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http://www.multifactordimensionalityreduction.org/

Software application that is a data mining strategy for detecting and characterizing nonlinear interactions among discrete attributes (e.g. SNPs, smoking, gender, etc.) that are predictive of a discrete outcome (e.g. case-control status). The MDR software combines attribute selection, attribute construction and classification with cross-validation to provide a powerful approach to modeling interactions. (entry from Genetic Analysis Software)

Proper citation: MDR (RRID:SCR_013427) Copy   

•   Source: SciCrunch Registry

http://lbm.ab.a.u-tokyo.ac.jp/~iwata/antmap/

Software application based on the Ant Colony Optimization to solve the special case of the traveling salesman problem of ordering markers when the number of loci is large. ANYMAP performs segregation test, linkage grouping and locus ordering, and constructs a linkage map rapidly. (entry from Genetic Analysis Software)

Proper citation: ANTMAP (RRID:SCR_013426) Copy   

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http://www.gridqtl.org.uk/

Publicly available Web-based application that can perform QTL mapping on a variety of population types. GridQTL will extend the functionality of QTLExpress by adding new and advanced approaches for modelling QTL analysis in simple and complex populations. These new methods will be available on a Grid system that will offer flexible workflow management, resource allocation, data persistence, detached execution of simulations and the scalability required for the increase in data volume, data sources and complexity required by the new models. (entry from Genetic Analysis Software)

Proper citation: GRIDQTL (RRID:SCR_013397) Copy   

•   Source: SciCrunch Registry

http://vipbg.vcu.edu/vipbg/trimhap//

Software application for linkage disequilibrium mapping based on ancestral founder haplotypes. Method uses haplotype data from general pedigrees. (entry from Genetic Analysis Software)

Proper citation: TRIMHAP (RRID:SCR_013512) Copy   

•   Source: SciCrunch Registry

http://www.bio.unc.edu/faculty/vision/lab/mappop/

Software application that selects high resolution mapping subsamples and performs bin mapping (entry from Genetic Analysis Software)

Proper citation: MAPPOP (RRID:SCR_013490) Copy   

•   Source: SciCrunch Registry

http://dlin.web.unc.edu/software/SNPMStat/

A command-line program for the statistical analysis of SNP-disease association in case-control/cohort/cross-sectional studies with potentially missing genotype data. SNPMStat allows the user to estimate or test SNP effects and SNP-environment interactions by maximizing the (observed-data) likelihood that properly accounts for phase uncertainty, study design and gene-environment dependence. For SNPs without missing data, the program performs the standard association analysis. For typed SNPs with missing data or untyped SNPs, the program performs the maximum-likelihood analysis. (entry from Genetic Analysis Software)

Proper citation: SNPMSTAT (RRID:SCR_013339) Copy   

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http://www.cbil.ece.vt.edu/ResearchOngoingSNP.htm

Software application (entry from Genetic Analysis Software)

Proper citation: MECPM (RRID:SCR_013341) Copy   

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http://www.bios.unc.edu/~lin/software/MAOS/

Software application that implements valid and efficient statistical methods for meta-analysis of genomewide association studies with overlapping subjects. The current release performs logistic regression analysis of individual level data under the additive mode of inheritance. Data from genome-wide association studies are often analyzed jointly for the purposes of combining information from multiple studies of the same disease or comparing results across different disorders. In many instances, the same subjects appear in multiple studies. Failure to account for overlapping subjects can greatly inflate type I error when combining results from multiple studies of the same disease and can drastically reduce power when comparing results across different disorders. (entry from Genetic Analysis Software)

Proper citation: MAOS (RRID:SCR_013351) Copy   

•   Source: SciCrunch Registry

http://www.sanger.ac.uk/science/tools/alien-hunter

Software for the prediction of putative Horizontal Gene Transfer (HGT) events with the implementation of Interpolated Variable Order Motifs (IVOMs). The predictions (embl format) can be automatically loaded into Artemis genome viewer.

