Tools   Select Another Resource Report Type

http://chromium.lovd.nl/LOVD2/home.php?select_db=CDKN2A

THIS RESOURCE IS NO LONGER IN SERVICE, documented August 23, 2016. The CDKN2A Database presents the germline and somatic variants of the CDKN2A tumor suppressor gene recorded in human disease through June 2003, annotated with evolutionary, structural, and functional information, in a format that allows the user to either download it or manipulate it for their purposes online. The goal is to provide a database that can be used as a resource by researchers and geneticists and that aids in the interpretation of CDKN2A missense variants. Most online mutation databases present flat files that cannot be manipulated, are often incomplete, and have varying degrees of annotation that may or may not help to interpret the data. They hope to use CDKN2A as a prototype for integrating computational and laboratory data to help interpret variants in other cancer-related genes and other single nucleotide polymorphisms (SNPs) found throughout the genome. Another goal of the lab is to interpret the functional and disease significance of missense variants in cancer susceptibility genes. Eventually, these results will be relevant to the interpretation of single nucleotide polymorphisms (SNPs) in general. The CDKN2A locus is a valuable model for assessing relationships among variation, structure, function, and disease because: Variants of this gene are associated with hereditary cancer: Familial Melanoma (and related syndromes); somatic alterations play a role in carcinogenesis; allelic variants occur whose functional consequences are unknown; reliable functional assays exist; and crystal structure is known. All variants in the database are recorded according to the nomenclature guidelines as outlined by the Human Genome Variation Society. This database is currently designed for research purposes only and is not yet recommended as a clinical resource. Many of the mutations reported here have not been tested for disease association and may represent normal, non-disease causing polymorphisms.

Proper citation: CDKN2A Database (RRID:SCR_008179) Copy   

•   Source: SciCrunch Registry

http://www.schematikon.org/Nh3D.html

THIS RESOURCE IS NO LONGER IN SERVICE, documented on July 17, 2013. It is freely available as a reference dataset for the statistical analysis of sequence and structure features of proteins in the PDB. It is a dataset of structurally dissimilar proteins. This dataset has been compiled by selecting well resolved representatives from the Topology level of the CATH database which hierarchically classifies all protein structures. These have been been pruned to remove: i) domains that may contain homologous elements (by pairwise sequence comparison and structural superposition of aligned residues) ii) internal duplications (by repeat detection) iii) regions with high B-Factor The statistical analysis of protein structures requires datasets in which structural features can be considered independently distributed, i.e. not related through common ancestry, and that fulfill minimal requirements regarding the experimental quality of the structures it contains. However, non-redundant datasets based on sequence similarity invariably contain distantly related homologues. Here a reference dataset of non-homologous protein domains is provided, assuming that structural dissimilarity at the topology level is incompatible with recognizable common ancestry. It contains the best refined representatives of each Topology level, validates structural dissimilarity and removes internally duplicated fragments. The compilation of Nh3D is fully scripted. The current Nh3D list contains 570 domains with a total of 90780 residues. It covers more than 70% of folds at the Topology level of the CATH database and represents more than 90% of the structures in the PDB that have been classified by CATH. Even though all protein pairs are structurally dissimilar, some pairwise sequence identities after global alignment are greater than 30%. Nh3D is freely available as a reference dataset for the statistical analysis of sequence and structure features of proteins in the PDB.

Proper citation: Nh3D: A Reference Dataset of Structures of Non-homologous Proteins (RRID:SCR_008212) Copy   

