Curator Dashboard

THIS RESOURCE IS NO LONGER IN SERVICE, documented August 18, 2016. MAP-O-MAT is a web-based server for automated linkage mapping of human polymorphic DNA markers. The server uses publicly available genotype data for over 15,000 markers. It facilitates the verification of order and map distances for custom mapping sets using genotype data from the CEPH database, and from the Marshfield, SNP Consortium and Rutgers linkage maps. The CRI-MAP program is used for likelihood calculations and some mapping algorithms, and physical map positions are provided from the human genome assembly.

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• SciCrunch
• 17 years ago - by Anonymous

ITER is a toxicology data file on the National Library of Medicine''s (NLM) Toxicology Data Network. It contains data in support of human health risk assessments. It is compiled by Toxicology Excellence for Risk Assessment (TERA) and contains over 600 chemical records with key data from the Agency for Toxic Substances & Disease Registry (ATSDR), Health Canada, National Institute of Public Health & the Environment (RIVM) - The Netherlands, U.S. Environmental Protection Agency (EPA), and independent parties whose risk values have undergone peer review. ITER provides a comparison of international risk assessment information in a side-by-side format and explains differences in risk values derived by different organizations. ITER data, focusing on hazard identification and dose-response assessment, is extracted from each agencys assessment and contains links to the source documentation. Among the key data provided in ITER are ATSDRs minimal risk levels; Health Canadas tolerable intakes/concentrations and tumorigenic doses/concentrations; EPAs carcinogen classifications, unit risks, slope factors, oral reference doses, and inhalation reference concentrations; RIVMs maximum permissible risk levels; NSF International''s reference doses and carcinogen risk levels, IARC''s cancer classifications, and noncancer and/or cancer risk values (that have undergone peer review) derived by independent parties. Users can search by chemical or other name, chemical name fragment, or Chemical Abstracts Service Registry Number(RN), and/or subject terms. Search results can easily be viewed, printed or downloaded. Search results are displayed in relevancy ranked order. Users may select to display exact term matches, complete records, or any combination of data from the following broad groupings: -Noncancer Oral -Cancer Oral -Noncancer Inhalation -Cancer Inhalation

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• SciCrunch
• 17 years ago - by Anonymous

