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At least 379 records · Page 21Linked to original sources
PubMed2004

ApiEST-DB: analyzing clustered EST data of the apicomplexan parasites.

ApiEST-DB (http://www.cbil.upenn.edu/paradbs-servlet/) provides integrated access to publicly available EST data from protozoan parasites in the phylum Apicomplexa. The database currently incorporates a total of nearly 100,000 ESTs from several parasite species of clinical and/or veterinary interest, including Eimeria tenella, Neospora caninum, Plasmodium falciparum, Sarcocystis neurona and Toxoplasma gondii. To facilitate analysis of these data, EST sequences were clustered and assembled to form consensus sequences for each organism, and these assemblies were then subjected to automated annotation via similarity searches against protein and domain databases. The underlying relational database infrastructure, Genomics Unified Schema (GUS), enables complex biologically based queries, facilitating validation of gene models, identification of alternative splicing, detection of single nucleotide polymorphisms, identification of stage-specific genes and recognition of phylogenetically conserved and phylogenetically restricted sequences.

Animals↗
PubMed2005

A strategy for integrative computational physiology.

Organ function (the heart beat for example) can only be understood through knowledge of molecular and cellular processes within the constraints of structure-function relations at the tissue level. A quantitative modeling framework that can deal with these multiscale issues is described here under the banner of the International Union of Physiological Sciences Physiome Project.

Cell Physiological Phenomena↗
PubMed2001

GlycoSuiteDB: a new curated relational database of glycoprotein glycan structures and their biological sources.

GlycoSuiteDB is a relational database that curates information from the scientific literature on glyco-protein derived glycan structures, their biological sources, the references in which the glycan was described and the methods used to determine the glycan structure. To date, the database includes most published O:-linked oligosaccharides from the last 50 years and most N:-linked oligosaccharides that were published in the 1990s. For each structure, information is available concerning the glycan type, linkage and anomeric configuration, mass and composition. Detailed information is also provided on native and recombinant sources, including tissue and/or cell type, cell line, strain and disease state. Where known, the proteins to which the glycan structures are attached are reported, and cross-references to the SWISS-PROT/TrEMBL protein sequence databases are given if applicable. The GlycoSuiteDB annotations include literature references which are linked to PubMed, and detailed information on the methods used to determine each glycan structure are noted to help the user assess the quality of the structural assignment. GlycoSuiteDB has a user-friendly web interface which allows the researcher to query the database using mono-isotopic or average mass, monosaccharide composition, glycosylation linkages (e.g. N:- or O:-linked), reducing terminal sugar, attached protein, taxonomy, tissue or cell type and GlycoSuiteDB accession number. Advanced queries using combinations of these parameters are also possible. GlycoSuiteDB can be accessed on the web at http://www.glycosuite.com.

Animals↗
PubMed2004

Application of predictive QSAR models to database mining: identification and experimental validation of novel anticonvulsant compounds.

We have developed a drug discovery strategy that employs variable selection quantitative structure-activity relationship (QSAR) models for chemical database mining. The approach starts with the development of rigorously validated QSAR models obtained with the variable selection k nearest neighbor (kNN) method (or, in principle, with any other robust model-building technique). Model validation is based on several statistical criteria, including the randomization of the target property (Y-randomization), independent assessment of the training set model's predictive power using external test sets, and the establishment of the model's applicability domain. All successful models are employed in database mining concurrently; in each case, only variables selected as a result of model building (termed descriptor pharmacophore) are used in chemical similarity searches comparing active compounds of the training set (queries) with those in chemical databases. Specific biological activity (characteristic of the training set compounds) of external database entries found to be within a predefined similarity threshold of the training set molecules is predicted on the basis of the validated QSAR models using the applicability domain criteria. Compounds judged to have high predicted activities by all or the majority of all models are considered as consensus hits. We report on the application of this computational strategy for the first time for the discovery of anticonvulsant agents in the Maybridge and National Cancer Institute (NCI) databases containing ca. 250,000 compounds combined. Forty-eight anticonvulsant agents of the functionalized amino acid (FAA) series were used to build kNN variable selection QSAR models. The 10 best models were applied to mining chemical databases, and 22 compounds were selected as consensus hits. Nine compounds were synthesized and tested at the NIH Epilepsy Branch, Rockville, MD using the same biological test that was employed to assess the anticonvulsant activity of the training set compounds; of these nine, four were exact database hits and five were derived from the hits by minor chemical modifications. Seven of these nine compounds were confirmed to be active, indicating an exceptionally high hit rate. The approach described in this report can be used as a general rational drug discovery tool.

Amides↗
PubMed1997

Adding semantics to genome databases: towards an ontology for molecular biology.

Molecular biology has a communication problem. There are many databases using their own labels and categories for storing data objects and some using identical labels and categories but with a different meaning. Conversely, one concept is often found under different names. Prominent examples are the concepts "gene" and "protein sequence" which are used with different semantics by major international genomic and protein databases thereby making database integration difficult and strenuous. This situation can only be improved by either defining individual semantic interfaces between each pair of databases (complexity of order n2) or by implementing one agreeable, transparent and computationally tractable semantic repository and linking each database to it (complexity of order n). Ontologies are one means to provide such semantic repository by explicitly specifying the meaning of and relation between the fundamental concepts in an application domain. Here, heuristics for building an ontology and the upper level and a database branch of a prospective Ontology for Molecular Biology are presented and compared to other ontologies with respect to suitability for molecular biology (http:/(/)igd.rz-berlin.mpg.de/www/oe/mbo.html).

Computational Biology↗
PubMed2003

Computational methods of analysis of protein-protein interactions.

