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JAIL is a database that classifies the interfaces between domain architectures and those between protein chains and between proteins and nucleic acids. Interacting proteins are difficult to crystallize and rarely present within the Protein Data Base. Nevertheless, it is essential to analyze the interacting parts of the proteins to understand the process of protein-protein docking. To overcome this problem we have built up the JAIL database. Since interacting domains exhibit similar structural features than proteins, all known interfaces between interacting domains of the SCOP database were extracted and classified in JAIL. Only a part of all protein structures are included in SCOP. Particularly, new PDB entries are not yet annotated. To overcome this problem additionally all interfaces between protein chains were calculated and included in the database. This type of interface also comprises the interacting parts of the assumed biological units. The last important type of interfaces provided here is composed of the interacting parts between proteins and nucleic acids. Overall the data set consists of about 180,000 interfaces. JAIL is a comfortable tool to browse through the interface library and to analyze single interfaces. However, more general questions require large-scale analysis. For this purpose, a detailed form enables the compiling of comprehensive non redundant data sets for download.

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• SciCrunch
• 17 years ago - by Anonymous

How is association mapping going to help me find genes? During the past decade, the genes for a large number of rare mendelian traits have been identified. However, traditional linkage analyses lack power and precision when applied to complex disease. Association mapping, which compares the effects of different chromosomal variants, may be more successful at identifying genes of small effect. How does QTDT help association mapping? Association mapping can produce misleading results when the study population is not homogeneous, but includes individuals with different genetic backgrounds. Family based association tests, commonly referred to as TDTs (Transmission Disequilibrium Tests), do not produce misleading results in these circumstances. QTDT can use all the information in a pedigree to construct powerful tests of association that are robust in the presence of stratification. What does the Q stant for ? Q stands for Quantitative. Quantitative traits provide effective descriptions of many complex diseases, including asthma. For many of these conditions, all or nothing definitions of disease are arbitrary and unsatisfactory. QTDT incorporates variance components methodology in the analysis of family data and includes exact estimation of p-values for analysis of small samples and non-normal data. The QTDT abbreviation (for Quantitative Transmission Disequilibrium Tests) was first used by David Allison in his 1997 paper. This research was supported in part by the intramural program of the National Eye Institute and by National Institutes of Health Grants EY016862, EY007758, EY09859, EY012118, P30-EY014801, EY-014458, EY014467, HL084729, and HG002651, by the Foundation Fighting Blindness, the Macula Vision Research Foundation, the American Health Assistance Foundation, Research to Prevent Blindness, the Pew Charitable Trusts, the Mayo Clinic Foundation, the Casey Macular Degeneration Center Fund, the Marion W. and Edward F. Knight AMD Fund, the Harold and Pauline Price Foundation, National Genotyping Centre of Spain, and the Elmer and Sylvia Sramek Foundation. The Center for Inherited Disease Research, fully funded through a federal contract (HHSN268200782096C) from National Institutes of Health to

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• SciCrunch
• 17 years ago - by Anonymous

THIS RESOURCE IS NO LONGER IN SERVICE, documented August 23, 2016. EPGD isfocused on the paralogs and the duplication events in the evolution. It is gene-centered and organized by paralog family. The paralog families and paralogons can be searched by text or sequence, and are downloadable from the website in plain text files. The database will be very useful for both experimentalists and bioinformaticians for the study of duplication events or paralog families.

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• SciCrunch
• 17 years ago - by Anonymous

The GPCR NaVa database describes sequence variants within the family of human G Protein-Coupled Receptors (GPCRs). GPCRs regulate many physiological functions and are the targets for most of today's medicines. The acronym NaVa stands for Natural Variant, which means any (non-artificial) variant that occurs in humans. The GPCR NaVa database includes: 1) rare mutations (frequency < 1%); 2) polymorphisms (frequency > 1%), including Single Nucleotide Polymorphisms (SNPs); 3) variants without estimates of allele frequency. The GPCR NaVa database aids GPCR research by categorising and integrating information on variants from databases and scientific papers. Moreover, the GPCR NaVa database is linked with the reputable GPCRDB. In case of missing NaVas, please help our users by sending a completed Excel-file to Jeroen Kazius. human G Protein-Coupled Receptors, human G Protein, human G-protein

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• SciCrunch
• 17 years ago - by Anonymous

Web server for identification of complete gene structures in genomic DNA.Tool for predicting locations and exon-intron structures of genes in genomic sequences from variety of organisms. Used for prediction of complete gene structures in human genomic DNA.

