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Statistical test for model organisms association mapping correcting for the confounding from population structure and genetic relatedness. EMMA takes advantage of the specific nature of the optimization problem in applying mixed models for association mapping, which substantially increases the computational speed and the reliability of the results. The current implementation of EMMA is available in an R package. The documentation is included in the installation package.

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• SciCrunch
• 17 years ago - by Anonymous

Web application that uses a combination of computational methods to identify those changes most likely to be cancer-associated.

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• SciCrunch
• 13 years ago - by Anonymous

NSDL is a digital library of exemplary resource collections and services, organized in support of science education at all levels. Starting with a partnership of NSDL-funded projects, NSDL is emerging as a center of innovation in digital libraries as applied to education, and a community center for groups focused on digital-library-enabled science education. The National Science Digital Library (NSDL) was created by the National Science Foundation to provide organized access to high quality resources and tools that support innovations in teaching and learning at all levels of science, technology, engineering, and mathematics (STEM) education. As a national network of learning environments, resources, and partnerships, NSDL seeks to serve a vital role as STEM educational cyberlearning for the nation, meeting the informational and technological needs of educators and learners at all levels. Educators need efficient and reliable methods to discover and use science and math materials that help them meet the demands of instruction, assessment, and professional development in an increasingly complex technology-based world. NSDL provides an organized point of access to: -High-quality STEM content aggregated from a variety of other digital libraries, NSF-funded projects, and NSDL-reviewed web sites. -Services and tools that enhance the use of this content in a variety of contexts. NSDL is designed primarily for K-16 educators, but anyone can access NSDL.org and search the library at no cost. Access to most resources discovered through NSDL is free; however, some content providers may require a login, or a nominal fee or subscription to retrieve their specific resources. NSDL serves as a nexus for educators, researchers, policy makers and the public by building bridges: -Between private sector and public interests by providing access to resources such as publisher'' journal articles, teacher-created lesson plans and real-time data sets from scientists -Between the scientific, research and educational communities by applying advanced technologies to stimulate new ways for educators and learners to access and use scientific information -Between teachers and learners at all levels, in all locations by supplying content and tools in open-access, non-proprietary formats in an easily accessible online environment. Sponsors: This work supported by the National Science Foundation under Grant No. 0733600, Grant No. 0424671, Grant No. 0227648, Grant No. 0227656, and Grant No. 0227888.

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• SciCrunch
• 17 years ago - by Anonymous

Concise, clinically useful information for diagnosing, managing and treating infectious diseases in adults; however it does cover some pediatric topics including vaccines. It is designed for primary care providers and other non-infectious disease specialists as a tool that can be used at the point of care to assist in prescribing antibiotics.

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• SciCrunch
• 17 years ago - by Anonymous

Generates data for use in developing and refining computational tools for comparing genomic sequence from multiple species. The NISC Comparative Sequencing Program's goal is to establish a data resource consisting of sequences for the same set of targeted genomic regions derived from multiple animal species. The broader program includes plans for a diverse set of analytical studies using the generated sequence and the publication of a series of papers describing the results of those analysis in peer-reviewed journals in a timely fashion. Experimentally, this project involves the shotgun sequencing of mapped BAC clones. For each BAC, an assembly is first performed when a sufficient number of sequence reads have been generated to provide full shotgun coverage of the clone. At that time, the assembled sequence is submitted to the HTGS division of GenBank. Subsequent refinements of the sequence, including the generation of higher-accuracy finished sequence, results in the updating of the sequence record in GenBank. By immediately submitting our BAC-derived sequences to GenBank, it makes their data available as a public service to allow colleagues to speed up their research, consistent with the now well-established routine of sequencing centers participating in the Human Genome Project. However, at the same time, it has made considerable investment in acquiring these mapping and sequence data, including sizable efforts of graduate students, postdoctoral fellows, and other trainees. Furthermore, in most cases, large data sets involving multiple BAC sequences from multiple species must first be generated, often taking many months to accumulate, before the planned analysis can be performed and the resulting papers written and submitted for publication.

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• SciCrunch
• 17 years ago - by Anonymous

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.

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• SciCrunch
• 17 years ago - by Anonymous

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.

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• SciCrunch
• 17 years ago - by Anonymous

THIS RESOURCE IS NO LONGER IN SERVICE, documented on 6/24/13. A repository of information on commercially available phospho-specific antibodies to human phosphorylation sites. It provides a BLAST search for phosphorylation sites using as query the amino acid sequence surrounding the site. It also provides direct links to the relevant antibodies from many companies including BD Pharmingen, Biosource International, Cell Signaling Technology (CST), Santa Cruz Biotechnologies, Upstate Biotechnology.

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• SciCrunch
• 17 years ago - by Anonymous

A database of manually annotated mammalian protein complexes. To obtain a high-quality dataset, information was extracted from individual experiments described in the scientific literature. Data from high-throughput experiments was not included.

