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PubMed1999

KEGG: Kyoto Encyclopedia of Genes and Genomes.

Kyoto Encyclopedia of Genes and Genomes (KEGG) is a knowledge base for systematic analysis of gene functions in terms of the networks of genes and molecules. The major component of KEGG is the PATHWAY database that consists of graphical diagrams of biochemical pathways including most of the known metabolic pathways and some of the known regulatory pathways. The pathway information is also represented by the ortholog group tables summarizing orthologous and paralogous gene groups among different organisms. KEGG maintains the GENES database for the gene catalogs of all organisms with complete genomes and selected organisms with partial genomes, which are continuously re-annotated, as well as the LIGAND database for chemical compounds and enzymes. Each gene catalog is associated with the graphical genome map for chromosomal locations that is represented by Java applet. In addition to the data collection efforts, KEGG develops and provides various computational tools, such as for reconstructing biochemical pathways from the complete genome sequence and for predicting gene regulatory networks from the gene expression profiles. The KEGG databases are daily updated and made freely available (http://www.genome.ad.jp/kegg/).

Animals↗

PubMed2004

IMGT/3Dstructure-DB and IMGT/StructuralQuery, a database and a tool for immunoglobulin, T cell receptor and MHC structural data.

IMGT/3Dstructure-DB and IMGT/Structural-Query are a novel 3D structure database and a new tool for immunological proteins. They are part of IMGT, the international ImMunoGenetics information system, a high-quality integrated knowledge resource specializing in immunoglobulins (IG), T cell receptors (TR), major histocompatibility complex (MHC) and related proteins of the immune system (RPI) of human and other vertebrate species, which consists of databases, Web resources and interactive on-line tools. IMGT/3Dstructure-DB data are described according to the IMGT Scientific chart rules based on the IMGT-ONTOLOGY concepts. IMGT/3Dstructure-DB provides IMGT gene and allele identification of IG, TR and MHC proteins with known 3D structures, domain delimitations, amino acid positions according to the IMGT unique numbering and renumbered coordinate flat files. Moreover IMGT/3Dstructure-DB provides 2D graphical representations (or Collier de Perles) and results of contact analysis. The IMGT/StructuralQuery tool allows search of this database based on specific structural characteristics. IMGT/3Dstructure-DB and IMGT/StructuralQuery are freely available at http://imgt. cines.fr.

Animals↗

PubMed2001

A general empirical model of protein evolution derived from multiple protein families using a maximum-likelihood approach.

Phylogenetic inference from amino acid sequence data uses mainly empirical models of amino acid replacement and is therefore dependent on those models. Two of the more widely used models, the Dayhoff and JTT models, are estimated using similar methods that can utilize large numbers of sequences from many unrelated protein families but are somewhat unsatisfactory because they rely on assumptions that may lead to systematic error and discard a large amount of the information within the sequences. The alternative method of maximum-likelihood estimation may utilize the information in the sequence data more efficiently and suffers from no systematic error, but it has previously been applicable to relatively few sequences related by a single phylogenetic tree. Here, we combine the best attributes of these two methods using an approximate maximum-likelihood method. We implemented this approach to estimate a new model of amino acid replacement from a database of globular protein sequences comprising 3,905 amino acid sequences split into 182 protein families. While the new model has an overall structure similar to those of other commonly used models, there are significant differences. The new model outperforms the Dayhoff and JTT models with respect to maximum-likelihood values for a large majority of the protein families in our database. This suggests that it provides a better overall fit to the evolutionary process in globular proteins and may lead to more accurate phylogenetic tree estimates. Potentially, this matrix, and the methods used to generate it, may also be useful in other areas of research, such as biological sequence database searching, sequence alignment, and protein structure prediction, for which an accurate description of amino acid replacement is required.

Algorithms↗

PubMed2004

Presentation of differentially regulated proteins within a web-accessible proteome database system of microorganisms.