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• SciCrunch
• 13 years ago - by Anonymous

Windows(c) desktop software application, customizable and standalone, that facilitates the biological interpretation of gene lists derived from the results of microarray, proteomic, and SAGE experiments. Provides statistical methods for discovering enriched biological themes within gene lists, generates gene annotation tables, and enables automated linking to online analysis tools. Offers statistical models to deal with multi-test comparison problem. Platform: Windows compatible

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• SciCrunch
• 15 years ago - by Anonymous

Software program that performs exact linkage analysis with the same input-output relationships as in standard genetic linkage programs such as LINKAGE, FASTLINK, VITESSE, but can run larger files than previous programs. (entry from Genetic Analysis Software)

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• SciCrunch
• 14 years ago - by Anonymous

The operational criteria OPCRIT checklist for psychotic and affective illness has been designed to facilitate a polydiagnostic approach to mental illness. The package is specifically for the needs of the researcher and is intended to be used by clinicians or investigators trained in clinical research. It is not recommended for use by raters without previous experience in psychopathology and psychiatric diagnosis. Click the Download link below to download Opcrit. This download is compressed zip file which will yield the actual installer files. Open WinopInstallerFiles.zip and when prompted extract the contents to a temporary location. One of the files extracted is Setup.exe. Run this setup file and follow the instructions to install the Opcrit. Depending on your system type and version of Windows, you may need to reboot your system once installation is complete. After installation, you will also need to download and run the installer for the october 2009 update (see below).

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• SciCrunch
• 17 years ago - by Anonymous

The Mouse Brain Image/Atlas Visualizer (MBIV) is a Java-based 2D visualization tool, available both as a web-based applet or downloadable application, for browsing high resolution 3D MRI mouse brain images and their associated atlases. A user can dynamically access images and atlases in our mouse brain database through the interface provided by MBIV and then select from the database the datasets they wish to upload and browse on their local machine.

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• SciCrunch
• 16 years ago - by Anonymous

A method for detecting DNA copy number variation (CNV) using high-throughput sequencing., THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 16,2025.

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• SciCrunch
• 13 years ago - by Anonymous

THIS RESOURCE IS NO LONGER IN SERVCE, documented June, 2019.Comparative Genomic Visualization with Adobe Flash.

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• SciCrunch
• 13 years ago - by Anonymous

A data set of a multicohort study of persons 70 years of age and over designed primarily to measure changes in the health, functional status, living arrangements, and health services utilization of two cohorts of Americans as they move into and through the oldest ages. The project is comprised of four surveys: * The 1984 Supplement on Aging (SOA) * The 1984-1990 Longitudinal Study of Aging (LSOA) * The 1994 Second Supplement on Aging (SOA II) * The 1994-2000 Second Longitudinal Study of Aging (LSOA II) The surveys, administered by the U.S. Census Bureau, provide a mechanism for monitoring the impact of proposed changes in Medicare and Medicaid and the accelerating shift toward managed care on the health status of the elderly and their patterns of health care utilization. SOA and SOA II were conducted as part of the in-person National Health Interview Survey (NHIS) of noninstitutionalized elderly people aged 55 years and over living in the United States in 1984, and at least 70 years of age in 1994, respectively. The 1984 SOA served as the baseline for the LSOA, which followed all persons who were 70 years of age and over in 1984 through three follow-up waves, conducted by telephone in 1986, 1988, and 1990. The SOA covered housing characteristics, family structure and living arrangements, relationships and social contracts, use of community services, occupation and retirement (income sources), health conditions and impairments, functional status, assistance with basic activities, utilization of health services, nursing home stays, and health opinions. Most of the questions from the SOA were repeated in the SOA II. Topics new to the SOA II included use of assistive devices and medical implants; health conditions and impairments; health behaviors; transportation; functional status, assistance with basic activities, unmet needs; utilization of health services; and nursing home stays. The major focus of the LSOA follow-up interviews was on functional status and changes that had occurred between interviews. Information was also collected on housing and living arrangements, contact with children, utilization of health services and nursing home stays, health insurance coverage, and income. LSOA II also included items on cognitive functioning, income and assets, family and childhood health, and more extensive health insurance information. The interview data are augmented by linkage to Medicare enrollment and utilization records, the National Death Index, and multiple cause-of-death records. Data Availability: Copies of the LSOA CD-ROMs are available through the NCHS or through ICPSR as Study number 8719. * Dates of Study: 1984-2000 * Study Features: Longitudinal * Sample Size: ** 1984: 16,148 (55+, SOA) ** 1984: 7,541(70+, LSOA) ** 1986: 5,151 (LSOA followup 1) ** 1988: 6,921 (LSOA followup 2) ** 1990: 5,978 (LSOA followup 3) ** 1994-6: 9,447 (LSOA II baseline) ** 1997-8: 7,998 (LSOA II wave 2) ** 1999-0: 6,465 (LSOA II wave 3) Link: * LSOA 1984-1990 ICPSR: http://www.icpsr.umich.edu/icpsrweb/ICPSR/studies/08719