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• SciCrunch
• 17 years ago - by Anonymous

MitoRes, is a comprehensive and reliable resource for massive extraction of sequences and sub-sequences of nuclear genes and encoded products targeting mitochondria in metazoa. It has been developed for supporting high-throughput in-silico analyses aimed to studies of functional genomics related to mitochondrial biogenesis, metabolism and to their pathological dysfunctions. It integrates information from the most accredited world-wide databases to bring together gene, transcript and encoded protein sequences associated to annotations on species name and taxonomic classification, gene name, functional product, organelle localization, protein tissue specificity, Enzyme Classification (EC), Gene Ontology (GO) classification and links to other related public databases. The section Cluster, has been dedicated to the collection of data on protein clustering of the entire catalogue of MitoRes protein sequences based on all versus all global pair-wise alignments for assessing putative intra- and inter-species functional relationships. The current version of MitoRes is based on the UniProt release 4 and contains 64 different metazoan species. The incredible explosion of knowledge production in Biology in the past two decades has created a critical need for bioinformatic instruments able to manage data and facilitate their retrieval and analysis. Hundreds of biological databases have been produced and the integration of biological data from these different resources is very important when we want to focus our efforts towards the study of a particular layer of biological knowledge. MitoRes is a completely rebuilt edition of MitoNuc database, which has been extensively modified to deal successfully with the challenges of the post genomic era. Its goal is to represent a comprehensive and reliable resource supporting high-quality in-silico analyses aimed to the functional characterization of gene, transcript and amino acid sequences, encoded by the nuclear genome and involved in mitochondrial biogenesis, metabolism and pathological dysfunctions in metazoa. The central features of MitoRes are: # an integrated catalogue of protein, transcript and gene sequences and sub-sequences # a Web-based application composed of a wide spectrum of search/retrieval facilities # a sequence export manager allowing massive extraction of bio-sequences (genes, introns, exons, gene flanking regions, transcripts, UTRs, CDS, proteins and signal peptides) in FASTA, EMBL and GenBank formats. It is an interconnected knowledge management system based on a MySQL relational database, which ensures data consistency and integrity, and on a Web Graphical User Interface (GUI), built in Seagull PHP Framework, offering a wide range of search and sequence extraction facilities. The database is compiled extracting and integrating information from public resources and data generated by the MitoRes team. The MitoRes database consists of comprehensive sequence entries whose core data are protein, transcript and gene sequences and taxonomic information describing the biological source of the protein. Additional information include: bio-sequences structure and location, biological function of protein product and dynamic links to both, external public databases used as data resources and public databases reporting complementary information. The core entity of the MitoRes database is represented by the protein so that each MitoRes entry is generated for each protein reported in the UniProt database as a nuclear encoded protein involved in mitochondrial biogenesis and function. Sponsors: MitoRes has been supported by Ministero Universit e Ricerca Scientifica, Italy (PRIN, Programma Biotecnologie legge 95/95-MURST 5, Proiect MURST Cluster C03/2000, CEGBA). Currently it is supported by operating grants from the Ministero dellIstruzione, dellUniversit e della Ricerca (MIUR), Italy (PNR 2001-2003 (FIRB art.8) D.M. 199, Strategic Program: Post-genome, grant 31-063933 and Project n.2, Cluster C03 L. 488/929).

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• SciCrunch
• 17 years ago - by Anonymous

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.

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• SciCrunch
• 14 years ago - by Anonymous

THIS RESOURCE IS NO LONGER IN SERVICE, documented August 23, 2016. MILANO provides a useful tool for the automatic custom annotation of microarray results which is based on all the available literature. It has two major advances over similar tools: the ability to expand gene names to include all their informative synonyms while removing synonyms that are not informative and access to the GeneRIF database which provides short summaries of curated articles relevant to known genes. MILANO allows annotation of lists of genes derived from microarray results by user defined terms. The annotation strategy is based on counting the number of literature co-occurrences of each gene on the list with a user defined term. This strategy allows the customization of the annotation procedure and thus overcomes one of the major limitations of the functional annotations usually provided with microarray results. MILANO expands the gene names to include all their informative synonyms while filtering out gene symbols that are likely to be less informative as literature searching terms. It supports searching two literature databases: GeneRIF and Medline (through PubMed), allowing retrieval of both quick and comprehensive results. MILANO''s ability can improve microarray analysis by analyzing a list of 150 genes that were affected by p53 overproduction. This analysis reveals that it enables immediate identification of known p53 target genes on this list and assists in sorting the list into genes known to be involved in p53 related pathways, apoptosis and cell cycle arrest. This program performs automatic searches in PubMed or the GeneRIF collection for articles containing co-occurrences of search terms with a list of genes (e.g. from a microarray experiment). The program is used by pasting the list of Gene ID''s or symbols in the Primary Search Term field, and the list of cross-reference search terms in the Secondary Search Term field. The output is a table containing the number of hits for each pair of search terms. Sample input is provided in the fields, so you can just click submit and see what happens. If you want to run data of your own, delete the sample input first.

