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http://etblast.vbi.vt.edu/argh/index.shtml

ARGH is an automated, accurate and scalable method by which acronym-definition pairs can be identified within text. Its primary advantage is in enabling information processing methods to resolve author-defined acronyms, however it also allows an automated creation of a reference work on acronym definitions.
ARGH has several advantages over manual or semi-automated methods, besides time and effort saved, such as enabling identification of relative frequencies for alternate acronyms and definitions as well as spelling, phrasing and hyphenation variants for a unique acronym-definition pair. It also aids users in identifying acronym/definition variants present in the literature that may not necessarily be in biomedical databases.
ARGH functions by a set of heuristics to accurately locate and identify the boundaries of acronym-definition pairs was developed and refined in terms of precision and recall on subsets of MEDLINE records. These training sets were gradually increased in size and heuristics re-evaluated to ensure scalability.
ARGH can be tested on over 12 million MEDLINE records, and it can identify more than 174,000 unique acronyms and their 737,000 associated definitions. Currently, it is the world''s largest and most comprehensive catalog of biomedical acronyms and abbreviations, containing approximately 257,000 out of an estimated 277,000 unique acronyms within MEDLINE.
Sponsors: ARGH is a service of UT Southwestern eTBLAST team.

Proper citation: ARGH: Biomedical Acronym Resolver (RRID:SCR_008131) Copy   

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http://mips.gsf.de/proj/ppi/

The MIPS mammalian protein-protein interaction database (MPPI) is a new resource of high-quality experimental protein interaction data in mammals. The content is based on published experimental evidence that has been processed by human expert curators. It is a collection of manually curated high-quality PPI data collected from the scientific literature by expert curators. We took great care to include only data from individually performed experiments since they usually provide the most reliable evidence for physical interactions. To suit different users needs we provide a variety of interfaces to search the database: -Expert interface Simple but powerful boolean query language. -PPI search form Easy to use PPI search -Protein search Just find proteins of interest in the database Sponsors: This work is funded by a grant from the German Federal Ministry of Education and Research.

Proper citation: MIPS Mammalian Protein-Protein Interaction Database (RRID:SCR_008207) Copy   

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http://skonops.imbb.forth.gr/

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on November 22, 2023. A database containing genomic/biological information on anopheline mosquitoes, with an emphasis on Anopheles gambiae, the world''''s most important malaria vector. AnoBase is an integrated, relational database of basic biological and genetic data on anopheline species, with a particular emphasis on Anopheles gambiae. It has been designed as an information source and research support tool for the broad vector biology community. Although AnoBase is not a primary genomic database that develops and provides tools to access the genome of the malaria mosquito, it nevertheless contains several sections that offer data of genomic interest such as in situ hybridization images, an integrated gene tool and direct online access to AnoXcel, the proteomic database of An. gambiae. Moreover, AnoBase also contains information on non-gambiae mosquito species and a novel section on studies related to insecticide resistance.

Proper citation: AnoBase: An Anopheles database (RRID:SCR_008166) Copy   

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http://www.molgen.ua.ac.be/ADMutations/default.cfm?MT=1&ML=0&Page=ADMDB

A locus-specific database aimed at collecting known mutations and non-pathogenic coding variations in the genes related to Alzheimer disease (AD) and frontotemporal dementia (FTD), following the guidelines of the Human Genome Variation Society. Mutations can be retrieved based on the gene, phenotype and publication. The database contains mutations reported in the literature and at scientific meetings, and unpublished mutations directly submitted to the database. To date, AD&FTDMDB contains mutations in the genes encoding the Amyloid Beta Precursor Protein (APP), Presenilin 1 (PSEN1), Presenilin 2 (PSEN2), Chromatin Modifying Protein 2B (CHMP2B), fusion (involved in t(12;16) in malignant liposarcoma) (FUS), Granulin (GRN), Microtubule Associated Protein Tau (MAPT), TAR DNA binding protein (TARDBP) and Valosin-containing Protein (VCP) and holds 415 different mutations observed in 1027 patients or families. As of March 2013, the latest publications referenced were from 2008, indicating that this resource may not be up to date.

