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http://compgen.rutgers.edu/crimap.shtml

Software application for constructing multilocus linkage map (entry from Genetic Analysis Software)

Proper citation: CRIMAP (RRID:SCR_000834) Copy   

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http://web.bioinformatics.ic.ac.uk/eqtlexplorer/

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 23,2022. An eQTL visualization tool that allows users to mine and understand data from a repository of genetical genomics experiments (entry from Genetic Analysis Software)

Proper citation: EQTL EXPLORER (RRID:SCR_001123) Copy   

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http://people.virginia.edu/~wc9c/TDTPC/Download.htm

Software program to compute the statistical power of the Transmission/Disequilibrium Test (TDT) analytically, based on the most accurate asymptotic algorithms up to date, and is applicable in very general situations, where different parental disease status, multiple children, mixed family type and recombination events are considered. Routine algorithms for Monte Carlo simulations with significant improvements are also implemented in this program. (entry from Genetic Analysis Software)

Proper citation: TDT-PC (RRID:SCR_001116) Copy   

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http://bmsr.usc.edu/software/targetgene/

MATLAB tool to effectively identify potential therapeutic targets and drugs in cancer using genetic network-based approaches. It can rapidly extract genetic interactions from a precompiled database stored as a MATLAB MAT-file without the need to interrogate remote SQL databases. Millions of interactions involving thousands of candidate genes can be mapped to the genetic network within minutes. While TARGETgene is currently based on the gene network reported in (Wu et al.,Bioinformatics 26:807-813, 2010), it can be easily extended to allow the optional use of other developed gene networks. The simple graphical user interface also enables rapid, intuitive mapping and analysis of therapeutic targets at the systems level. By mapping predictions to drug-target information, TARGETgene may be used as an initial drug screening tool that identifies compounds for further evaluation. In addition, TARGETgene is expected to be applicable to identify potential therapeutic targets for any type or subtype of cancers, even those rare cancers that are not genetically recognized. Identification of Potential Therapeutic Targets * Prioritize potential therapeutic targets from thousands of candidate genes generated from high-throughput experiments using network-based metrics * Validate predictions (prioritization) using user-defined benchmark genes and curated cancer genes * Explore biologic information of selected targets through external databases (e.g., NCBI Entrez Gene) and gene function enrichment analysis Initial Drug Screening * Identify for further evaluation existing drugs and compounds that may act on the potential therapeutic targets identified by TARGETgene * Explore general information on identified drugs of interest through several external links Operating System: Windows XP / Vista / 7

Proper citation: TARGETgene (RRID:SCR_001392) Copy   

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http://faculty.washington.edu/browning/beagle/beagle.html

Software package for analysis of large-scale genetic data sets with hundreds of thousands of markers genotyped on thousands of samples. BEAGLE can * phase genotype data (i.e. infer haplotypes) for unrelated individuals, parent-offspring pairs, and parent-offspring trios. * infer sporadic missing genotype data. * impute ungenotyped markers that have been genotyped in a reference panel. * perform single marker and haplotypic association analysis. * detect genetic regions that are homozygous-by-descent in an individual or identical-by-descent in pairs of individuals. Beagle can also be used in conjunction with PRESTO, a program for fast and flexible permutation testing. PRESTO can compute empirical distributions of order statistics, analyze stratified data, and determine significance levels for one-stage and two-stage genetic association studies. BEAGLE is written in Java and runs on any computing platform with a Java version 1.6 interpreter (e.g. Windows, Unix, Linux, Solaris, Mac).

Proper citation: BEAGLE (RRID:SCR_001789) Copy   

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http://www.math.hkbu.edu.hk/~mng/CLUSTAG/CLUSTAG.html

Software application that uses hierarchical clustering and graph methods for selecting tag SNPs (single nucleotide polymorphisms). Cluster and set-cover algorithms are developed to obtain a set of tag SNPs that can represent all the known SNPs in a chromosomal region, subject to the constraint that all SNPs must have a squared correlation R2 > C with at least one tag SNP, where C is specified by the user. The program is implemented with Java, and it can run in Windows platform as well as the Unix environment.

Proper citation: CLUSTAG (RRID:SCR_001816) Copy   

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http://research.i2r.a-star.edu.sg:8080/kleisli/demos/pedigree/

Software application (entry from Genetic Analysis Software)

Proper citation: PEDIGREE-VISUALIZER (RRID:SCR_000842) Copy   

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http://vorlon.case.edu/~jxl175/haplotyping.html

THIS RESOURCE IS NO LONGER IN SERVICE.Documented on August 23,2022. Software application for inferring haplotypes from genotypes on pedigree data (entry from Genetic Analysis Software)

Proper citation: PEDPHASE (RRID:SCR_000843) Copy   

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http://animalgene.umn.edu/locusmap/index.html

Software package designed for rapid linkage analysis and map construction of loci with a variety of inheritance modes. (entry from Genetic Analysis Software)

Proper citation: LOCUSMAP (RRID:SCR_000840) Copy   

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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://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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