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http://www.animalgenome.org/pigs/nagrp.html

Database and resources on the pig genome.

Proper citation: U.S. Pig Genome Project (RRID:SCR_008151) Copy   

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http://csgr.pgml.uga.edu/

The objective of this project is to develop physical maps of the sorghum and rice genomes, based on BAC contigs that are cross-linked to each other and also to genetic maps and BAC islands for other large-genome crops and a library of ca. 50,000 expressed-sequence tags (EST''s) and corresponding cDNA clones, from diverse sorghum organs and developmental states. It also aims to improve understanding of genetic diversity and allelic richness that might be harbored ex situ (in gene banks) or in situ (in nature), and refine techniques for assesing allelic richness and Expedite data acquisition and utilization by a sound parnership between laboratory scientists and computational biologists. Specific goals of developing physical maps of sorghum and rice genomes include: -Enrich cross-links between sorghum and rice by mapping additional rice probes on sorghum. -Apply mapped DNA probes to macroarrays of sorghum, sugarcane, rice, and maize BACs. -Fingerprint 10x BAC libraries of Sorghum bicolor and S. propinquum. Libraries presently 3x and 6x respectively, to be expanded to 10x each. -Use fragment-matching (BAC-RF) method to determine locus-specificity in polyploids. - Contig assembly based on 1-3, plus rice BAC fingerprints generated under a separate Novartis project. -Evaluate methodology for rapid high-throughput assignment of new ESTs to BACs. -Conduct genomic sequencing in a region duplicated in both sorghum and arabidopsis. Selected BACs from sorghum(2), sugarcane, maize, rice, wheat. By improving the understanding of genetic diversity and allelic richness, the goal is to: -Sequence previously mapped sorghum DNA probes. -Discover & characterize 100 single nucleotide polymorphisms (SNPs) from cDNA markers. -Develop colorimetric high-throughput genotyping assays, and utilize to assess genetic diversity in geographically- and phenotypically-diverse sorghums. -Develop colorimetric high-throughput asssays for identifying phytochrome allelic variation, and apply these assays to a core collection representing a large set of genetic resources. -Support informatics group to streamline cataloging of DNA-level information relevant to large genetic resources collections. Lastly, the goals of expediting data acquisition and utilization include: -A new web-based resource for 3D-integration and visualization of structural and functional genomic data will be developed. -New sequence assembly and alignment software SABER (Sequence AssemBly in the presence of ERror), and PRIMAL(Practical RIgorous Multiple ALignment), will be evaluated with reference to existing standards (PHRED, PHRAP). -Specialized image processing and image analysis tools will be developed for acquistion and interpretation of qualitative and quantitative hybridization signals. To deal expeditiously with large volumes of data, parallel processing approaches will be investigated. Sponsors: * National Science Foundation (NSF) * National Sorghum Producers * University of Georgia Research Foundation (UGARF) * Georgia Research Alliance (GRA)

Proper citation: Comparative Saccharinae Genomics Resource (RRID:SCR_008153) Copy   

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http://genewindow.nci.nih.gov/

Software tool for pre- and post-genetic bioinformatics and analytical work, developed and used at the Core Genotyping Facility (CGF) at the National Cancer Institute. While Genewindow is implemented for the human genome and integrated with the CGF laboratory data, it stands as a useful tool to assist investigators in the selection of variants for study in vitro, or in novel genetic association studies. The Genewindow application and source code is publicly available for use in other genomes, and can be integrated with the analysis, storage, and archiving of data generated in any laboratory setting. This can assist laboratories in the choice and tracking of information related to genetic annotations, including variations and genomic positions. Features of GeneWindow include: -Intuitive representation of genomic variation using advanced web-based graphics (SVG) -Search by HUGO gene symbol, dbSNP ID, internal CGF polymorphism ID, or chromosome coordinates -Gene-centric display (only when a gene of interest is in view) oriented 5 to 3 regardless of the reference strand and adjacent genes -Two views, a Locus Overview, which varies in size depending on the gene or genomic region being viewed and, below it, a Sequence View displaying 2000 base pairs within the overview -Navigate the genome by clicking along the gene in the Locus Overview to change the Sequence View, expand or contract the genomic interval, or shift the view in the 5 or 3 direction (relative to the current gene) -Lists of available genomic features -Search for sequence matches in the Locus Overview -Genomic features are represented by shape, color and opacity with contextual information visible when the user moves over or clicks on a feature -Administrators can insert newly-discovered polymorphisms into the Genewindow database by entering annotations directly through the GUI -Integration with a Laboratory Information Management System (LIMS) or other databases is possible