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• SciCrunch
• 14 years ago - by Anonymous

The FlyTrap database presents the current results of large scale protein trapping screens that provide both information on which cells express each tagged gene, and subcellular localization of GFP-tagged proteins. Expression is under the control of endogenous promoter and enhancer elements, allowing for visualization of normal expression patterns. Drosophila proteins tagged with Green Fluorescent Protein (GFP) were created by insertion into genes of an artificial exon encoding GFP flanked by splice acceptor (SA) and splice donor (SD) sequences so that expression of GFP relies on splicing into mature mRNAs and in-frame fusion.

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• SciCrunch
• 17 years ago - by Anonymous

On line database of alu pairs. This map file is a subfile of repbase (Genetic Information Research Institute(GIRI)), derived by comparing genomic sequences in the GenBank database (release 112.0, National center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD) with the Alu consensus sequence, was made in April 1999 and is maintained at the GIRI), Sunnyvale, CA). The Alu map includes the following information in columnar form: locus, beginning sequence position, ending sequence position, fragment start relative to repeat consensus, type of Alu sequence, the fragment start relative to repeat consensus, the fragment end relative to repeat consensus, the orientation of the sequence (D, denoting direct, versus C, denoting complementary), the percent similarity to the Alu consensus sequence, the ratio of mismatches to matches, and the alignment score. The database is available for download. An Alu element is a short stretch of DNA originally characterized by the action of the Alu (Arthrobacter luteus) restriction endonuclease. Alu elements of different kinds occur in large numbers in primate genomes. In fact, Alu elements are the most abundant Transposable elements in the human genome. They are derived from the small cytoplasmic 7SL RNA, a component of the signal recognition particle. The event, when a copy of the 7SL RNA became a precursor of the Alu elements, took place in the genome of an ancestor of Supraprimates. (from Wikipedia).

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• SciCrunch
• 16 years ago - by Anonymous

A web server designed to provide a total solution to analyze small RNAs sequencing data generated by SOLEXA., THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 16,2025.

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• SciCrunch
• 13 years ago - by Anonymous

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)

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• SciCrunch
• 14 years ago - by Anonymous

THIS RESOURCE IS NO LONGER IN SERVICE, documented August 19, 2016. A resource that allows users to identify cis-NATs in eleven eukaryotic species, screening eight of these species for the first time and bringing the number of candidate SA pairs in human to 7,246. We construct this free and publicly accessible database that allows researchers to query the dataset.

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• SciCrunch
• 17 years ago - by Anonymous

Database platform of an integrated view of eight databases (mouse gene expression resources: EMAGE, GXD, GENSAT, BioGPS, ABA, EUREXPRESS; human gene expression databases: HUDSEN, BioGPS and Human Protein Atlas) that allows the experimentalist to retrieve relevant statistical information relating gene expression, anatomical structure (space) and developmental stage (time). Moreover, general biological information from databases such as KEGG, OMIM and MTB is integrated too. It can be queried using gene and anatomical structure. Output information is presented in a friendly format, allowing the user to display expression maps and correlation matrices for a gene or structure during development. An in-depth study of a specific developmental stage is also possible using heatmaps that relate gene expression with anatomical components. This is a powerful tool in the gene expression field that makes easy the access to information related to the anatomical pattern of gene expression in human and mouse, so that it can complement many functional genomics studies. The platform allows the integration of gene expression data with spatial-temporal anatomic data by means of an intuitive and user friendly display., THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 16,2025.

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• SciCrunch
• 14 years ago - by Anonymous

A database of cognate ligands for the domains of enzyme structures in CATH, SCOP and Pfam. The database contains an assignment of PDB ligands to the domains of structures as classified by the CATH, SCOP and Pfam databases. Cognate ligands have been identified using data from the ENZYME and KEGG databases and compared to the PDB ligand using graph matching to assess chemical similarity. Cognate ligands from the known reactions in ENZYME and KEGG for a particular enzyme are then assigned to enzymes structures which have EC numbers.

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• SciCrunch
• 17 years ago - by Anonymous

An R package for simulations and likelihood calculations of pair-wise family relationships using DNA marker data. (entry from Genetic Analysis Software)

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• SciCrunch
• 14 years ago - by Anonymous