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• SciCrunch
• 17 years ago - by Anonymous

Infers both the sample genotypes and error parameters that produced a metagenome data set.

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• SciCrunch
• 13 years ago - by Anonymous

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on May 12,2023. Set of databases and tools that handle genomic and metagenomic sequences in their environmental contexts.Includes geographic information system to systematically store and analyse marine genomic and metagenomic data in conjunction with contextual information; environmental genome browser with fast search functionalities; database with precomputed analyses for selected complete genomes; database and tool to classify metagenomic fragments based on oligonucleotide signatures.

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• SciCrunch
• 17 years ago - by Anonymous

The University of West Bohemia is a university in Pilsen, Czech Republic. It was founded in 1991 and consists of nine faculties.

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• SciCrunch
• 17 years ago - submitted by Andrea Stagg

THIS RESOURCE IS NO LONGER IN SERVICE, documented August 29, 2016. An algorithm that finds articles most relevant to a genetic sequence. In the genomic era, researchers often want to know more information about a biological sequence by retrieving its related articles. However, there is no available tool yet to achieve conveniently this goal. Here, a new literature-mining tool MedBlast is developed, which uses natural language processing techniques, to retrieve the related articles of a given sequence. An online server of this program is also provided. The genome sequencing projects generate such a large amount of data every day that many molecular biologists often encounter some sequences that they know nothing about. Literature is usually the principal resource of such information. It is relatively easy to mine the articles cited by the sequence annotation; however, it is a difficult task to retrieve those relevant articles without direct citation relationship. The related articles are those described in the given sequence (gene/protein), or its redundant sequences, or the close homologs in various species. They can be divided into two classes: direct references, which include those either cited by the sequence annotation or citing the sequence in its text; indirect references, those which contain gene symbols of the given sequence. A few additional issues make the task even more complicated: (1) symbols may have aliases; and (2) one sequence may have a couple of relatives that we want to take into account too, which include redundant (e.g. protein and gene sequences) and close homologs. Here the issues are addressed by the development of the software MedBlast, which can retrieve the related articles of the given sequence automatically. MedBlast uses BLAST to extend homology relationships, precompiled species-specific thesauruses, a useful semantics technique in natural language processing (NLP), to extend alias relationship, and EUtilities toolset to search and retrieve corresponding articles of each sequence from PubMed. MedBlast take a sequence in FASTA format as input. The program first uses BLAST to search the GenBank nucleic acid and protein non-redundant (nr) databases, to extend to those homologous and corresponding nucleic acid and protein sequences. Users can input the BLAST results directly, but it is recommended to input the result of both protein and nucleic acid nr databases. The hits with low e-values are chosen as the relatives because the low similarity hits often do not contain specific information. Very long sequences, e.g. 100k, which are usually genomic sequences, are discarded too, for they do not contain specific direct references. User can adjust these parameters to meet their own needs.

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• SciCrunch
• 17 years ago - by Anonymous

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.

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• SciCrunch
• 17 years ago - by Anonymous

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.

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• SciCrunch
• 17 years ago - by Anonymous

THIS RESOURCE IS NO LONGER IN SERVICE, documented August 23, 2016. KinasePathwayDatabase is an integrated database concerning completed sequenced major eukaryotes, which contains the classification of protein kinases and their functional conservation and orthologous tables among species, protein-protein interaction data, domain information, structural information, and automatic pathway graph image interface. The protein-protein interactions are extracted by natural language processing (NLP) from abstracts using basic word pattern and protein name dictionary GENA: developed by our group. In this system, pathways are easily compared among species using protein interactions data more than 47,000 and orthologous tables.

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• SciCrunch
• 17 years ago - by Anonymous

THIS RESOURCE IS NO LONGER IN SERVICE, documented on July 17, 2013. International interdisciplinary Internet based unbiased forum for review of medical literature. Very much like an on-line journal club, we aim to provide a venue which will improve communication amongst physicians and lead to better understanding and interpretation of medical literature. In the academic world, Journal Clubs are a common way to discuss and critically question medical literature. The knowledge gained by this activity can be immeasurable, and often leads to ideas both relating to patient care and to future research. However, many clinicians are unable to participate in these academic activities. In addition, information shared within an individual journal club is seldom disseminated. Here you will find a free, user-friendly website where you can rate and discuss medical literature. It offers innovative and powerful search tools that will allow you to quickly query the medical literature and identify the information you need. In addition, their exclusive tools will help identify the most important and controversial articles. It is their hope to bring both insight and questions to the literature -- both of which will be used to shape future research. Clinicians will have a better understanding of the evidence, and ability to partake in a venue that fosters communication between peers.