Proper citation: GeneWindow (RRID:SCR_008183) Copy   

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http://www.jax.org/imr/index.html

THIS RESOURCE IS NO LONGER IN SERVICE, documented on June 08, 2012. The function of the IMR is to select, import, cryopreserve, maintain, and distribute these important strains of mice to the research community. To improve their value for research, the IMR also undertakes genetic development of stocks, such as transferring mutant genes or transgenes to defined genetic backgrounds and combining transgenes and/or targeted mutations to create new mouse models for research. The function of the IMR is to: * select biomedically important stocks of transgenic, chemically induced, and targeted mutant mice * import these stocks into the Jackson Laboratory by rederivation procedures that rid them of any pathogens they might carry * cryopreserve embryos from these stocks to protect them against accidental loss and genetic contamination * backcross the mutation onto an inbred strain, if necessary * distribute them to the scientific community More than 1000 mutant stocks have been accepted by the IMR from 1992 through December 2006. Current holdings include models for research on cancer; breast cancer; immunological and inflammatory diseases; neurological diseases; behavioral, cardiovascular and heart diseases; developmental, metabolic and other diseases; reporter (e.g., GFP) and recombinase (e.g., cre/loxP) strains. About eight strains a month are being added to the IMR holdings. Research is being conducted on improved methods for assisted reproduction and speed congenic production. Most of the targeted mutants arrive on a mixed 129xC57BL/6 genetic background, and as many of these as possible are backcrossed onto an inbred strain (usually C57BL/6J). In addition, new mouse models are being created by intercrossing carriers of specific transgenes and/or targeted mutations. Simple sequence length polymorphism DNA markers are being used to characterize and evaluate differences between inbred strains, substrains, and embryonic stem cell lines.

Proper citation: Induced Mutant Resource (RRID:SCR_008366) Copy   

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http://www.biomedbridges.eu/

Consortium of 12 Biomedical sciences research infrastructure (BMS RI) partners to develop a shared e-infrastructure to allow interoperability between data and services in the biological, medical, translational and clinical domains (providing a complex knowledge environment comprising standards, ontologies, data and services) and thus strengthen biomedical resources in Europe. The BMS RIs are on the roadmap of the European Strategy Forum on Research Infrastructures (ESFRI). Connecting several European research infrastructures brings a diversity of ethical, legal and security concerns including data security requirements for participating e-Infrastructures that are storing or processing patient-related data (or biosamples): EATRIS, ECRIN, BBMRI, EuroBioImaging and EMBL-EBI. In addition, INSTRUCT is interested in secure sample transport and in intellectual property rights; Infrafrontier stores high-throughput data from mice. BBMRI with its focus on the availability of biomaterials is currently emphasizing aspects like k-anonymity and metadata management for its data. Sharing of imaging data by Euro-BioImaging poses challenges with respect to anonymisation and intellectual property. Therefore, an ethical, regulatory and security framework for international data sharing that covers these diverse areas and different types of data (e.g. clinical trials data, mouse data, and human genotype and DNA sequence data) is of crucial importance. The outcomes will lead to real and sustained improvement in the services the biomedical sciences research infrastructures offer to the research community. Data curation and sample description will be improved by the adoption of best practices and agreed standards. Many improvements will emerge from new interactions between RIs created by data linkage and networking. Ensuring access to relevant information for all life science researchers across all BMS RIs will enable scientists to conduct and share cutting-edge research.