INFOMINE is a virtual library of Internet resources relevant to faculty, students, and research staff at the university level. It contains useful Internet resources such as databases, electronic journals, electronic books, bulletin boards, mailing lists, online library card catalogs, articles, directories of researchers, and many other types of information. It is librarian built. Librarians from the University of California, Wake Forest University, California State University, the University of Detroit - Mercy, and other universities and colleges have contributed to building INFOMINE.INFOMINE is a unique Web resource featuring well organized access to important university level research and educational tools on the Internet. A virtual library, INFOMINE is notable for its collection of annotated and indexed links. Information in INFOMINE is easy to find given the multiplicity of access points provided (ways of finding the information contained). It contains over 100,000 links (26,000 librarian created links and 75,000 plus robot/crawler created links). Substantive databases, electronic journals, guides to the Internet for most disciplines, textbooks and conference proceedings are among the many types of resources included. The life sciences INFOMINE alone, for example, provides interactive access to several hundred databases. Separate virtual collections or INFOMINEs exist in most major areas of university level research and educational interests. Click here to see the Database Content Information. It began in January of 1994 as a project of the Library of the University of California, Riverside. It was one of the first Web resources of any type offered by a Library. It was also one of the first Web-based, academic virtual libraries as well as one of the first to develop a system combining the advantages of the hypertext and multi-media capabilities of the Web with those of the organizational and retrieval functions of a database manager. We now include focused, automatic Internet crawling as well as automatic text extraction and metadata creation functions to assist our experts in content creation and users in searching. Many of INFOMINE''s important features and services, described below, remain unique among Internet resource collections. INFOMINE, as mentioned, provides a great number of access points, BROWSE (What''s New, Title, Table of Contents, Subject -- LCSH, Subject - LCC, Search -- Research Discipline, Key Word, Megatopics - Keyword in context, Title, Author, hyperlinked indexing) and SEARCH (Title, Subject -- LCSH, Key Word, Author, Description, Full-text), and LIMIT search (Resource Type, Resource Origin and Access) modes. Searching in fielded and full-text mode allows the user to quickly find high quality resources on the chosen subject(s). Nested, boolean searching capabilities are featured as is exact searching. Search results come back in the form of dynamically created Web pages. Results within these can be ranked by relevance to the search or alphabetically by title. Displays available include title only, regular display, long display and full display. Many of the displays feature indexing terms that are viewable and in hyperlink form and, when clicked upon, allow further broadening or narrowing of the search as desired. Noteworthy as well is that INFOMINE is augmenting its content with a large, second-tier collection of automatically selected and described resources that should complement its first-tier collection of expert created records and allow the user more detail in searching and a greater number of useful results. Finally, on the Advanced Search page, we have browsing indexes which cover all topics and more. These include: * Subjects -- LCSH (Library of Congress Subject Headings), Table of Contents (LCSH with titles filing under each heading), LCC (Library of Congress Classifications) and Research Disciplines * Keywords -- MegaTopics (keyphrases drawn from our title, subject, keyword and annotation fields) and standard Keywords * Other Indexes: Authors, Titles, Resource Types and What''s New Among the contributions of INFOMINE is the essential enrichment or value added service, as mentioned, of providing concise descriptive information (e.g., an annotation as well as in-depth indexing terminology including Library of Congress Subject Headings) for each record. This greatly helps users to quickly retrieve a focused results set, examine the relevance of individual records and then choose among them immediately prior to accessing thus saving considerable time. Librarian collection expertise and concerns regarding resource comprehensiveness, quality and general usefulness from an academic perspective guide all INFOMINE resource selection activities. Also a crucial behind-the-scenes contribution from INFOMINE is that we are addressing the challenge of better subject portal scaling (keeping up with the growth of important resources on the Internet) by semi-automating or automating a number of collection building tasks (e.g., identification of significant resources for collecting and developing metadata descriptions of these resources). This will help us save crucial resources and redirect them into creating a better and larger collection. In-depth description and indexing, careful selection, a considerable number of options in browsing/searching, and ample help in usage mean that faculty, students and staff can find important, relevant Internet resources quickly and easily via INFOMINE. Sponsors: INFOMINE is supported by the University of California, Riverside.

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• SciCrunch
• 17 years ago - by Anonymous

Comprehensive collection of spatially interacting motifs in proteins. Interacting motif database lists interacting motifs that are identified for all structural entries in PDB. Conserved patterns or finger prints are identified for individual structural entries and also grouped together for reporting common motifs shared among all superfamily members.

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• SciCrunch
• 17 years ago - by Anonymous

THIS RESOURCE IS NO LONGER IN SERVICE, documented on July 15, 2013. It contains annotated structural elements characteristic for certain classes of structural and/or functional RNAs. These elements are described in a language specifically designed for this purpose. The language allows convenient specification of hybrid patterns, i.e. motifs consisting of sequence features and structural elements together with sequence similarity and thermodynamic constraints. A system that searches complex patterns (on nucleic-acid or protein level) in large biosequence-databases. As patterns, they allow hybrid patterns, which combine sequence similarity, structure similarity and arbitrary characteristics, like thermodynamic constraints. Applications are in the research of highly specific Protein/RNA-interactions or in the search of RNA-tertiary-structure-interactions. They developed a declarative pattern description language, which is implemented by known and new pattern-matching algorithms and an optimizing backtracking procedure. To achieve high efficiency when screening large data sets, the patterns are divided and queries are composed. The significance of patterns is estimated by a Monte-Carlo procedure. Complex results of queries are processed by a visualizing component. A library of biologically relevant patterns is developed and it is provided on the WWW together with the search-tool. The evaluation of the tool w.r.t. to the biosequence databases will in some cases mean to make laboratory-experiments, in order to check algorithmically developed functional hypothesis. Sponsors: This project is supported by a grant from the Deutsche Forschungsgemeinschaft. It is part of the special program on Computational Methods for the Analysis and Interpretation of large genomic data

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• SciCrunch
• 17 years ago - by Anonymous

A REST-based web application designed for visualizing deep sequencing data and other genome annotation data.

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• SciCrunch
• 13 years ago - by Anonymous

JenPep is a database of quantitative binding data for immunological protein-peptide interactions. It is a system which allows speedy access to this binding data through simple on-line interfaces and effective search mechanisms.