Proper citation: Alzheimer Disease and Frontotemporal Dementia Mutation Database (RRID:SCR_008286) Copy   

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http://locustdb.genomics.org.cn/

The migratory locust (Locusta migratoria) is an orthopteran pest and a representative member of hemimetabolous insects. Its transcriptomic data provide invaluable information for molecular entomology study of the insect and pave a way for comparative studies of other medically, agronomically, and ecologically relevant insects. This first transcriptomic database of the locust (LocustDB) has been developed, building necessary infrastructures to integrate, organize, and retrieve data that are either currently available or to be acquired in the future. It currently hosts 45,474 high quality EST sequences from the locust, which were assembled into 12,161 unigenes. This database contains original sequence data, including homologous/orthologous sequences, functional annotations, pathway analysis, and codon usage, based on conserved orthologous groups (COG), gene ontology (GO), protein domain (InterPro), and functional pathways (KEGG). It also provides information from comparative analysis based on data from the migratory locust and five other invertebrate species, such as the silkworm, the honeybee, the fruitfly, the mosquito and the nematode. LocustDB also provides information from comparative analysis based on data from the migratory locust and five other invertebrate species, such as the silkworm, the honeybee, the fruitfly, the mosquito and the nematode. It starts with the first transcriptome information for an orthopteran and hemimetabolous insect and will be extended to provide a framework for incorporation of in-coming genomic data of relevant insect groups and a workbench for cross-species comparative studies.

Proper citation: Migratory Locust EST Database (RRID:SCR_008201) Copy   

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http://andromeda.gsf.de/litminer

THIS RESOURCE IS NO LONGER IN SERVICE, documented August 23, 2016. The LitMiner software is a literature data-mining tool that facilitates the identification of major gene regulation key players related to a user-defined field of interest in PubMed abstracts. The prediction of gene-regulatory relationships is based on co-occurrence analysis of key terms within the abstracts. LitMiner predicts relationships between key terms from the biomedical domain in four categories (genes, chemical compounds, diseases and tissues). The usefulness of the LitMiner system has been demonstrated recently in a study that reconstructed disease-related regulatory networks by promoter modeling that was initiated by a LitMiner generated primary gene list. To overcome the limitations and to verify and improve the data, we developed WikiGene, a Wiki-based curation tool that allows revision of the data by expert users over the Internet. It is based on the annotation of key terms in article abstracts followed by statistical co-citation analysis of annotated key terms in order to predict relationships. Key terms belonging to four different categories are used for the annotation process: -Genes: Names of genes and gene products. Gene name recognition is based on Ensembl . Synonyms and aliases are resolved. -Chemical Compounds: Names of chemical compounds and their respective aliases. -Diseases and Phenotypes: Names of diseases and phenotypes -Tissues and Organs: Names of tissues and organs LitMiner uses a database of disease and phenotype terms for literature annotation. Currently, there are 2225 diseases or phenotypes, 801 tissues and organs, and 10477 compounds in the database.

Proper citation: LitMiner (RRID:SCR_008200) Copy   

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http://www.ebi.ac.uk/asd/altsplice/index.html

AltSplice is a computer generated high quality data set of human transcript-confirmed splice patterns, alternative splice events, and the associated annotations. This data is being integrated with other data that is generated by other members of the ASD consortium. The ASD project will provide the following in its three year duration: -human curated database of alternative spliced genes and their properties -a computer generated database of alternatively spliced genes and their properties -the integration of the above and newly found knowledge in a user-friendly interface and research workbench for both bioinformaticists and biologists -DNA chips that are based on the data in the above databases -the DNA chips will be used to test against predisposition for and diagnoses of human diseases ASD aims to analyse this mechanism on a genome-wide scale by creating a database that contains all alternatively spliced exons from human, and other model species. Disease causing mutations seem to induce aberrations in the process of splicing and its regulation. The ASD consortium will develop a DNA microarray (chip) that contains cDNAs of all the splicing regulatory proteins and their isoforms, as well as a chip that contains a number of disease relevant genes. We will concentrate on three models of disease (breast cancer, FTDP-17, male infertility) in which a connection between mis-splicing and a pathological state has been observed. Finally, these chips will be developed as demonstrative kits to detect predisposition for and diagnosis of such diseases. Categories: Nucleotide Sequences: Gene Structure, Introns and Exons, & Splice Sites Databases