Web-accessible proteome databases represent indispensable tools for quantitative and comparative proteomics research. The majority of two-dimensional gel electrophoresis (2-DE) databases contains clickable 2-DE gel images and descriptive textual information such as protein name, Mr/pI values, methods of identification, cellular localization and other information on proteins. Although a great part of the work in comparative proteomics consists of the analysis of 2-DE gels using image analysis approaches, most proteome databases lack the ability to present protein abundance data and their alterations within experiments via the web. Now, differentially regulated proteins detected in microbial experiments by quantitative gel image analysis are presented in a web-accessible relational database DIFF (Differentially Regulated Proteins). The DIFF database is a part of the proteome database system for microbial research available at http://www.mpiib-berlin.mpg.de/2D-PAGE.

Bacterial Proteins↗

PubMed2001

Selection of optimal DNA oligos for gene expression arrays.

MOTIVATION: High density DNA oligo microarrays are widely used in biomedical research. Selection of optimal DNA oligos that are deposited on the microarrays is critical. Based on sequence information and hybridization free energy, we developed a new algorithm to select optimal short (20-25 bases) or long (50 or 70 bases) oligos from genes or open reading frames (ORFs) and predict their hybridization behavior. Having optimized probes for each gene is valuable for two reasons. By minimizing background hybridization they provide more accurate determinations of true expression levels. Having optimum probes minimizes the number of probes needed per gene, thereby decreasing the cost of each microarray, raising the number of genes on each chip and increasing its usage. RESULTS: In this paper we describe algorithms to optimize the selection of specific probes for each gene in an entire genome. The criteria for truly optimum probes are easily stated but they are not computable at all levels currently. We have developed an heuristic approach that is efficiently computable at all levels and should provide a good approximation to the true optimum set. We have run the program on the complete genomes for several model organisms and deposited the results in a database that is available on-line (http://ural.wustl.edu/~lif/probe.pl). AVAILABILITY: The program is available upon request.

Algorithms↗

PubMed2006

Advancing glycomics: implementation strategies at the consortium for functional glycomics.

Glycomics-an integrated approach to study structure-function relationships of complex carbohydrates (or glycans)-is an emerging field in this age of post-genomics. Realizing the importance of glycomics, many large scale research initiatives have been established to generate novel resources and technologies to advance glycomics. These initiatives are generating and cataloging diverse data sets necessitating the development of bioinformatic platforms to acquire, integrate, and disseminate these data sets in a meaningful fashion. With the consortium for functional glycomics (CFG) as the model system, this review discusses databases and the bioinformatics platform developed by this consortium to advance glycomics.

Animals↗

PubMed2004

VirGen: a comprehensive viral genome resource.

VirGen is a comprehensive viral genome resource that organizes the 'sequence space' of viral genomes in a structured fashion. It has been developed with the objective of serving as an annotated and curated database comprising complete genome sequences of viruses, value-added derived data and data mining tools. The current release (v1.1) contains 559 complete genomes in addition to 287 putative genomes of viruses belonging to eight viral families for which the host range includes animals and plants. Viral genomes in VirGen are annotated using sequence-based Bioinformatics approaches. The genomic data is also curated to identify 'alternate names' of viral proteins, where available. VirGen archives the results of comparisons of genomes, proteomes and individual proteins within and between viral species. It is the first resource to provide phylogenetic trees of viral species computed using whole-genome sequence data. The module of predicted B-cell antigenic determinants in VirGen is an attempt to link the genome to its vaccinome. Comparative genome analysis data facilitate the study of genome organization and evolution of viruses, which would have implications in applied research to identify candidates for the design of vaccines and antiviral drugs. VirGen is a relational database and is available at http://bioinfo. ernet.in/virgen/virgen.html.

Antigens, Viral↗

PubMed2004

MitoP2, an integrated database on mitochondrial proteins in yeast and man.

