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At least 127 records · Page 7Linked to original sources
PubMed2005

Storing, linking, and mining microarray databases using SRS.

BACKGROUND: SRS (Sequence Retrieval System) has proven to be a valuable platform for storing, linking, and querying biological databases. Due to the availability of a broad range of different scientific databases in SRS, it has become a useful platform to incorporate and mine microarray data to facilitate the analyses of biological questions and non-hypothesis driven quests. Here we report various solutions and tools for integrating and mining annotated expression data in SRS. RESULTS: We devised an Auto-Upload Tool by which microarray data can be automatically imported into SRS. The dataset can be linked to other databases and user access can be set. The linkage comprehensiveness of microarray platforms to other platforms and biological databases was examined in a network of scientific databases. The stored microarray data can also be made accessible to external programs for further processing. For example, we built an interface to a program called Venn Mapper, which collects its microarray data from SRS, processes the data by creating Venn diagrams, and saves the data for interpretation. CONCLUSION: SRS is a useful database system to store, link and query various scientific datasets, including microarray data. The user-friendly Auto-Upload Tool makes SRS accessible to biologists for linking and mining user-owned databases.

Computational Biology↗
PubMed1997

Using explicitly represented biological relationships for database navigation and searching via the World-Wide Web.

MOTIVATION: The increasing availability of biological databases on the World-Wide Web and hypertext links between them has made a wealth of information easily accessible to biologists. Additional retrieval capabilities can be achieved by storing explicitly specified biological relationships between different entities as discrete database entries. RESULTS: We have built CySPID, a prototype database about the cytoskeleton that explores the approach of explicitly representing biological relationships. The stored relationships are displayed along with other retrieved information, can be used to make hyperlinks to related entities, and can be used to search for entities with specified properties. CySPID is extensible in that new types of relationships may be created without altering the database schema. AVAILABILITY: CySPID is available for public use (http://ycmi.med.yale.edu/cyspid/). The CGI scripts used by CySPID are available upon request.

Animals↗
PubMed2005

Routine database registration of biological therapy increases the reporting of adverse events twentyfold in clinical practice. First results from the Danish Database (DANBIO).

OBJECTIVE: To present from the Danish Database for Biological Therapies in Rheumatology (DANBIO) the frequencies and types of adverse events as well as risk factors during treatment with biological agents in clinical practice. METHODS: Adverse events during the first 2 years of clinical use of biological agents in Denmark were reported to the nationwide DANBIO and compared to the mandatory reports to the Danish Medicines Agency. RESULTS: Almost 90% of the patients treated with biological agents were registered in the DANBIO, and the database picked up 20 times as many adverse events as the Danish Medicines Agency. Infections and hypersensitivity reactions were the most prevalent adverse events. Age, disease duration, and previous number of disease-modifying anti-rheumatic drugs (DMARDS) were found to be risk factors for bacterial infections. CONCLUSION: A routine-based Danish database for biological therapies covers approximately 90% of patients and improves the reporting of adverse events.

Adult↗
PubMed2001

GOBASE: the organelle genome database.

GOBASE (http://megasun.bch.umontreal.ca/gobase/) is a network-accessible biological database, which is unique in bringing together diverse biological data on organelles with taxonomically broad coverage, and in furnishing data that have been exhaustively verified and completed by experts. So far, we have focused on mitochondrial data: GOBASE contains all published nucleotide and protein sequences encoded by mitochondrial genomes, selected RNA secondary structures of mitochondria-encoded molecules, genetic maps of completely sequenced genomes, taxonomic information for all species whose sequences are present in the database and organismal descriptions of key protistan eukaryotes. All of these data have been integrated and organized in a formal database structure to allow sophisticated biological queries using terms that are inherent in biological concepts. Most importantly, data have been validated, completed, corrected and standardized, a prerequisite of meaningful analysis. In addition, where critical data are lacking, such as genetic maps and RNA secondary structures, they are generated by the GOBASE team and collaborators, and added to the database. The database is implemented in a relational database management system, but features an object-oriented view of the biological data through a Web/Genera-generated World Wide Web interface. Finally, we have developed software for database curation (i.e. data updates, validation and correction), which will be described in some detail in this paper.