Proper citation: AltSplice Database of Alternative Spliced Events (RRID:SCR_008162) Copy   

•   Source: SciCrunch Registry

http://research.amnh.org/herpetology/amphibia/index.php

The Amphibian Species of the World database has two searching tools, a BROWSE table and a SEARCH table. It provides needed information for research and conservation needs and to help illuminate where geographical data or taxonomic information are woefully inadequate. In addition, all underlined author names, dates, and publications may be clicked through to see other records citing this author, date, or publication. The basic structure of a taxonomic record is (1) Current scientific name, author and year of publication; (2) original name, authorship, citation, and if relevant, location of primary types and type locality (direct quotation if possible). This is followed by a synonymy composed of all new names, their authorship, literature citation, and (if relevant) location of primary types, and type locality as well as all new combinations and the literature source of the synonymy or combination; (3) published English names; (4) known or inferred distribution of the taxon; (5) comments to controversies or relevant taxonomic literature. A synonymy follows the currently recognized name. The synonymy includes synonyms and relevant combinations (in the sense of the International Code of Zoological Nomenclature, 1999) as well as deposition of types, type localities, and the source of the synonymy. Users who are looking for the synonymy to provide unerringly and precisely the names by which a taxon have been mentioned will be disappointed, and will be misled if they approach synonymies this way.

Proper citation: Amphibian Species of the World (RRID:SCR_008164) Copy   

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http://agricola.nal.usda.gov/

A database, catalog and index to the collections of the National Agricultural Library, as well as a primary public source for world-wide access to agricultural information. This database resource covers materials in all formats and periods, including printed works from as far back as the 15th century. AGRICOLA is a bibliographic database of citations to the agricultural literature created by the National Agricultural Library and its cooperators. The records describe publications and resources encompassing all aspects of agriculture and allied disciplines, including animal and veterinary sciences, entomology, plant sciences, forestry, aquaculture and fisheries, farming and farming systems, agricultural economics, extension and education, food and human nutrition, and earth and environmental sciences. Although the NAL Catalog (AGRICOLA) does not contain the text of the materials it cites, thousands of its records are linked to full-text documents online, with new links added daily. The NAL Catalog (AGRICOLA) is organized into two bibliographic data sets: *The NAL Online Public Access Catalog (AGRICOLA NAL) contains citations to books, audiovisuals, serials, and other materials, most of which are in the Library''s collection. (The Catalog does contain some records for items not held at NAL.) *The Article Citation Database (AGRICOLA IND) includes citations, many with abstracts, to journal articles (see Journals Indexed in AGRICOLA), book chapters, reports, and reprints, selected primarily from the materials found in the NAL Catalog.

Proper citation: AGRICOLA (RRID:SCR_008158) Copy   

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http://www.ebi.ac.uk/genomes/plasmid.html

The Plasmid Genome Database aims to collate biological and genomic data for all bacterial plasmids in the hopes of enabling rapid, interrogation of both meta- and genomic data. Data maintained includes access to all plasmid genomes and information on core genomic features obtained from parsing the original EMBL/DDBJ/NCBI submission. In addition a suite of third party analyses has been performed for each genome to supplement the original annotation. This site also links to Genome Atlases provided by the Centre for Biological Sequence Analysis (CBS). The motivation behind the construction of this site derived from observations from genome sequencing projects: the abundance and inferred importance of the horizontal gene pool (HGP) in bacterial adaptation and evolution. In so far as plasmids are autonomously replicating, extrachromosomal elements they are a readily identifiable and accessible component of the HGP. Also plasmids have been identified in almost all bacterial divisions, ranging in size from less than 2 kbp to > 1.5 Mbp and as such represent a defined, yet diverse and complex sample of genes in the HGP.