•   Source: SciCrunch Registry

http://dtp.nci.nih.gov/docs/3d_database/dis3d.html

The NCI DIS 3D database is a collection of 3D structures for over 400,000 drugs. The database is an extension of the NCI Drug Information System. The structural information stored in the DIS is only the connection table for each drug. The connection table is just a list of which atoms are connected and how they are connected. It is essentially a searcheable database of three-dimensional structures has been developed from the chemistry database of the NCI Drug Information System (DIS), a file of about 450,000 primarily organic compounds which have been tested by NCI for anticancer activity. The DIS database is very similar in size and content to the proprietary databases used in the pharmaceutical industry; its development began in the 1950s; and this history led to a number of problems in the generation of 3D structures. This information can be searched to find drugs that share similar patterns of connections, which can correlate with similar biological activity. But the cellular targets for drug action, as well as the drugs themselves, are 3 dimensional objects and advances in computer hardware and software have reached the point where they can be represented as such. In many cases the important points of interaction between a drug and its target can be represented by a 3D arrangement of a small number of atoms. Such a group of atoms is called a pharmacophore. The pharmacophore can be used to search 3D databases and drugs that match the pharmacophore could have similar biological activity, but have very different patterns of atomic connections. Having a diverse set of lead compounds increases the chances of finding an active compound with acceptable properties for clinical development. Sponsor: The ICBG are supported by the Cooperative Agreement mechanism, with funds from nine components of the NIH, the National Science Foundation, and the Foreign Agricultural Service of the USDA.

Proper citation: National Cancer Institute 3D Structure Database (RRID:SCR_008211) Copy   

•   Source: SciCrunch Registry

http://databases.unesco.org/bioethics/biowebintro.shtml

Bioethics database comprises over 645 bioethics institutions (bioethics committees, commissions, training, research and documentation centres) in over 80 countries, including information on activities and publications. Information is based on replies obtained from a widely distributed questionnaire and has been gathered in cooperation with National Commissions and Permanent Delegations to UNESCO. The Program develops four main action areas: -Intellectual forum -Standard-setting action -Advisory role and capacity-building -Education and awareness raising The Bioethics Program is part of UNESCOs Division of the Ethics of Science and Technology in the Social and Human Sciences Sector. It is primarily responsible for the Secretariat of two advisory bodies: the International Bioethics Committee (IBC), composed of 36 independent experts, and the Intergovernmental Bioethics Committee (IGBC), composed of representatives of 36 Member States. These Committees cooperate to produce advice, recommendations and proposals that each submits to the Director-General for consideration by UNESCOs governing bodies.

Proper citation: Bioethics Institutes Database (RRID:SCR_008173) Copy   

•   Source: SciCrunch Registry

http://openlibrary.org/

Open Library is a project of the non-profit Internet Archive, and is funded in part by a grant from the California State Library. They have a small team of fantastic programmers who have accomplished a lot, but we can''t do it alone! This is an Open project - the software is open, the data is open, the documentation is open, and the site is open. To build it, they need hundreds of millions of book records, a brand new database infrastructure for handling huge amounts of dynamic information, a wiki interface, multi-language support, and people who are willing to contribute their time, effort, and book data. To date, they have gathered about 30 million records (20 million are available through the site now), and more are on the way. They have built the database infrastructure and the wiki interface, and you can search millions of book records, narrow results by facet, and search across the full text of 1 million scanned books. Sponsors: Open Library is funded by a grant from the California State Library.

Proper citation: Open Library (RRID:SCR_008295) Copy   

•   Source: SciCrunch Registry

http://www3.isrl.uiuc.edu/~TeleNature/bibe/

THIS RESOURCE IS NO LONGER IN SERVICE, documented on July 15, 2013. A facility to help novices and experts find information about plants and animals in digital collections. The objectives of the Project are to facilitate access to online flora and fauna by both novices and experts through enhanced indexing, searching, and visualization techniques. Specific search facility and content will be added to help users with different levels of domain knowledge identify species based on the augmentation of professionally developed taxonomic treatments or species descriptions. This is a novel use of taxonomic descriptions.

Proper citation: Biological Information Browsing Environment (RRID:SCR_008170) Copy   

•   Source: SciCrunch Registry

http://amazonia.montp.inserm.fr/

A web interface and associated tools for easy query of public human transcriptome data by keyword, through thematic pages with list annotations. Amazonia provides a thematic entry to public transcriptomes: users may for instance query a gene on a Stem Cells page, where they will see the expression of their favorite gene across selected microarray experiments related to stem cell biology. This selection of samples can be customized at will among the 6331 samples currently present in the database. Every transcriptome study results in the identification of lists of genes relevant to a given biological condition. In order to include this valuable information in any new query in the Amazonia database, they indicate for each gene in which lists it is included. This is a straightforward and efficient way to synthesize hundreds of microarray publications., THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 16,2025.