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• SciCrunch
• 17 years ago - by Anonymous

THIS RESOURCE IS NO LONGER IN SERVICE, documented August 22, 2016. A database of candidate genes for mapped inherited human diseases. Candidate priorities are automatically established by a data mining algorithm that extracts putative genes in the chromosomal region where the disease is mapped, and evaluates their possible relation to the disease based on the phenotype of the disorder. Data analysis uses a scoring system developed for the possible functional relations of human genes to genetically inherited diseases that have been mapped onto chromosomal regions without assignment of a particular gene. Methodology can be divided in two parts: the association of genes to phenotypic features, and the identification of candidate genes on a chromosonal region by homology. This is an analysis of relations between phenotypic features and chemical objects, and from chemical objects to protein function terms, based on the whole MEDLINE and RefSeq databases.

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• SciCrunch
• 17 years ago - by Anonymous

The Foundational Model Explorer (FME) is an internet based software application developed for viewing the content and organization of the Foundational Model of Anatomy Ontology (FMA). The initial purpose of the FME was to provide a simple and intuitive interface to the FMA for domain experts, in the field of anatomy, participating in the evaluation of the FMA. The FME also provides an easily available method of exploring the FMA to individuals or groups considering the adoption of the Foundational Model of Anatomy knowledge base. The FME display consists of two panes: a hierarchical tree may be opened up in the pane on the left side; if a class is selected in the hierarchical tree, the pane on the right side displays the information that has been entered in the FMA for that class. The information associated with a given class is organized in so-called slots. Each slot has a name (e.g., Definition, Parts) and some content, which is that particular slots value (e.g., the English definition and the names of parts of the selected class, respectively). For an explanation of the interactive features of the FME, see the Knowledge Navigation Section. For a guided tutorial check out the Conducted Tour. In the left pane, the default tree is a subclass hierarchy, based on the -is a- or -kind of- relationship; it is the instantiation of the Anatomy taxonomy (At) component in the high level scheme of the Foundational Model of Anatomy. Apart from the slots Preferred Name and Synonyms, other slots relate to the Anatomical Structural Abstraction (ASA) component in the FMAs high level scheme. Hierarchies based on various part-whole relationships can also be opened up in the left pane. Once a class has been highlighted in the subclass hierarchy, you can choose a relationship from a drop down list labeled Select navigation tree type. Some other transitive relationships (e.g., -branch of- and -tributary of-) are also available. The Search facility matches a search term to the preferred name, as well as to the Latin name, or synonym of an FMA class (if such exist). The tree is expanded to reveal the matching class and the information about this class is displayed. The wildcard * is allowed in the search term and will match to any sequence of characters. For example the search term h*d matches the class names Head and Hepatic cord (amongst others). The search function is not case sensitive. If more than one class name matches with the search term, a list of matching terms is presented for the user to choose between.

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• SciCrunch
• 17 years ago - by Anonymous

eTBLAST is a unique search engine for searching biomedical literature. Our service is very different from PubMed. While PubMed searches for keywords, our search engine lets you input an entire paragraph and returns MEDLINE abstracts that are similar to it. This is something like PubMed''s Related Articles feature, only better because it runs on your unique set of interests. For example, input the abstract of an unpublished paper or a grant proposal into our engine, and with the touch of a button you''ll be able to find every abstract in MEDLINE dealing with your topic. No more guessing whether your set of keywords has found all the right papers. No more sorting through hundreds of papers you don''t care about to find the handful you were looking for--our search engine does it for you. When most people use PubMed to search MEDLINE they pick one or two keywords to describe their topic, then browse through a long list of results. When they find a paper that looks interesting they click on its Related Articles, in hopes of finding more papers like that one. If they find another relevant paper, they explore it''s related articles--and so on. This process of culling long lists of documents by hand makes literature searching tedious and time consuming. We make it easier for you by providing better results the first time, and then allowing you to automatically combine the papers you care about for a second round. Our Iterate feature allows you to checkmark the abstracts you found interesting in the first round and combine them all to create a new query. It''s like rolling several Related Articles lists into one. * We sort our results by relevance, while PubMed sorts by date. * We save you the time and effort of creating a complicated query. * We let you iterate your search over several good papers to narrow your focus. * We provide you the full MEDLINE abstract in our results, and a link to the PubMed page. * We can send your results straight to your email so you never lose a reference or forget where you found it. * This absolutely free service is provided by the University of Texas Southwestern Medical Center. No registration necessary!