The aim of the MitoP2 database (http://ihg.gsf.de/mitop2) is to provide a comprehensive list of mitochondrial proteins of yeast and man. Based on the current literature we created an annotated reference set of yeast and human proteins. In addition, data sets relevant to the study of the mitochondrial proteome are integrated and accessible via search tools and links. They include computational predictions of signalling sequences, and summarize results from proteome mapping, mutant screening, expression profiling, protein-protein interaction and cellular sublocalization studies. For each individual approach, specificity and sensitivity for allocating mitochondrial proteins was calculated. By providing the evidence for mitochondrial candidate proteins the MitoP2 database lends itself to the genetic characterization of human mitochondriopathies.

Computational Biology↗

PubMed2003

Identification of biological relationships from text documents using efficient computational methods.

The biological literature databases continue to grow rapidly with vital information that is important for conducting sound biomedical research and development. The current practices of manually searching for information and extracting pertinent knowledge are tedious, time-consuming tasks even for motivated biological researchers. Accurate and computationally efficient approaches in discovering relationships between biological objects from text documents are important for biologists to develop biological models. The term "object" refers to any biological entity such as a protein, gene, cell cycle, etc. and relationship refers to any dynamic action one object has on another, e.g. protein inhibiting another protein or one object belonging to another object such as, the cells composing an organ. This paper presents a novel approach to extract relationships between multiple biological objects that are present in a text document. The approach involves object identification, reference resolution, ontology and synonym discovery, and extracting object-object relationships. Hidden Markov Models (HMMs), dictionaries, and N-Gram models are used to set the framework to tackle the complex task of extracting object-object relationships. Experiments were carried out using a corpus of one thousand Medline abstracts. Intermediate results were obtained for the object identification process, synonym discovery, and finally the relationship extraction. For the thousand abstracts, 53 relationships were extracted of which 43 were correct, giving a specificity of 81 percent. These results are promising for multi-object identification and relationship finding from biological documents.

Algorithms↗

PubMed2006

In silico Northern blot, an automated method to determine expression patterns from EST databases, reveals tissue specificity of murine 17beta-hydroxysteroid dehydrogenase type 11.

In this study, we developed an automated in silico Northern blot (ISNB) for analysis of gene expression patterns from EST databases. This kind of analysis can facilitate initial enzyme characterization by providing tissue distribution patterns. For proof of principle, we analyzed the expression pattern of well-characterized murine 17beta-hydroxysteroid dehydrogenases type 1 and 4. Less characterized murine 17beta-hydroxysteroid dehydrogenase type 11 (Dhrs 8) also was included and processed bioinformatically (ISNB) and further analyzed by Northern blot. The 17beta-hydroxysteroid dehydrogenase type 11 showed the same wide expression pattern by in silico and by wet laboratory approaches. The data point to its involvement of the enzyme in lipid metabolism. The quality of the ISNB relies on the quality of EST-databases. However, our approach is an easy and versatile tool of potentially universal application.

17-Hydroxysteroid Dehydrogenases↗

PubMed2004

A local alignment metric for accelerating biosequence database search.

We introduce a metric for local sequence alignments that has utility for accelerating optimal alignment searches without loss of sensitivity. The metric's triangle inequality property permits identification of redundant database entries guaranteed to have optimal alignments to the query sequence that fall below a specified score threshold, thereby permitting comparisons to these entries to be skipped. We prove the existence of the metric for a variety of scoring systems, including the most commonly used ones, and show that a triangle inequality can be established as well for nucleotide-to-protein sequence comparisons. We discuss a database clustering and search strategy that takes advantage of the triangle inequality. The strategy permits moderate but significant acceleration of searches against the widely used "nr" protein database. It also provides a theoretically based method for database clustering in general and provides a standard against which to compare heuristic clustering strategies.

Algorithms↗

PubMed2004

Rice Proteome Database based on two-dimensional polyacrylamide gel electrophoresis: its status in 2003.