Proper citation: AmaZonia: Explore the Jungle of Microarrays Results (RRID:SCR_008405) Copy   

•   Source: SciCrunch Registry

http://dcv.uhnres.utoronto.ca/SCRIPDB/search/

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 2, 2025. Database of chemicals and reactions inside of US patents (2001 - 2011). SCRIPDB provides the full original patent text, reactions and relationships described within any individual patent, in addition to the molecular files common to structural databases. The patent literature is a rich catalog of biologically relevant chemicals; many public and commercial molecular databases contain the structures disclosed in patent claims. However, patents are an equally rich source of metadata about bioactive molecules, including mechanism of action, disease class, homologous experimental series, structural alternatives, or the synthetic pathways used to produce molecules of interest. Unfortunately, this metadata is discarded when chemical structures are deposited separately in databases. SCRIPDB is a chemical structure database designed to make this metadata accessible. The SCRIPDB information is valuable in medical text mining, chemical image analysis, reaction extraction and in silico pharmaceutical lead optimization. SCRIPDB may be searched by exact chemical structure, substructure or molecular similarity and the results may be restricted to patents describing synthetic routes.

Proper citation: SCRIPDB (RRID:SCR_008922) Copy   

•   Source: SciCrunch Registry

http://www.ebi.ac.uk/asd/altextron/indexhtml

THIS RESOURCE IS NO LONGER IN SERVICE. A computer generated high quality dataset of human transcript-confirmed constitutive and alternative exons and introns. The alternative events have been delineated and annotated with various characterizations. AltExtron is the prototype database for the production version AltSplice. AltExtron is more geared towards investigating various aspects of the methodologies used, and focuses in general on the biology behind alternative splicing. The complete data used in this work is available for downloading in several flat files, containing human genes, introns, exons, isoform events, human-mouse comparisons, and additional information on GC-AG introns. Two versions of AltExtron data are available - one as prototype (for human) and another as latest build (for human, drosophila, mouse, and others) based on EMBL/GenBank (Feb 2003).

Proper citation: AltExtron Database (RRID:SCR_008404) Copy   

•   Source: SciCrunch Registry

http://www.gavialliance.org/

Although Haemophilus influenza type b (Hib) diseases and Hepatitis B (Hep B) infections are preventable with one combined life-saving vaccine, both continue to pose risks to the worlds most vulnerable populations, leading to life-long disabilities or even death. Vaccinating against Hib and Hepatitis B represents an essential step towards reaching Millennium Development Goal 4. The Hib bacterium causes meningitis and pneumonia and is considered the third vaccine-preventable cause of death in children aged under five. It is estimated that there are three million cases of serious Hib infection annually, of which 400,000 result in childhood death. The majority of survivors suffer paralysis, deafness, mental retardation and learning disabilities. Babies and young children are most at risk from Hep B, a viral disease, which attacks the liver and can cause both acute and chronic disease. This can lead to chronic liver disease and puts victims at high risk of death from cirrhosis of the liver and liver cancer in later life. More than two billion people are infected by Hep B worldwide of whom 360 million suffer from chronic Hep B infection; the latter is highly prevalent in all countries that GAVI supports. Children are most vulnerable to infection with 90 percent of infants infected in the first months of their lives developing chronic Hep B infection. Infections in the developing world are mostly from mother to child, or from child to child, mainly through cuts, bites, scrapes and scratches. Vaccinating against Hib and Hep B represents an essential step towards reaching Millennium Development Goal 4, which is to reduce the under-five mortality rate by two thirds by 2015. GAVI uses two mechanisms that draw heavily on private-sector thinking to help overcome historic limitations to development funding for immunisation. These mechanisms are the AMC and the IFFIm. The former reflects the need to meet disproportionately high costs in the early stages of implementing aid programmes; the latter developing countries'' need for sustainable predictable funding., THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 16,2025.