Proper citation: Alien-hunter (RRID:SCR_015967) Copy   

•   Source: SciCrunch Registry

http://clinicaltrials.gov/show/NCT01211678

A consortium evaluating a new biomarker screening test that might help identify patients with rheumatoid arthritis (RA) who are unlikely to benefit from anti-tumor necrosis factor-alpha (TNFalpha) medications. BATTER-UP will enroll around 1,000 patients being treated by one of several marketed anti-TNF RA drugs: Enbrel, Remicade, Humira, Simponi, or Cimzia. Through data analyses and predictive response modeling, the consortium aims to better understand which patients with RA will derive the greatest benefit from TNF inhibitors. The investigators in this observational study will attempt to validate an 8-gene biomarker set based on work by Biogen Idec researchers as likely to predict anti-TNF responsiveness in patients with RA. In preliminary results, the 8-gene biomarker set predicted with 89% accuracy individuals who did not reach European League Against Rheumatism (EULAR) Disease Activity Score (DAS)-28 good response after 14 weeks of treatment. The 8 genes included in the screen are CLTB, MXRA7, CXorf52, COL4A3BP, YIPF6, FAM44A, SFRS2, and PGK1. Biological samples and clinical outcome information will be used to confirm and extend the utility of previously published biomarkers that can predict response to anti-TNF agents. These data may also generate new hypotheses for further testing. The BATTER-UP samples and data will be established as a reference set for investigation of personalized medicine in RA. The study will be a resource of DNA and other biological materials that can be investigated for biomarkers in the future as new technologies arise.

Proper citation: Biomarkers of Anti-TNF Treatment Efficacy in Rheumatoid Arthritis - Unresponsive Populations (RRID:SCR_004019) Copy   

•   Source: SciCrunch Registry

http://www.genmapp.org/

GenMAPP is a free computer application designed to visualize gene expression and other genomic data on maps representing biological pathways and groupings of genes. Integrated with GenMAPP are programs to perform a global analysis of gene expression or genomic data in the context of hundreds of pathway MAPPs and thousands of Gene Ontology Terms (MAPPFinder), import lists of genes/proteins to build new MAPPs (MAPPBuilder), and export archives of MAPPs and expression/genomic data to the web. The main features underlying GenMAPP are: *Draw pathways with easy to use graphics tools *Color genes on MAPP files based on user-imported genomic data *Query data against MAPPs and the GeneOntology Enhanced features include the simultaneous view of multiple color sets, expanded species-specific gene databases and custom database options.

Proper citation: Gene Map Annotator and Pathway Profiler (RRID:SCR_005094) Copy   

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http://tikus.gsf.de

THIS RESOURCE IS NO LONGER IN SERVICE, documented on October 23, 2014. Consortium that generated a reference library of gene trap sequence tags (GTST) from insertional mutations generated in mouse embryonic stem (ES) cells. The gene trap database represents a repository of sequences produced in a large scale gene trap screen in mouse ES cells using various gene trapping vectors which are delivered either by electroporation or retroviral infections. A type of retroviral gene trap vector has been developed that can induce conditional mutations in most genes expressed in mouse embryonic stem (ES) cells. The vectors rely on directional site-specific recombination systems that can repair and re-induce gene trap mutations when activated in succession. After the gene traps are inserted into the mouse genome, genetic mutations can be produced at a particular time and place in somatic cells. In addition to their conditional features, the vectors create multipurpose alleles amenable to a wide range of post-insertional modifications. Here they have used these directional recombination vectors to assemble the largest library of ES cell lines with conditional mutations in single genes yet assembled, presently totaling 1,000 unique genes. The trapped ES cell lines, which can be ordered from the German Gene Trap Consortium, are freely available to the scientific community.

Proper citation: German Gene Trap Consortium (RRID:SCR_008532) Copy   

•   Source: SciCrunch Registry

https://www.ncbi.nlm.nih.gov/UniGene/help.cgi?item=DDD

Software tool for comparing EST profiles in order to identify genes with significantly different expression levels.

