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PubMed2006

Curation of complex, context-dependent immunological data.

BACKGROUND: The Immune Epitope Database and Analysis Resource (IEDB) is dedicated to capturing, housing and analyzing complex immune epitope related data http://www.immuneepitope.org. DESCRIPTION: To identify and extract relevant data from the scientific literature in an efficient and accurate manner, novel processes were developed for manual and semi-automated annotation. CONCLUSION: Formalized curation strategies enable the processing of a large volume of context-dependent data, which are now available to the scientific community in an accessible and transparent format. The experiences described herein are applicable to other databases housing complex biological data and requiring a high level of curation expertise.

Allergy and Immunology↗
PubMed2006

Resources for integrative systems biology: from data through databases to networks and dynamic system models.

In systems biology, biologically relevant quantitative modelling of physiological processes requires the integration of experimental data from diverse sources. Recent developments in high-throughput methodologies enable the analysis of the transcriptome, proteome, interactome, metabolome and phenome on a previously unprecedented scale, thus contributing to the deluge of experimental data held in numerous public databases. In this review, we describe some of the databases and simulation tools that are relevant to systems biology and discuss a number of key issues affecting data integration and the challenges these pose to systems-level research.

Animals↗
PubMed2004

MIPS Arabidopsis thaliana Database (MAtDB): an integrated biological knowledge resource for plant genomics.

Arabidopsis thaliana is the most widely studied model plant. Functional genomics is intensively underway in many laboratories worldwide. Beyond the basic annotation of the primary sequence data, the annotated genetic elements of Arabidopsis must be linked to diverse biological data and higher order information such as metabolic or regulatory pathways. The MIPS Arabidopsis thaliana database MAtDB aims to provide a comprehensive resource for Arabidopsis as a genome model that serves as a primary reference for research in plants and is suitable for transfer of knowledge to other plants, especially crops. The genome sequence as a common backbone serves as a scaffold for the integration of data, while, in a complementary effort, these data are enhanced through the application of state-of-the-art bioinformatics tools. This information is visualized on a genome-wide and a gene-by-gene basis with access both for web users and applications. This report updates the information given in a previous report and provides an outlook on further developments. The MAtDB web interface can be accessed at http://mips.gsf.de/proj/thal/db.

Arabidopsis↗
PubMed2002

SeqHound: biological sequence and structure database as a platform for bioinformatics research.

BACKGROUND: SeqHound has been developed as an integrated biological sequence, taxonomy, annotation and 3-D structure database system. It provides a high-performance server platform for bioinformatics research in a locally-hosted environment. RESULTS: SeqHound is based on the National Center for Biotechnology Information data model and programming tools. It offers daily updated contents of all Entrez sequence databases in addition to 3-D structural data and information about sequence redundancies, sequence neighbours, taxonomy, complete genomes, functional annotation including Gene Ontology terms and literature links to PubMed. SeqHound is accessible via a web server through a Perl, C or C++ remote API or an optimized local API. It provides functionality necessary to retrieve specialized subsets of sequences, structures and structural domains. Sequences may be retrieved in FASTA, GenBank, ASN.1 and XML formats. Structures are available in ASN.1, XML and PDB formats. Emphasis has been placed on complete genomes, taxonomy, domain and functional annotation as well as 3-D structural functionality in the API, while fielded text indexing functionality remains under development. SeqHound also offers a streamlined WWW interface for simple web-user queries. CONCLUSIONS: The system has proven useful in several published bioinformatics projects such as the BIND database and offers a cost-effective infrastructure for research. SeqHound will continue to develop and be provided as a service of the Blueprint Initiative at the Samuel Lunenfeld Research Institute. The source code and examples are available under the terms of the GNU public license at the Sourceforge site http://sourceforge.net/projects/slritools/ in the SLRI Toolkit.

Amino Acid Sequence↗
PubMed2000

RSDB: representative protein sequence databases have high information content.

MOTIVATION: Biological sequence databases are highly redundant for two main reasons: 1. various databanks keep redundant sequences with many identical and nearly identical sequences 2. natural sequences often have high sequence identities due to gene duplication. We wanted to know how many sequences can be removed before the databases start losing homology information. Can a database of sequences with mutual sequence identity of 50% or less provide us with the same amount of biological information as the original full database? RESULTS: Comparisons of nine representative sequence databases (RSDB) derived from full protein databanks showed that the information content of sequence databases is not linearly proportional to its size. An RSDB reduced to mutual sequence identity of around 50% (RSDB50) was equivalent to the original full database in terms of the effectiveness of homology searching. It was a third of the full database size which resulted in a six times faster iterative profile searching. The RSDBs are produced at different granularity for efficient homology searching. AVAILABILITY: All the RSDB files generated and the full analysis results are available through internet: ftp://ftp.ebi.ac. uk/pub/contrib/jong/RSDB/http://cyrah.e bi.ac.uk:1111/Proj/Bio/RSDB

Algorithms↗
PubMed2006

QxDB: a generic database to support mathematical modelling in biology.