Proper citation: BioMedBridges (RRID:SCR_006179) Copy   

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https://bdsc.indiana.edu/

Collects, maintains and distributes Drosophila melanogaster strains for research. Emphasis is placed on genetic tools that are useful to a broad range of investigations. These include basic stocks of flies used in genetic analysis such as marker, balancer, mapping, and transposon-tagging strains; mutant alleles of identified genes, including a large set of transposable element insertion alleles; defined sets of deficiencies and a variety of other chromosomal aberrations; engineered lines for somatic and germline clonal analysis; GAL4 and UAS lines for targeted gene expression; enhancer trap and lacZ-reporter strains with defined expression patterns for marking tissues; and a collection of transposon-induced lethal mutations.

Proper citation: Bloomington Drosophila Stock Center (RRID:SCR_006457) Copy   

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https://www.nimhgenetics.org/

Collaborative venture between the National Institute of Mental Health (NIMH) and several academic institutions. Repository facilitates psychiatric genetic research by providing patient and control samples and phenotypic data for wide-range of mental disorders and Stem Cells.Stores biosamples, genetic, pedigree and clinical data collected in designated NIMH-funded human subject studies. RGR database likewise links to other repositories holding data from same subjects, including dbGAP, GEO and NDAR. Allows to access these data and biospecimens (e.g., lymphoblastoid cell lines, induced pluripotent cell lines, fibroblasts) and further expand genetic and molecular characterization of patient populations with severe mental illness.

Proper citation: NIMH Repository and Genomics Resources (RRID:SCR_006698) Copy   

•   Source: SciCrunch Registry

https://www.drugabuse.gov/publications/drugfacts/genetics-epigenetics-addiction

THIS RESOURCE IS NO LONGER IN SERVICE, documented on July 17, 2013. An archived video on the web providing comprehensive and hands-on training in genetics and epigenetic methodology. The purpose of the course is to provide an introduction to approaches and tools for identifying genes that confer vulnerability to addiction and individual differences in responses to treatments. The course is targeted to those who are new to the field of addiction genetics. The course was held over 5 days with lectures and hands-on demonstrations given each day. Viewers of the course will gain familiarity with conceptual and practical approaches to complex disorders using relevant genetic and epigenetic databases, and appropriate statistical and empirical approaches. Topics covered Behavioral genetics, genetic epidemiology, twin and adoption studies, statistical genetic concepts and approaches for mapping complex traits, haplotype based approaches for association mapping, genome-wide scans for addictive disorders, application of linkage for mapping genes and genetic loci for addictive disorders, pharmacogenomics of treatment of addictive disorders, Baysian Methods for identifying gene-gene interactions, analysis of copy number variation, practical use of genetic databases, mapping of complex traits in mice, methods for analyzing gene expression, and methods for doing epigenetic analysis are covered. The course was held April 4, 2008, at the Bethesda North Marriott Hotel and Conference Center, 5701 Marinelli Road, Bethesda, MD 20852.

Proper citation: Short Course on the Genetics and Epigenetics of Addiction National Institute on Drug Abuse: Archived Video (RRID:SCR_002783) Copy   

•   Source: SciCrunch Registry

http://cran.r-project.org/web/packages/gap/

GAP is designed as an integrated package for genetic data analysis of both population and family data. Currently, it contains functions for sample size calculations of both population-based and family-based designs, classic twin models, probability of familial disease aggregation, kinship calculation, some statistics in linkage analysis, and association analysis involving one or more genetic markers including haplotype analysis with or without environmental covariates.

Proper citation: Genetic Analysis Package (RRID:SCR_003006) Copy   

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https://www.ddbj.nig.ac.jp/jga/index-e.html

A service for permanent archiving and sharing of all types of personally identifiable genetic and phenotypic data resulting from biomedical research projects. The JGA contains exclusive data collected from individuals whose consent agreements authorize data release only for specific research use or to bona fide researchers. Strict protocols govern how information is managed, stored and distributed by the JGA. Once processed, all data are encrypted. The JGA accepts only de-identified data approved by JST-NBDC. The JGA implements access-granting policy whereby the decisions of who will be granted access to the data resides with the JST-NBDC. After data submission the JGA team will process the data into databases and archive the original data files. The accepted data types include manufacturer-specific raw data formats from the array-based and new sequencing platforms. The processed data such as the genotype and structural variants or any summary level statistical analyses from the original study authors are stored in databases. The JGA also accepts and distributes any phenotype data associated with the samples. For other human biological data, please contact the NBDC human data ethical committee.