Animals↗
PubMed2006

Database integration for predisposition genes discovery.

Lightweight and flexible data integration is nowadays a great challenge in biology. Databases size increased exponentially, and biologists need tools to extract and merge interesting data coming from several heterogeneous and distributed sources over the Internet. Furthermore, there is a great need for data mediation on grid computing architectures. This paper is about a demonstration which goal is to show how Mediation, a data integration technique, can help in building such bioinformatics tools. We focused on research of genes functions, especially those involved in the resistance to malaria or those that may be considered as predisposition genes.

Computational Biology↗
PubMed1998

Zomit: biological data visualization and browsing.

MOTIVATION: The problems caused by the difficulty in visualizing and browsing biological databases have become crucial. Scientists can no longer interact directly with the huge amount of available data. However, future breakthroughs in biology depend on this interaction. We propose a new metaphor for biological data visualization and browsing that allows navigation in very large databases in an intuitive way. The concepts underlying our approach are based on navigation and visualization with zooming, semantic zooming and portals; and on data transformation via magic lenses. We think that these new visualization and navigation techniques should be applied globally to a federation of biological databases. RESULTS: We have implemented a generic tool, called Zomit, that provides an application programming interface for developing servers for such navigation and visualization, and a generic architecture-independent client (Javatrade mark applet) that queries such servers. As an illustration of the capabilities of our approach, we have developed ZoomMap, a prototype browser for the HuGeMap human genome map database. AVAILABILITY: Zomit and ZoomMap are available at the URL http://www.infobiogen.fr/services/zomit.

Biological Science Disciplines↗
PubMed2005

Lafora progressive Myoclonus Epilepsy mutation database-EPM2A and NHLRC1 (EPM2B) genes.

Progressive Myoclonus Epilepsy (PME) of the Lafora type is an autosomal recessive disease, which presents in teenage years with myoclonia and generalized seizures leading to death within a decade of onset. It is characterized by pathognomonic inclusions, Lafora bodies (LB), in neurons and other cell types. Two genes causing Lafora disease (LD), EPM2A on chromosome 6q24 and NHLRC1 (EPM2B) on chromosome 6p22.3 have been identified, and our recent results indicate there is at least one other gene causing the disease. The EPM2A gene product, laforin, is a protein tyrosine phosphatase (PTP) with a carbohydrate-binding domain (CBD) in the N-terminus. NHLRC1 encodes a protein named malin, containing a zinc finger of the RING type in the N-terminal half and 6 NHL-repeat domains in the C-terminal direction. To date 43 different variations in EPM2A and 23 in NHLRC1 are known, including missense, nonsense, frameshift, and deletions. We have developed a human LD mutation database using a new generic biological database cross-referencing platform. The database, which currently contains 66 entries is accessible on the World Wide Web (http://projects.tcag.ca/lafora). Entries can be submitted via the curator of the database or via a web-based form.

Adolescent↗
PubMed2004

The Mouse Genome Database (MGD): integrating biology with the genome.

The Mouse Genome Database (MGD) is one component of the Mouse Genome Informatics (MGI) system (http://www.informatics.jax.org), a community database resource for the laboratory mouse. MGD strives to provide a comprehensive knowledgebase about the mouse with experiments and data annotated from both literature and online sources. MGD curates and presents consensus and experimental data representations of genetic, genotype (sequence) and phenotype information including highly detailed reports about genes and gene products. Primary foci of integration are through representations of relationships between genes, sequences and phenotypes. MGD collaborates with other bioinformatics groups to curate a definitive set of information about the laboratory mouse and to build and implement the data and semantic standards that are essential for comparative genome analysis. Recent developments in MGD discussed here include an extensive integration of the mouse sequence data and substantial revisions in the presentation, query and visualization of sequence data.