Proper citation: GAVI (RRID:SCR_008528) Copy   

•   Source: SciCrunch Registry

http://www.lamhdi.org/

THIS RESOURCE IS NO LONGER IN SERVICE, it has been replaced by Monarch Initiative. LAMHDI, the initiative to Link Animal Models to Human DIsease, is designed to accelerate the research process by providing biomedical researchers with a simple, comprehensive Web-based resource to find the best animal model for their research. LAMDHI is a free, Web-based, resource to help researchers bridge the gap between bench testing and human trials. It provides a free, unbiased resource that enables scientists to quickly find the best animal models for their research studies. LAMHDI includes mouse data from MGI, the Mouse Genome Informatics website; zebrafish data from ZFIN, the Zebrafish Model Organism Database; rat data from RGD, the Rat Genome Database; yeast data from SGD, the Saccharomyces Genome Database; and fly data from FlyBase. LAMHDI.org is operational today, and data is added regularly. Enhancements are planned to let researchers contribute their knowledge of the animal models available through LAMHDI. The LAMHDI goal is to allow researchers to share information about and access to animal models so they can refine research and testing, and reduce or replace the use of animal models where possible. LAMHDI Database Search: LAMHDI brings together scientifically validated information from various sources to create a composite multi-species database of animal models of human disease. To do this, the LAMHDI database is prepared from a variety of sources. The LAMHDI team takes publicly available data from OMIM, NCBI''s Entrez Gene database, Homologene, and WikiPathways, and builds a mathematical graph (think of it as a map or a web) that links these data together. OMIM is used to link human diseases with specific human genes, and Entrez provides universal identifiers for each of those genes. Human genes are linked to their counterpart genes in other species with Homologene, and those genes are linked to other genes tentatively or authoritatively using the data in WikiPathways. This preparatory work gives LAMHDI a web of human diseases linked to specific human genes, orthologous human genes, homologous genes in other species, and both human and non-human genes involved in specific metabolic pathways associated with those diseases. LAMHDI includes model data that partners provide directly from their data structures. For instance, MGI provides information about mouse models, including a disease for each model, as well as some genetic information (the ID of the model, in fact, identifies one or more genes). ZFIN provides genetic information for each zebrafish model, but no diseases, so zebrafish models are integrated by using the genes as the glue. For instance, a zebrafish model built to feature the zebrafish PKD2 gene would plug into the larger disease-gene map at the node representing the zebrafish PKD2 gene, which is connected to the node representing the human PKD2 gene, which in turn is connected to the node representing the human disease known as polycystic kidney disease. (Some of the partner data LAMHDI receives can even extend the base map. MGI provides a disease for every model, and in some cases this allows the creation of a disease-to-gene relationship in the LAMHDI database that might not already be documented in the OMIM dataset.) With curatorial and model information in hand, LAMHDI runs a lengthy automated process that exhaustively searches for every possible path between each model and each disease in the data, up to a set number of hops, producing for each disease-to-model pair a set of links from the disease to the model. The algorithm avoids circular paths and paths that include more than one disease anywhere in the middle of the path. At the end of this phase, LAMHDI has a comprehensive set of paths representing all the disease-to-model relationships in the data, varying in length from one hop to many hops. Each disease-to-model path is essentially a string of nodes in the data, where each node represents a disease, a gene, a linkage between genes (an orthologue, a homologue, or a pathway connection, referred to as a gene cluster or association), or a model. Each node has a human-friendly label, a set of terms and keywords, and - in most cases - a URL linking the node to the data source where it originated. When a researcher submits a search on the LAMHDI website, LAMHDI searches for the user''s search terms in its precomputed list of all known disease-to-model paths. It looks for the terms not only in the disease and model nodes, but also in every node along each path. The complete set of hits may include multiple paths between any given disease-to-model pair of endpoints. Each of these disease-to-model pair sets is ordered by the number of hops it involves, and the one involving the fewest hops is chosen to represent its respective disease-to-model pair in the search results presented to the user. Results are sorted by scores that represent their matches. The number of hops is one barometer of the strength of the evidence linking the model and the disease; fewer hops indicates the relationship is stronger, more hops indicates it may be weaker. This indicator works best for comparing models from a single partner dataset: MGI explicitly identifies a disease for each mouse model, so there can be disease-to-model hits for mice that involve just one hop. Because ZFIN does not explicitly identify a disease for each model, no zebrafish model will involve fewer than four hops to the nearest disease, from the zebrafish model to a zebrafish gene to a gene cluster to a human gene to a human disease.