Proper citation: Digital Differential Display (DDD) (RRID:SCR_016638) Copy   

•   Source: SciCrunch Registry

https://github.com/AlexsLemonade/refinebio

Software tool to uniformly process and normalize large amounts of data. Harmonizes petabytes of publicly available biological data into ready-to-use datasets for cancer researchers and AI/ML scientists.

Proper citation: refine.bio (RRID:SCR_017471) Copy   

•   Source: SciCrunch Registry

https://github.com/lufuhao/ExonerateTransferAnnotation

Software tool as pipeline to make anntotations using cDNA and CDS sequences.

Proper citation: ExonerateTransferAnnotation (RRID:SCR_017557) Copy   

•   Source: SciCrunch Registry

https://www.ibdgc.org/

Repository of biospecimen and phenotype data collected from Crohn's disease and ulcerative colitis cases and controls recruited at six sites throughout North America that are available to the scientific community. Phenotyping is performed using a standardized protocol, and lymphoblastoid cell lines are established for each subject. Phenotype data for each subject are collected by the Consortium's Data Coordinating Center (DCC), and phenotype data for all subjects with DNA samples are available. The resulting DNA samples have already been utilized by the Consortium to complete various association studies, including genome-wide association studies using dense genotyping arrays. Researchers can obtain DNA samples and phenotype, genotype, and pedigree data through the Data Repository. GWAS data must be requested through dbGAP. The IBDGC is involved with independent genetic research studies and actively works with members of the IBD and genetic communities on collaborative projects. They are also members of the International IBD Genetics Consortium. Phenotype Tools: The Consortium Phenotype Committee, led by Dr. Hillary Steinhart designed and validated paper forms to collect extensive phenotype data on Crohn's Disease and ulcerative colitis. Consortium phenotype tools are available for use by non-Consortium members.

Proper citation: NIDDK Inflammatory Bowel Disease Genetics Consortium (RRID:SCR_001461) Copy   

•   Source: SciCrunch Registry

https://catalog.coriell.org/

A biorepository and biomaterial supply resource which establishes, verifies, maintains, and distributes cells, cultures and DNA derived from cell cultures to the scientific community around the world. Scientists can browse the sample catalog and request specific cell lines for their research studies. An inventory of the remaining stock of each cell line and DNA preparation is presented in real time. Coriell's significant cell biobank collections include: NIGMS Human Genetic Cell Repository, NINDS Human Genetics DNA and Cell Line Repository, NIA Aging Cell Repository, NHGRI Sample Repository for Human Genetic Research, NEI Age-Related Eye Disease Study (AREDS) Genetic Repository, HD Community BioRepository, American Diabetes Association, GENNID Study, and Autism Research Resource. The repositories are ISO 9000-2001 compliant.

Proper citation: Coriell Cell Repositories (RRID:SCR_003244) Copy   

•   Source: SciCrunch Registry

http://www.genetrap.org/

Consortium represents all publicly available gene trap cell lines, which are available on non-collaborative basis for nominal handling fees. Researchers can search and browse IGTC database for cell lines of interest using accession numbers or IDs, keywords, sequence data, tissue expression profiles and biological pathways, can find trapped genes of interest on IGTC website, and order cell lines for generation of mutant mice through blastocyst injection. Consortium members include: BayGenomics (USA), Centre for Modelling Human Disease (Toronto, Canada), Embryonic Stem Cell Database (University of Manitoba, Canada), Exchangeable Gene Trap Clones (Kumamoto University, Japan), German Gene Trap Consortium provider (Germany), Sanger Institute Gene Trap Resource (Cambridge, UK), Soriano Lab Gene Trap Resource (Mount Sinai School of Medicine, New York, USA), Texas Institute for Genomic Medicine - TIGM (USA), TIGEM-IRBM Gene Trap (Naples, Italy).