Proper citation: Japanese Genotype-phenotype Archive (JGA) (RRID:SCR_003118) Copy   

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http://www.transformproject.eu/portfolio-item/d6-2-clinical-research-information-model/

A clinical research information model for the integration of clinical research covering randomized clinical trials (RCT), case-control studies and database searches into the TRANSFoRm application development. TRANSFoRm clinical research is based on primary care data, clinical data and genetic data stored in databases and electronic health records and employs the principle of reusing primary care data, adapting data collection by patient reported outcomes (PRO) and eSource based Case Report Forms. CRIM was developed using the TRANSFoRm clinical use cases of GORD and Diabetes. Their use case driven approach consisted of three levels of modelling drawing heavily on the clinical research workflow of the use cases. Different available information models were evaluated for their usefulness to represent TRANSFoRm clinical research, including for example CTOM of caBIG, Primary Care Research Object Model (PRCOM) of ePCRN and BRIDG of CDISC. The PCROM model turned out to be the most suitable and it was possible to extend and modify this model with only 12 new information objects, 3 episode of care related objects and 2 areas to satisfy all requirements of the TRANSFoRm research use cases. Now the information model covers Good Clinical Practice (GCP) compliant research, as well as case control studies and database search studies, including the interaction between patient and GP (family doctor) during patient consultation, appointment, screening, patient recruitment and adverse event reporting.

Proper citation: TRANSFoRm Clinical Research Information Model (RRID:SCR_003889) Copy   

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http://www.europeanlung.org/en/projects-and-research/projects/airprom/

Consortium focused on developing computer and physical models of the airway system for patients with asthma and chronic obstructive pulmonary disease (COPD). Developing accurate models will better predict how asthma and COPD develop, since current methods can only assess the severity of disease. They aim to bridge the gaps in clinical management of airways-based disease by providing reliable models that predict disease progression and the response to treatment for each person with asthma or COPD. A data management platform provides a secure and sustainable infrastructure that semantically integrates the clinical, physiological, genetic, and experimental data produced with existing biomedical knowledge from allied consortia and public databases. This resource will be available for analysis and modeling, and will facilitate sharing, collaboration and publication within AirPROM and with the broader community. Currently the AirPROM knowledge portal is only accessible by AirPROM partners.

Proper citation: AirPROM (RRID:SCR_003827) Copy   

•   Source: SciCrunch Registry

http://betabat.ulb.ac.be/

Project that aims to develop new treatment strategies based on knowledge of cellular dysfunction in diabetes. They will perform a detailed organelle diagnosis based on both focused and systems biology approaches, which will provide the scientific rationale for the design of specific interventions to boost the capacity of beta cells and brown adipocytes to regain homeostatic control. They propose that only by understanding the complex molecular mechanisms triggering cellular dysfunction in diabetes, and by integrating this knowledge at the systems level, will it be possible to develop interventional therapies that protect and restore beta cell and (Brown adipose tissue) BAT function. The ultimate goal is to offer individual therapeutic choices based on both genetic information and organelle diagnosis.

Proper citation: BetaBat (RRID:SCR_003834) Copy   

•   Source: SciCrunch Registry

http://www.genome.gov/Glossary/

Glossary of Genetic Terms to help everyone understand the terms and concepts used in genetic research. In addition to definitions, specialists in the field of genetics share their descriptions of terms, and many terms include images, animation and links to related terms.