Proper citation: LAMHDI: The Initiative to Link Animal Models to Human DIsease (RRID:SCR_008643) Copy   

•   Source: SciCrunch Registry

https://www.i2b2.org/NLP/DataSets/Main.php

The data for the smoking challenge consisted exclusively of discharge summaries from Partners HealthCare which were preprocessed and converted into XML format, and separated into training and test sets. I2B2 is a data warehouse containing clinical data on over 150k patients, including outpatient DX, lab results, medications, and inpatient procedures. ETL processes authored to pull data from EMR and finance systems Institutional review boards of Partners HealthCare approved the challenge and the data preparation process. The data were annotated by pulmonologists and classified patients into Past Smokers, Current Smokers, Smokers, Non-smokers, and unknown. Second-hand smokers were considered non-smokers. Other institutions involved include Massachusetts Institute of Technology, and the State University of New York at Albany. i2b2 is a passionate advocate for the potential of existing clinical information to yield insights that can directly impact healthcare improvement. In our many use cases (Driving Biology Projects) it has become increasingly obvious that the value locked in unstructured text is essential to the success of our mission. In order to enhance the ability of natural language processing (NLP) tools to prise increasingly fine grained information from clinical records, i2b2 has previously provided sets of fully deidentified notes from the Research Patient Data Repository at Partners HealthCare for a series of NLP Challenges organized by Dr. Ozlem Uzuner. We are pleased to now make those notes available to the community for general research purposes. At this time we are releasing the notes (~1,000) from the first i2b2 Challenge as i2b2 NLP Research Data Set #1. A similar set of notes from the Second i2b2 Challenge will be released on the one year anniversary of that Challenge (November, 2010).

Proper citation: Smoking NLP Challenge Data (RRID:SCR_008644) Copy   

•   Source: SciCrunch Registry

http://dnatraffic.ibb.waw.pl/

DNAtraffic database is dedicated to be an unique comprehensive and richly annotated database of genome dynamics during the cell life. DNAtraffic contains extensive data on the nomenclature, ontology, structure and function of proteins related to control of the DNA integrity mechanisms such as chromatin remodeling, DNA repair and damage response pathways from eight model organisms commonly used in the DNA-related study: Homo sapiens, Mus musculus, Drosophila melanogaster, Caenorhabditis elegans, Saccharomyces cerevisiae, Schizosaccharomyces pombe, Escherichia coli and Arabidopsis thaliana. DNAtraffic contains comprehensive information on diseases related to the assembled human proteins. Database is richly annotated in the systemic information on the nomenclature, chemistry and structure of the DNA damage and drugs targeting nucleic acids and/or proteins involved in the maintenance of genome stability. One of the DNAtraffic database aim is to create the first platform of the combinatorial complexity of DNA metabolism pathway analysis. Database includes illustrations of pathway, damage, protein and drug. Since DNAtraffic is designed to cover a broad spectrum of scientific disciplines it has to be extensively linked to numerous external data sources. Database represents the result of the manual annotation work aimed at making the DNAtraffic database much more useful for a wide range of systems biology applications. DNAtraffic database is freely available and can be queried by the name of DNA network process, DNA damage, protein, disease, and drug.