Proper citation: International Gene Trap Consortium (RRID:SCR_002305) Copy   

•   Source: SciCrunch Registry

http://discover.nci.nih.gov/gominer/

GoMiner is a tool for biological interpretation of "omic" data including data from gene expression microarrays. Omic experiments often generate lists of dozens or hundreds of genes that differ in expression between samples, raising the question, What does it all mean biologically? To answer this question, GoMiner leverages the Gene Ontology (GO) to identify the biological processes, functions and components represented in these lists. Instead of analyzing microarray results with a gene-by-gene approach, GoMiner classifies the genes into biologically coherent categories and assesses these categories. The insights gained through GoMiner can generate hypotheses to guide additional research. GoMiner displays the genes within the framework of the Gene Ontology hierarchy in two ways: * In the form of a tree, similar to that in AmiGO * In the form of a "Directed Acyclic Graph" (DAG) The program also provides: * Quantitative and statistical analysis * Seamless integration with important public databases GoMiner uses the databases provided by the GO Consortium. These databases combine information from a number of different consortium participants, include information from many different organisms and data sources, and are referenced using a variety of different gene product identification approaches.

Proper citation: GoMiner (RRID:SCR_002360) Copy   

•   Source: SciCrunch Registry

http://www.betacell.org/

THIS RESOURCE IS NO LONGER IN SERVICE, documented May 10, 2017. A pilot effort that has developed a centralized, web-based biospecimen locator that presents biospecimens collected and stored at participating Arizona hospitals and biospecimen banks, which are available for acquisition and use by researchers. Researchers may use this site to browse, search and request biospecimens to use in qualified studies. The development of the ABL was guided by the Arizona Biospecimen Consortium (ABC), a consortium of hospitals and medical centers in the Phoenix area, and is now being piloted by this Consortium under the direction of ABRC. You may browse by type (cells, fluid, molecular, tissue) or disease. Common data elements decided by the ABC Standards Committee, based on data elements on the National Cancer Institute''s (NCI''s) Common Biorepository Model (CBM), are displayed. These describe the minimum set of data elements that the NCI determined were most important for a researcher to see about a biospecimen. The ABL currently does not display information on whether or not clinical data is available to accompany the biospecimens. However, a requester has the ability to solicit clinical data in the request. Once a request is approved, the biospecimen provider will contact the requester to discuss the request (and the requester''s questions) before finalizing the invoice and shipment. The ABL is available to the public to browse. In order to request biospecimens from the ABL, the researcher will be required to submit the requested required information. Upon submission of the information, shipment of the requested biospecimen(s) will be dependent on the scientific and institutional review approval. Account required. Registration is open to everyone., documented on August 1, 2015. Consortium that aims to facilitate interdisciplinary collaborations to advance the understanding of pancreatic islet development and function, with the goal of developing innovative therapies to correct the loss of beta cell mass in diabetes, including cell reprogramming, regeneration and replacement. They are responsible for collaboratively generating the necessary reagents, mouse strains, antibodies, assays, protocols, technologies and validation assays that are beyond the scope of any single research effort. The scientific goals for the BCBC are to: * Use cues from pancreatic development to directly differentiate pancreatic beta cells and islets from stem / progenitor cells for use in cell-replacement therapies for diabetes, * Determine how to stimulate beta cell regeneration in the adult pancreas as a basis for improving beta cell mass in diabetic patients, * Determine how to reprogram progenitor / adult cells into pancreatic beta-cells both in-vitro and in-vivo as a mean for developing cell-replacement therapies for diabetes, and * Investigate the progression of human type-1 diabetes using patient-derived cells and tissues transplanted in humanized mouse models. Many of the BCBC investigator-initiated projects involve reagent-generating activities that will benefit the larger scientific community. The combination of programs and activities should accelerate the pace of major new discoveries and progress within the field of beta cell biology.

Proper citation: Beta Cell Biology Consortium (RRID:SCR_005136) Copy   

•   Source: SciCrunch Registry