Software)

Proper citation: LDSUPPORT (RRID:SCR_007036) Copy   

•   Source: SciCrunch Registry