Proper citation: DNAtraffic (RRID:SCR_008886) Copy   

•   Source: SciCrunch Registry

http://www.ebi.ac.uk/msd-srv/ssm/

Secondary Structure Matching (SSM) is an interactive service for comparing protein structures in 3D. SSM compares to other protein matching services, see results here. It is used as a structure search engine in PISA service (Protein Interfaces, Surfaces and Assemblies). It queries may be launched from any web site, see instructions here and it is based on the CCP4 Coordinate Library, found here. The service provides for: -pairwise comparison and 3D alignment of protein structures -multiple comparison and 3D alignment of protein structures -examination of a protein structure for similarity with the whole PDB or SCOP archives -best Ca-alignment of compared structures -download and visualization of best-superposed structures using Rasmol (Unix/Linux platforms), Rastop (MS Windows machines) and Jmol (platform-independent server-side java viewer) -linking the results to other services - PDBe Motif, OCA, SCOP, GeneCensus, FSSP, 3Dee, CATH, PDBSum, SWISS-PROT and ProtoMap. Sponsors: The project is funded by the Collaborative Computational Project Number 4 in Protein Crystallography of the Biotechnology and Biological Sciences Research Council

Proper citation: Secondary Structure Matching (RRID:SCR_008365) Copy   

•   Source: SciCrunch Registry

http://www.diabetesgenes.org

Our genes and lifestyle factors, such as calorie rich diets and a lack of exercise, contribute to the development of Type 2 diabetes. But what we dont know is the exact nature of the genetic risk and how this interacts with lifestyle factors to cause diabetes. The Diabetes UK Warren 2 Group was formed in 1992 to investigate the genetic basis of Type 2 diabetes. The Group, comprising of researchers from six UK diabetes research centres, began by recruiting families into the study enriched for Type 2 diabetes ie having two or more siblings with the condition. With over 2000 individuals from 843 families in the collection, it is now being used to search for the genes that make people susceptible to Type 2 diabetes. When identifying susceptibility genes it is important to know how the gene affects normal metabolism in relatives without diabetes. In 1999. the Warren 2 Extension study recruited first degree relatives (siblings and children) of the original Warren 2 families, who did not have diabetes, in the knowledge that they would also be enriched with the same susceptibility genes. This study recruited 811 relatives without diabetes (586 offspring and 225 siblings) all of whom have undergone detailed metabolic assessment. A similar study was undertaken in Oxford called the Diabetes In Families (DIF) Study. In 2001, the Warren 2 Trios and Duos Study recruited 500 families from around the country consisting of an individual with Type 2 diabetes and both their parents (trios) or an individual with diabetes, one of their parents and at least 2 siblings (duos). In addition to this, a further 1500 individuals with diabetes were recruited as part of the Warren 2 Cases study. This website is run by the Diabetes Research department and the Centre for Molecular Genetics at the Peninsula Medical School and Royal Devon and Exeter Hospital, Exeter, UK.