Computational methods play an important role at all stages of the process of determining protein-protein interactions. They are used to predict potential interactions, to validate the results of high-throughput interaction screens and to analyze the protein networks inferred from interaction databases.

Computational Biology↗
PubMed1999

Using the Saccharomyces Genome Database (SGD) for analysis of protein similarities and structure.

The Saccharomyces Genome Database (SGD) collects and organizes information about the molecular biology and genetics of the yeast Saccharomyces cerevisiae. The latest protein structure and comparison tools available at SGD are presented here. With the completion of the yeast sequence and the Caenorhabditis elegans sequence soon to follow, comparison of proteins from complete eukaryotic proteomes will be an extremely powerful way to learn more about a particular protein's structure, its function, and its relationships with other proteins. SGD can be accessed through the World Wide Web at http://genome-www.stanford.edu/Saccharomyces/

Computational Biology↗
PubMed2006

Genomic scale sub-family assignment of protein domains.

Many classification schemes for proteins and domains are either hierarchical or semi-hierarchical yet most databases, especially those offering genome-wide analysis, only provide assignments to sequences at one level of their hierarchy. Given an established hierarchy, the problem of assigning new sequences to lower levels of that existing hierarchy is less hard (but no less important) than the initial top level assignment which requires the detection of the most distant relationships. A solution to this problem is described here in the form of a new procedure which can be thought of as a hybrid between pairwise and profile methods. The hybrid method is a general procedure that can be applied to any pre-defined hierarchy, at any level, including in principle multiple sub-levels. It has been tested on the SCOP classification via the SUPERFAMILY database and performs significantly better than either pairwise or profile methods alone. Perhaps the greatest advantage of the hybrid method over other possible approaches to the problem is that within the framework of an existing profile library, the assignments are fully automatic and come at almost no additional computational cost. Hence it has already been applied at the SCOP family level to all genomes in the SUPERFAMILY database, providing a wealth of new data to the biological and bioinformatics communities.

Computational Biology↗
PubMed2002

PRESTA: associating promoter sequences with information on gene expression.

BACKGROUND: Large sets of well-characterized promoter sequences are required to facilitate the understanding of promoter architecture. The major sequence databases are a prospective source of upstream regulatory regions, but suffer from inaccurate annotation. The software tool PRESTA (PRomoter EST Association) presented in this study is designed for efficient recovery of characterized and partially verified promoters from GenBank and EMBL libraries. RESULTS: The PRESTA algorithm examines the putative GenBank/EMBL promoters and automatically removes most of the poorly annotated entries. The remaining records are connected to expressed sequence tags (ESTs) through a high-stringency BLAST search. The frequency and source of recovered ESTs provide an estimate of the activity and expression pattern of the promoter, and the ESTs' 5' ends assist in transcription start-site verification. The PRESTA database provides easy access to non-redundant upstream regulatory regions recently extracted by the PRESTA algorithm. The current size of this resource is 552 human and 241 mouse promoters. Surprisingly, no overlap between the PRESTA database and the Eukaryotic Promoter Database (EPD) was detected by sequence comparison. CONCLUSIONS: The PRESTA algorithm demonstrates the principle of promoter verification by mapping EST 5' ends. The publicly available PRESTA database collects hundreds of characterized and partially verified promoter sequences and is complementary to other promoter databases.

Algorithms↗
PubMed1999

BOLD--a biological O-linked glycan database.

Glycans can be O-linked to proteins via the hydroxyl group of serine, threonine, tyrosine, hydroxylysine or hydroxyproline. Sometimes the glycan is O-linked to the hydroxyl group via a phosphodiester bond. The core monosaccharide residue may be N-acetylgalactosamine, N-acetylglucosamine, galactose, glucose, fucose, mannose, xylose or arabinose. These O-linked glycans can remain as a monosaccharide, but often a complex structure is built up by stepwise addition of monosaccharides. Monosaccharides known to be added include galactose, N-acetylglucosamine, fucose, N-acetylneuraminic acid, N-glycolylneuraminic acid and 2-keto-3-deoxynonulosonic acid. O-linked glycans can also contain sulfate and phosphate residues. This leads to the possibility of the existence of numerous O-glycan structures. The biological O-linked database (BOLD) is a relational database that contains information on O-linked glycan structures, their biological sources (with a link to the SWISS-PROT protein database), the references in which the glycan was described (with a link to MEDLINE), and the methods used to determine the glycan structure. The database provides a valuable resource for glycobiology researchers interested in O-linked oligosaccharide structures that have been previously described on proteins from different species and tissues.

Carbohydrate Conformation↗
PubMed2003

Pathways database system: an integrated system for biological pathways.

MOTIVATION: During the next phase of the Human Genome Project, research will focus on functional studies of attributing functions to genes, their regulatory elements, and other DNA sequences. To facilitate the use of genomic information in such studies, a new modeling perspective is needed to examine and study genome sequences in the context of many kinds of biological information. Pathways are the logical format for modeling and presenting such information in a manner that is familiar to biological researchers. RESULTS: In this paper we present an integrated system, called Pathways Database System, with a set of software tools for modeling, storing, analyzing, visualizing, and querying biological pathways data at different levels of genetic, molecular, biochemical and organismal detail. The novel features of the system include: (a) genomic information integrated with other biological data and presented from a pathway, rather than from the DNA sequence, perspective; (b) design for biologists who are possibly unfamiliar with genomics, but whose research is essential for annotating gene and genome sequences with biological functions; (c) database design, implementation and graphical tools which enable users to visualize pathways data in multiple abstraction levels, and to pose predetermined queries; and (d) an implementation that allows for web(XML)-based dissemination of query outputs (i.e. pathways data) to researchers in the community, giving them control on the use of pathways data. AVAILABILITY: Available on request from the authors.

Database Management Systems↗