Proper citation: Plasmid Genome Database (RRID:SCR_008228) Copy   

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http://www.cmbi.ru.nl/GeneSeeker/

The GeneSeeker allows you to search across different databases simultaneously, given a known human genetic location and expression/phenotypic pattern. The GeneSeeker returns any found gene names which are located on the specified location and expressed in the specified tissue. To search for more expression location in one search, just enter them in the textbox for the expression location and separate them with logical operators (and, or, not). You can specify as many tissues as you want, the program starts 20 queries simultaneously, and then waits for a query to finish before starting another query, to keep server loads to a minimum. You can also search only for expression, just leave the cytogenetic location fields blank, and do the query. If you only want to look for one cytogenetic location, only fill in the first location field, and the GeneSeeker will search with only this one. Housekeeping genes , found in Swissprot can be excluded, or genes that are to be excluded can be specified. Human chromosome localizations are translated with an oxford-grid to mouse chromosome localizations, and then submitted to the Mgd. Sponsors: GeneSeeker is a service provided by the Centre for Molecular and Biomolecular Informatics (CMBI).

Proper citation: GeneSeeker (RRID:SCR_008347) Copy   

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http://www.bioinf.mdc-berlin.de/splice/db/

THIS RESOURCE IS NO LONGER IN SERVICE, documented on July 15, 2013. An online available compendium of alternative splice forms for several organisms (Arabidopsis thaliana, Bos taurus, Caenorhabditis elegans, Drosophila melanogaster, Danio rerio, Homo sapiens, Mus musculus, Rattus norvegicus, Xenopus laevis). Alternative splice forms are defined by comparing high-scoring ESTs to mRNA sequences (both from GenBank) with known exon-intron information (from ENSEMBL database) using BLAST. Repetitive sequences of all mRNAs have beforehand been masked by MaskerAid. Filtering programs with defined parameters compare the ends of each aligned sequence pair for deletions or insertions in the EST sequence, which suggest the existence of alternative splice forms. The database is accessible by typing in accession numbers (ACC) or keywords like description, gene names, organism or other keywords. (If more than one hit was found a list of all results is given.) And the result page is divided into 4 major parts. The first part (General Information About The Entry) summarizes the most important information as database ids, organism, and description. The so called alternative splice profile (ASP) of each human sequence is shown in the second part (Alternative Splice Frequency). The ASP indicates the number of alternatively spliced ESTs (NAE), the number of constitutively spliced ESTs (NCE) as well as the number of alternative splice sites (NSS) per mRNA. NAE and NCE corresponds to the EST coverage and can be used as a quality value for the predicted alternative splice variants. The NSS value specifies the splice propensity of a gene. Moreover the number of ESTs from cancerous tissues is shown. The histological source and the developmental stages are illustrated with several colors to enables the user to get an overview of the origins of the matching ESTs. Also, the Splice Site View shows graphically all alternative splice sites for the whole transcript.

Proper citation: Extended Alternatively Spliced EST Database (RRID:SCR_008186) Copy   

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http://csbdb.mpimp-golm.mpg.de/

CSB.DB presents the results of bio-statistical analysis on gene expression data in association with additional biochemical and physiological knowledge. The main aim of this database platform is to provide tools that support insight into life''s complexity pyramid with a special focus on the integration of data from transcript and metabolite profiling experiments. The main focus of the CSB project is the generation of new easily accessible knowledge about the relationship and the hierarchy of cellular components. Thus new progress towards understanding lifes complexity pyramid is made. For this aim statistical and computational algorithms are applied to organism specific data derived from publicly available multi-parallel technologies, currently such as expression profiles. The underlying data are derived from various research activities. Thus CSB project provides an integrated and centralized public resource allowing universal access on the generated knowledge CSB.DB: A Comprehensive Systems-Biology Database. The derived knowledge should support the formulation of new hypotheses about the respective functional involvement of genes beyond their (inter-) relationships. Another major goal of the CSB project is to supply the researchers with necessary information to formulate these new hypotheses without demanding any a-priori statistical knowledge of the user. The CSB project mainly focuses on application of required statistical tests as well as to assist the user during exploration of results with information / help files to support hypothesis generation

Proper citation: Comprehensive Systems-Biology Database (RRID:SCR_008185) 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   

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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   

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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://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://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