Proper citation: Diabetes Genes (RRID:SCR_008639) Copy   

•   Source: SciCrunch Registry

http://www.familybuilder.com

Family members connect with our Family Tree application. We have over 178,385,793 relatives added and growing! Familybuilder, the top family destination on social networks, today announced that it has been chosen by AlwaysOn as one of the AlwaysOn East Top 100 winners. Inclusion in the AlwaysOn East 100 signifies leadership amongst its peers and game-changing approaches and technologies that are likely to disrupt existing markets and entrenched players. Familybuilder was specially selected by the AlwaysOn editorial team and industry experts spanning the globe based on a set of five criteria: innovation, market potential, commercialization, stakeholder value, and media buzz. Familybuilder and the AlwaysOn East Top 100 companies will be honored at AlwaysOn''s Venture Summit East event on June 21st, 2010, at Harvard Business School in Boston, MCA. This two-day executive event features CEO presentations and high-level debates that highlight the significant economic, political, and technology trends impacting the global growth investor on the East Coast. Now more than ever, staying abreast of market conditions and innovations is crucial to financial success. After examining the companies that are on the AOE100 list, it''s obvious that innovation is not only alive and well on the East Coast, it''s accelerating in economic power and scope. says Tony Perkins, founder and editor of AlwaysOn. The companies certainly represent some of the highest-growth opportunities in the private company marketplace. The AlwaysOn East 100 winners were selected from among hundreds of other technology companies nominated by investors, bankers, journalists, and industry insiders. The AlwaysOn editorial team conducted a rigorous three-month selection process to finalize the 2010 list. Familybuilder is the first company to offer applications for communicating and staying in touch with family members on social networks including Facebook, MySpace, and others. The Company, with over 26 million users and over 160 million family members added, is quickly becoming one of the most used family services online. The Company''s flagship application on Facebook, Family Tree, is one of the top 15 non-gaming applications on the platform. With its substantial base of customers, Familybuilder is now expanding into new lines of businesses including a newsletter http://familybuilder.com/newsletter as well as subscription functionality. About AlwaysOn AlwaysOn is the leading business media brand networking the Global Silicon Valley. AlwaysOn helped ignite the social media revolution in early 2003 when it launched the AlwaysOn network. In 2004, it became the first media brand to socially network its online readers and event attendees. AlwaysOn''s preeminent executive event series includes the Summit at Stanford, OnMedia, OnHollywood, Venture Summit Mid-Atlantic, OnDemand, Venture Summit Silicon Valley, Venture Summit East, GoingGreen Silicon Valley, GoingGreen East, and GoingGreen Europe. The AlwaysOn network and live event series continue to lead the industry by empowering its readers, event participants, sponsors, and advertisers like no other media brand.

Proper citation: Familybuilder (RRID:SCR_008513) Copy   

•   Source: SciCrunch Registry

http://gcat.davidson.edu/rakarnik/kyte-doolittle.htm

Kyte-Doolittle hydropathy plots give you information about the possible structure of a protein. A hydropathy plot can indicate potential transmembrane or surface regions in proteins i did not find a parent or sponsor