Animals↗
PubMed1999

Kleisli: a new tool for data integration in biology.

One of the central problems in bioinformatics is data retrieval and integration. The existing biological databases are geographically distributed across the Internet, complex and heterogeneous in data types and data structures, and constantly changing. With the current rapid growth of biomedical data, the challenge is how large volumes of data retrieved from multiple databases can be transformed and integrated automatically and flexibly. This article describes a powerful new tool, the Kleisli system, for complex queries across multiple databases and data integration.

Computational Biology↗
PubMed2006

PhenoGO: assigning phenotypic context to gene ontology annotations with natural language processing.

Natural language processing (NLP) is a high throughput technology because it can process vast quantities of text within a reasonable time period. It has the potential to substantially facilitate biomedical research by extracting, linking, and organizing massive amounts of information that occur in biomedical journal articles as well as in textual fields of biological databases. Until recently, much of the work in biological NLP and text mining has revolved around recognizing the occurrence of biomolecular entities in articles, and in extracting particular relationships among the entities. Now, researchers have recognized a need to link the extracted information to ontologies or knowledge bases, which is a more difficult task. One such knowledge base is Gene Ontology annotations (GOA), which significantly increases semantic computations over the function, cellular components and processes of genes. For multicellular organisms, these annotations can be refined with phenotypic context, such as the cell type, tissue, and organ because establishing phenotypic contexts in which a gene is expressed is a crucial step for understanding the development and the molecular underpinning of the pathophysiology of diseases. In this paper, we propose a system, PhenoGO, which automatically augments annotations in GOA with additional context. PhenoGO utilizes an existing NLP system, called BioMedLEE, an existing knowledge-based phenotype organizer system (PhenOS) in conjunction with MeSH indexing and established biomedical ontologies. More specifically, PhenoGO adds phenotypic contextual information to existing associations between gene products and GO terms as specified in GOA. The system also maps the context to identifiers that are associated with different biomedical ontologies, including the UMLS, Cell Ontology, Mouse Anatomy, NCBI taxonomy, GO, and Mammalian Phenotype Ontology. In addition, PhenoGO was evaluated for coding of anatomical and cellular information and assigning the coded phenotypes to the correct GOA; results obtained show that PhenoGO has a precision of 91% and recall of 92%, demonstrating that the PhenoGO NLP system can accurately encode a large number of anatomical and cellular ontologies to GO annotations. The PhenoGO Database may be accessed at the following URL: http://www.phenoGO.org

Computational Biology↗
PubMed2001

Bioinformatic tools for DNA/protein sequence analysis, functional assignment of genes and protein classification.

The development of efficient DNA sequencing methods has led to the achievement of the DNA sequence of entire genomes from (to date) 55 prokaryotes, 5 eukaryotic organisms and 10 eukaryotic chromosomes. Thus, an enormous amount of DNA sequence data is available and even more will be forthcoming in the near future. Analysis of this overwhelming amount of data requires bioinformatic tools in order to identify genes that encode functional proteins or RNA. This is an important task, considering that even in the well-studied Escherichia coli more than 30% of the identified open reading frames are hypothetical genes. Future challenges of genome sequence analysis will include the understanding of gene regulation and metabolic pathway reconstruction including DNA chip technology, which holds tremendous potential for biomedicine and the biotechnological production of valuable compounds. The overwhelming volume of information often confuses scientists. This review intends to provide a guide to choosing the most efficient way to analyze a new sequence or to collect information on a gene or protein of interest by applying current publicly available databases and Web services. Recently developed tools that allow functional assignment of genes, mainly based on sequence similarity of the deduced amino acid sequence, using the currently available and increasing biological databases will be discussed.

Computational Biology↗