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• SciCrunch
• 17 years ago - by Anonymous

THIS RESOURCE IS NO LONGER IN SERVICE, documented on July 15, 2013. EDGE is a scientific resource for toxicology-related gene expression information. The site contains databases and analyses of gene expression studies following exposure to a variety of chemicals or physiological changes. The ultimate goal of the EDGE is to map transcriptional changes from chemical exposure that will someday be used as a diagnostic fingerprint to predict toxicity as well as provide valuable insights into the basic molecular changes responsible. EDGE gives you the ability to easily answer the following fundamental questions about your data 1. Can I compare transcriptional profiles across treatments? 2. What genes respond to my treatment? 3. What influences my favorite gene(s)? One of the major objectives of toxicology is to understand the adverse health effects that result from exposure to foreign chemicals. The traditional method for assessing the toxicity of a test chemical is very resource intensive; requiring the commitment of large amounts of money, time, and animals. According to the National Toxicology Program (NTP), each chemical study requires between 2 and 4 million dollars and several years to complete. Due to the cost and labor intensive nature of these studies, the number of chemicals currently tested by the NTP stands at less than 500. Given these statistics and the fact that there are approximately 70,000 chemicals in commerce today, it is increasingly apparent that alternative methods for assessing toxic potential must be explored if a significant portion of the remaining chemicals is to be tested. One potential solution is to develop a comprehensive database that describes alterations in gene expression resulting from chemical exposure. The pattern of transcriptional activity will not only be highly sensitive indicator of chemical exposure, but that this pattern will be diagnostic for mechanistically linked toxicants. In our laboratory, we have chosen to address this problem through a combination of high throughput sequencing of expressed sequence tags (ESTs) and construction of custom toxicology-related cDNA microarrays derived from the unique ESTs identified in the sequencing effort. By using this approach, we can simultaneously develop a quantitative gene expression profile using ESTs and the reagents for further analyzing these changes in a rapid, highly parallel manner. In addition, the expression profiles are not biased for preselected favorite genes. The resulting gene expression pattern can then be used as diagnostic fingerprint to predict toxicity and/or carcinogenicity as well as provide valuable insight into the basic biochemical and molecular changes responsible for toxicity. Submission of total RNA for Bradfield Lab Microarray Microarray comparisons are made between untreated, control animals and animals treated with ONE treatment. Please make sure the RNA submitted adheres to this experimental design. Necessary information is available on the site.

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• SciCrunch
• 17 years ago - by Anonymous

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.

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• SciCrunch
• 17 years ago - by Anonymous

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

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• SciCrunch
• 17 years ago - by Anonymous

Software package that contains a collection of statistical tools for analysing RNA-seq expression data.

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• SciCrunch
• 13 years ago - by Anonymous

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

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• SciCrunch
• 17 years ago - by Anonymous

THIS RESOURCE IS NO LONGER IN SERVICE, documented on 6/12/25. ITTACA is a database created for Integrated Tumor Transcriptome Array and Clinical data Analysis. ITTACA centralizes public datasets containing both gene expression and clinical data and currently focuses on the types of cancer that are of particular interest to the Institut Curie: breast carcinoma, bladder carcinoma, and uveal melanoma. ITTACA is developed by the Institut Curie Bioinformatics group and the Molecular Oncology group of UMR144 CNRS/Institut Curie. A web interface allows users to carry out different class comparison analyses, including comparison of expression distribution profiles, tests for differential expression, patient survival analyses, and users can define their own patient groups according to clinical data or gene expression levels. The different functionalities implemented in ITTACA are: - To test if one or more gene, of your choice, is differentially expressed between two groups of samples exhibiting distinct phenotypes (Student and Wilcoxon tests). - The detection of genes differentially expressed (Significance Analysis of Microarrays) between two groups of samples. - The creation of histograms which represent the expression level according to a clinical parameter for each sample. - The computation of Kaplan Meier survival curves for each group. ITTACA has been developed to be a useful tool for comparing personal results to the existing results in the field of transcriptome studies with microarrays.