Proper citation: Kyte Doolittle Hydropathy Plots (RRID:SCR_008358) Copy   

•   Source: SciCrunch Registry

http://www.FH-HUS.org

The database has now been updated to include ALL mutations found in HUS patients, including those in Factor I(FI) and Membrane (MCP). Homology models are available for the domains of FI and MCP and all analysis previously available for Factor H (FH) are now also available for FI and MCP. All SNP records for FH, FI and MCP are also now included in the database on the SNP pages. Only those SNPs within coding regions will be included in the full list of mutations and within the advanced search. For more information on the different versions of the database click here. We have also redesigned the site in order to display information more clearly. Please let us know what you think of the new design. Home Information Mutations Models References Links Submit Contact Us Help Collaborators NEWS !! SEP 2009 The database has now been recovered. Please report any bugs that you notice. NEWS !! MAY 2009 We have suffered from a complete server failure this month but these issues have been sorted out and work is being carried out to restore all the data within our FH-HUS database. Sorry for any inconvenience this may have caused. NEWS !! JAN 2007 Mutations within complement Factor B have also been associated with aHUS. (Goicoechea de Jorge et al., 2007) NEW !! Nov 2006 FH-HUS Database Version 2.1 The database has now been updated to include ALL mutations found in HUS patients, including those in Factor I(FI) and Membrane (MCP). Homology models are available for the domains of FI and MCP and all analysis previously available for Factor H (FH) are now also available for FI and MCP. All SNP records for FH, FI and MCP are also now included in the database on the SNP pages. Only those SNPs within coding regions will be included in the full list of mutations and within the advanced search. For more information on the different versions of the database click here. We have also redesigned the site in order to display information more clearly. Please let us know what you think of the new design. Quick Search Enter Codon No : Choose Protein : Advanced Search Have you or someone you know been diagnosed with aHUS? The information contained on this web site is provided for scientific research purposes only. We do not give medical advice or recommend any particular treatment for specific individuals. Here are several links for patient information on aHUS: http://renux.dmed.ed.ac.uk/ http://en.wikipedia.org/ http://kidney.niddk.nih.gov http://www.webmd.com HUS HUS (Haemolytic Uraemic Syndrome) is a disease associated with microangiopathic haemolytic anemia, thrombocytopenia and acute renal failure. A subgroup of the syndrome is strongly associated with abnormalities within the complement regulator factor H gene. To read information on HUS click here. To read information on Factor H (FH) click here. FH Mutations There are currently 74 Factor H mutations, 10 Factor I mutations and 25 MCP mutations linked with HUS patients within this database. There are also 5 mutations within FH that are associated with MPGN patients. . Following HGVS guidelines, mutations are numbered starting from the ATG initiation codon and include the 18-residue signal peptide. The number of the codon with respect to the mature FH protein and consistent with the RSCB PDB entry for secreted FH (1haq.pdb) is shown alongside in parenthesis. Type I and Type II Phenotype Type I indicates that the mutant protein is either absent from the plasma or present in lower amounts. This indicates the mutation has a structural effect on the mutant protein - ie reducing the stability Type II indicates that the mutant protein is present in normal amounts in plasma. This indicates that the mutation has a functional effect on the protein ie affecting substrate binding References There are three references you can use to reference this database Saunders et al, 2007. The interactive Factor H-atypical hemolytic uremic syndrome mutation database and website: update and integration of membrane cofactor protein and Factor I mutations with structural models. Hum Mutat. 2007 28:222-234. Saunders et al, 2006. An interactive web database of factor H-associated hemolytic uremic syndrome mutations: insights into the structural consequences of disease-associated mutations. Hum Mutat. 2006 27:21-30. Saunders & Perkins, 2006. A user''s guide to the interactive Web database of factor H-associated hemolytic uremic syndrome. Semin Thromb Hemost. 2006 32:160-8. Abstract. BACKGROUND: cblC disease is a cause of hemolytic uremic syndrome (HUS), which has been primarily described in neonates and infants with severe renal and neurological lesions. PATIENTS: Two sisters aged 6 and 8.5 years presented with a latent hemolytic process characterized by undetectable or low plasma haptoglobin, respectively, associated with renal failure and gross proteinuria. Renal biopsies performed in both patients found typical findings of thrombotic microangiopathy suggesting the diagnosis of HUS. Both patients were free of neurologic signs. RESULTS: Biochemical investigations found a cobalamin processing deficiency of the cblC type. Search for additional factors susceptible to worsen endothelial damage revealed homozygosity 677C--> T mutation in the methylenetetrahydrofolate reductase gene as well as heterozygosity for a 3254T--> C mutation in factor H in the patient with the most severe clinical presentation. Long-term subcutaneous administration of hydroxocobalamin in combination with oral betaine and folic acid resulted in clinical and biological improvement in both patients. CONCLUSION: cblC disease may be a cause of chronic HUS with delayed onset in childhood. Superimposed mutation of factor H gene might influence clinical severity.

Proper citation: FH HUS Mutation Database (RRID:SCR_008512) Copy   

•   Source: SciCrunch Registry

http://www.zf-models.org/

ZF-MODELS - Zebrafish Models for Human Development and Disease is an Integrated Project funded by the European Commission as part of its Sixth Framework Programme (EC Contract LSHG-CT-2003-503496). The project started on January 1, 2004 and is scheduled to run over a period of five years. The aim of this project is to exploit the advantages of the zebrafish to produce knowledge, technology and materials in the form of disease models, drug targets and insight into pathways of gene regulation applicable to human development and disease.

Proper citation: Zebrafish Models for Human Development and Disease (RRID:SCR_008595) Copy   

•   Source: SciCrunch Registry

http://blaster.docking.org/zinc/

Welcome to ZINC, a free database of commercially-available compounds for virtual screening. ZINC contains over 13 million purchasable compounds in ready-to-dock, 3D formats. ZINC is provided by the Shoichet Laboratory in the Department of Pharmaceutical Chemistry at the University of California, San Francisco (UCSF). To cite ZINC, please reference: Irwin and Shoichet, J. Chem. Inf. Model. 2005;45(1):177-82 PDF, DOI. We thank NIGMS for financial support (GM71896). There are release notes for ZINC 10. - We have a survey where you can give us feedback.

Proper citation: Zinc (RRID:SCR_008596) Copy   

•   Source: SciCrunch Registry