Proper citation: Talking Glossary of Genetic Terms (RRID:SCR_003215) Copy   

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http://www.oncotrack.eu/

An international consortium to develop and assess novel approaches to identify and characterize biological markers for colon cancer that will deepen the understanding of the variable make-up of tumors and how this affects the way patients respond to treatment. They will use cutting edge laboratory-based genome sequencing techniques coupled to novel computer modelling approaches to study both the biological heterogeneity of colon cancers (i.e. patient to patient variability) as well as tumor variation within the patient for example, by comparing primary tumors with metastases. This five year project brings together top scientists from European academic institutions offering a wide range of expertise, and partners them with pharmaceutical companies. The project is based on the premise that this genetic and epigenetic information, combined with a description of the molecular pathology of the tumor, will allow OncoTrack to generate a more accurate in-silico model of the cancer cell. This will facilitate the identification of predictive markers that can be used to guide the optimal therapy strategy at the level of the individual patient - and will also provide on-going prognostic guidance for the clinician. This project will not only advance understanding of the fundamental biology of colon cancers but will provide the means and approach for the identification of previously undetected biomarkers not only in the cancer under study, but potentially also in other solid cancers and, in doing so, open the door for personalized management of the oncology patient.

Proper citation: OncoTrack (RRID:SCR_003767) Copy   

•   Source: SciCrunch Registry

http://www.themmrf.org/research-programs/commpass-study/

A personalized medicine initiative to discover biomarkers that can better define the biological basis of multiple myeloma to help stratify patients. This effort hopes to obtain samples from approximately 1,000 multiple myeloma patients and follow them over time to identify how a patient's genetic profile is related to clinical progression and treatment response. As a partnership between 17 academic centers, 5 pharmaceuticals and the Department of Veterans Affairs, the goal of this eight year study is to create a database that can accelerate future clinical trials and personalized treatment strategies. MMRF's CoMMpass Study has the following goals: * Create a guide to which treatments work best for specific patient subgroups. * Share data with researchers to accelerate drug development for specific subtypes of multiple myeloma patients. In order to facilitate discoveries and development related to targeted therapies, the comprehensive data from CoMMpass is placed in an open-access research portal. The data will be part of the Multiple Myeloma Research Foundation's (MMRF) Personalized Medicine Platform combines CoMMpass data with those collected from MMRF's Genomics Initiative. It is hoped that the longitudinal data, combined with the annotated bio-specimens will help provide insights that can accelerate personalized therapies.

Proper citation: MMRF CoMMpass Study (RRID:SCR_003721) Copy   

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http://knightadrc.wustl.edu/

The Charles F. and Joanne Knight Alzheimer Disease Research Center (Knight ADRC) supports researchers and our surrounding community in their pursuit of answers that will lead to improved diagnosis and care for persons with Alzheimer disease (AD). The Center is committed to the long-term goal of finding a way to effectively treat and prevent AD. The Knight ADRC facilitates advanced research on the clinical, genetic, neuropathological, neuroanatomical, biomedical, psychosocial, and neuropsychological aspects of Alzheimer disease, as well as other related brain disorders.

Proper citation: Washington University School of Medicine Knight Alzheimers Disease Research Center (RRID:SCR_000210) Copy   

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http://www.genome.jp/kegg/expression/

Database for mapping gene expression profiles to pathways and genomes. Repository of microarray gene expression profile data for Synechocystis PCC6803 (syn), Bacillus subtilis (bsu), Escherichia coli W3110 (ecj), Anabaena PCC7120 (ana), and other species contributed by the Japanese research community.

Proper citation: Kyoto Encyclopedia of Genes and Genomes Expression Database (RRID:SCR_001120) Copy   

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http://www.bioconductor.org/packages/release/bioc/html/CGEN.html

Software R package for analysis of case-control studies in genetic epidemiology.

Proper citation: CGEN (RRID:SCR_001251) Copy   

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http://med.stanford.edu/tanglab/software/saber.html

Software program suitable for genome-scale data which uses a Markov-hidden Markov model (MHMM) to estimate local ancestry. The MHMM makes it possible to identify genomic blocks of a particular ancestry by use of any high-density single-nucleotide-polymorphism panel. One application is to perform admixture mapping without genotyping special ancestry-informative-marker panels.

Proper citation: SABER (RRID:SCR_001257) Copy   

•   Source: SciCrunch Registry