QxDB (quantitative x-modelling database) is a web-based generic database package designed especially to house quantitative and structural information. Its development was motivated by the need for centralized access to such results for development of mathematical models, but its usefulness extends to the general research community of both modellers and experimentalists. Written in PHP (Hyper Preprocessor) and MYSQL, the database is easily adapted to new fields of research and ported to Apache-based web servers. Unlike most existing databases, experimental and observational results curated in QxDB are supplemented by comments from the experts who contribute input to the database, giving their evaluations of experimental techniques, breadth of validity of results, experimental conditions, and the like, thus providing the visitor with a basis for gauging the quality (or appropriateness) of each item for his/her needs. QxDB can be easily customized by adapting the contents of the database table containing the descriptors that characterize each data record according to an informal ontology of the research domain. We will illustrate this adaptability of QxDB by presenting two examples, the first dealing with modelling in oncology and the second with mechanical properties of cells and tissues.

Biology↗
PubMed2000

Bioinformatics in protein analysis.

The chapter gives an overview of bioinformatic techniques of importance in protein analysis. These include database searches, sequence comparisons and structural predictions. Links to useful World Wide Web (WWW) pages are given in relation to each topic. Databases with biological information are reviewed with emphasis on databases for nucleotide sequences (EMBL, GenBank, DDBJ), genomes, amino acid sequences (Swissprot, PIR, TrEMBL, GenePept), and three-dimensional structures (PDB). Integrated user interfaces for databases (SRS and Entrez) are described. An introduction to databases of sequence patterns and protein families is also given (Prosite, Pfam, Blocks). Furthermore, the chapter describes the widespread methods for sequence comparisons, FASTA and BLAST, and the corresponding WWW services. The techniques involving multiple sequence alignments are also reviewed: alignment creation with the Clustal programs, phylogenetic tree calculation with the Clustal or Phylip packages and tree display using Drawtree, njplot or phylo_win. Finally, the chapter also treats the issue of structural prediction. Different methods for secondary structure predictions are described (Chou-Fasman, Garnier-Osguthorpe-Robson, Predator, PHD). Techniques for predicting membrane proteins, antigenic sites and postranslational modifications are also reviewed.

Computational Biology↗
PubMed2004

Saccharomyces Genome Database (SGD) provides tools to identify and analyze sequences from Saccharomyces cerevisiae and related sequences from other organisms.

The Saccharomyces Genome Database (SGD; http://www.yeastgenome.org/), a scientific database of the molecular biology and genetics of the yeast Saccharomyces cerevisiae, has recently developed several new resources that allow the comparison and integration of information on a genome-wide scale, enabling the user not only to find detailed information about individual genes, but also to make connections across groups of genes with common features and across different species. The Fungal Alignment Viewer displays alignments of sequences from multiple fungal genomes, while the Sequence Similarity Query tool displays PSI-BLAST alignments of each S.cerevisiae protein with similar proteins from any species whose sequences are contained in the non-redundant (nr) protein data set at NCBI. The Yeast Biochemical Pathways tool integrates groups of genes by their common roles in metabolism and displays the metabolic pathways in a graphical form. Finally, the Find Chromosomal Features search interface provides a versatile tool for querying multiple types of information in SGD.

Amino Acid Sequence↗
PubMed2006

The Candida Genome Database: facilitating research on Candida albicans molecular biology.

The Candida Genome Database (CGD; http://www.candidagenome.org) is a resource for information about the Candida albicans genomic sequence and the molecular biology of its encoded gene products. CGD collects and organizes data from the biological literature concerning C. albicans, and provides tools for viewing, searching, analysing, and downloading these data. CGD also serves as an organizing centre for the C. albicans research community, providing a gene-name registry, contact information, and research community news. This article describes the information contained in CGD and how to access it, either from the perspective of a bench scientist interested in the function of one or a few genes, or from the perspective of a biologist or bioinformatician interpreting large-scale functional genomic datasets.

Alleles↗