5,151 research outputs found
bdbms -- A Database Management System for Biological Data
Biologists are increasingly using databases for storing and managing their
data. Biological databases typically consist of a mixture of raw data,
metadata, sequences, annotations, and related data obtained from various
sources. Current database technology lacks several functionalities that are
needed by biological databases. In this paper, we introduce bdbms, an
extensible prototype database management system for supporting biological data.
bdbms extends the functionalities of current DBMSs to include: (1) Annotation
and provenance management including storage, indexing, manipulation, and
querying of annotation and provenance as first class objects in bdbms, (2)
Local dependency tracking to track the dependencies and derivations among data
items, (3) Update authorization to support data curation via content-based
authorization, in contrast to identity-based authorization, and (4) New access
methods and their supporting operators that support pattern matching on various
types of compressed biological data types. This paper presents the design of
bdbms along with the techniques proposed to support these functionalities
including an extension to SQL. We also outline some open issues in building
bdbms.Comment: This article is published under a Creative Commons License Agreement
(http://creativecommons.org/licenses/by/2.5/.) You may copy, distribute,
display, and perform the work, make derivative works and make commercial use
of the work, but, you must attribute the work to the author and CIDR 2007.
3rd Biennial Conference on Innovative Data Systems Research (CIDR) January
710, 2007, Asilomar, California, US
Graph theoretic methods for the analysis of structural relationships in biological macromolecules
Subgraph isomorphism and maximum common subgraph isomorphism algorithms from graph theory provide an effective and an efficient way of identifying structural relationships between biological macromolecules. They thus provide a natural complement to the pattern matching algorithms that are used in bioinformatics to identify sequence relationships. Examples are provided of the use of graph theory to analyze proteins for which three-dimensional crystallographic or NMR structures are available, focusing on the use of the Bron-Kerbosch clique detection algorithm to identify common folding motifs and of the Ullmann subgraph isomorphism algorithm to identify patterns of amino acid residues. Our methods are also applicable to other types of biological macromolecule, such as carbohydrate and nucleic acid structures
Chemoinformatics Research at the University of Sheffield: A History and Citation Analysis
This paper reviews the work of the Chemoinformatics Research Group in the Department of Information Studies at the University of Sheffield, focusing particularly on the work carried out in the period 1985-2002. Four major research areas are discussed, these involving the development of methods for: substructure searching in databases of three-dimensional structures, including both rigid and flexible molecules; the representation and searching of the Markush structures that occur in chemical patents; similarity searching in databases of both two-dimensional and three-dimensional structures; and compound selection and the design of combinatorial libraries. An analysis of citations to 321 publications from the Group shows that it attracted a total of 3725 residual citations during the period 1980-2002. These citations appeared in 411 different journals, and involved 910 different citing organizations from 54 different countries, thus demonstrating the widespread impact of the Group's work
Comprehensive structural classification of ligand binding motifs in proteins
Comprehensive knowledge of protein-ligand interactions should provide a
useful basis for annotating protein functions, studying protein evolution,
engineering enzymatic activity, and designing drugs. To investigate the
diversity and universality of ligand binding sites in protein structures, we
conducted the all-against-all atomic-level structural comparison of over
180,000 ligand binding sites found in all the known structures in the Protein
Data Bank by using a recently developed database search and alignment
algorithm. By applying a hybrid top-down-bottom-up clustering analysis to the
comparison results, we determined approximately 3000 well-defined structural
motifs of ligand binding sites. Apart from a handful of exceptions, most
structural motifs were found to be confined within single families or
superfamilies, and to be associated with particular ligands. Furthermore, we
analyzed the components of the similarity network and enumerated more than 4000
pairs of ligand binding sites that were shared across different protein folds.Comment: 13 pages, 8 figure
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