The Rice Proteome Database is the first detailed database to describe the proteome of rice. The current release contains 21 reference maps based on two-dimensional polyacrylamide gel electrophoresis (2D-PAGE) of proteins from rice tissues and subcellular compartments. These reference maps comprise 11 941 identified proteins showing tissue and subcellular localization, corresponding to 4180 separate protein entries in the database. The Rice Proteome Database contains the calculated properties of each protein such as molecular weight, isoelectric point and expression; experimentally determined properties such as amino acid sequences obtained using protein sequencers and mass spectrometry; and the results of database searches such as sequence homologies. The database is searchable by keyword, accession number, protein name, isoelectric point, molecular weight and amino acid sequence, or by selection of a spot on one of the 2D-PAGE reference maps. Cross-references are provided to tools for proteomics and to other 2D-PAGE databases, which in turn provide many links to other molecular databases. The information in the Rice Proteome Database is updated weekly, and is available on the World Wide Web at http://gene64.dna.affrc.go.jp/RPD/.

Computational Biology↗

PubMed2004

DSDBASE: a consortium of native and modelled disulphide bonds in proteins.

DSDBASE is a database of disulphide bonds in proteins, which provides information on native disulphides and those that are stereochemically possible between pairs of residues for all known protein structural entries. The modelling of disulphides has been performed, using MODIP, by the identification of residue pairs that can strainlessly accommodate a covalent cross-link. We also assess the stereochemical quality of the covalent cross-link and grade them appropriately. One of the potential uses of the database is to design site-directed mutants in order to enhance the thermal stability of a protein. The proposed sites of mutations can be viewed specifically with respect to active sites of enzymes and across physiological dimers. The occurrence of native and modelled disulphides increases the dimensions of the database enormously. This database can also be employed for proposing three-dimensional models of disulphide-rich short polypeptides. The database can be accessed from http://www.ncbs.res.in/ approximately faculty/mini/dsdbase/dsdbase.html. Supplementary information can be accessed from http://www.ncbs.res.in/ approximately faculty/mini/dsdbase/nar/suppl.htm.

Animals↗

PubMed2003

ELISA: structure-function inferences based on statistically significant and evolutionarily inspired observations.

UNLABELLED: The problem of functional annotation based on homology modeling is primary to current bioinformatics research. Researchers have noted regularities in sequence, structure and even chromosome organization that allow valid functional cross-annotation. However, these methods provide a lot of false negatives due to limited specificity inherent in the system. We want to create an evolutionarily inspired organization of data that would approach the issue of structure-function correlation from a new, probabilistic perspective. Such organization has possible applications in phylogeny, modeling of functional evolution and structural determination. ELISA (Evolutionary Lineage Inferred from Structural Analysis, http://romi.bu.edu/elisa) is an online database that combines functional annotation with structure and sequence homology modeling to place proteins into sequence-structure-function "neighborhoods". The atomic unit of the database is a set of sequences and structural templates that those sequences encode. A graph that is built from the structural comparison of these templates is called PDUG (protein domain universe graph). We introduce a method of functional inference through a probabilistic calculation done on an arbitrary set of PDUG nodes. Further, all PDUG structures are mapped onto all fully sequenced proteomes allowing an easy interface for evolutionary analysis and research into comparative proteomics. ELISA is the first database with applicability to evolutionary structural genomics explicitly in mind. AVAILABILITY: The database is available at http://romi.bu.edu/elisa.

Amino Acid Sequence↗

PubMed2006

BioWarehouse: a bioinformatics database warehouse toolkit.

BACKGROUND: This article addresses the problem of interoperation of heterogeneous bioinformatics databases. RESULTS: We introduce BioWarehouse, an open source toolkit for constructing bioinformatics database warehouses using the MySQL and Oracle relational database managers. BioWarehouse integrates its component databases into a common representational framework within a single database management system, thus enabling multi-database queries using the Structured Query Language (SQL) but also facilitating a variety of database integration tasks such as comparative analysis and data mining. BioWarehouse currently supports the integration of a pathway-centric set of databases including ENZYME, KEGG, and BioCyc, and in addition the UniProt, GenBank, NCBI Taxonomy, and CMR databases, and the Gene Ontology. Loader tools, written in the C and JAVA languages, parse and load these databases into a relational database schema. The loaders also apply a degree of semantic normalization to their respective source data, decreasing semantic heterogeneity. The schema supports the following bioinformatics datatypes: chemical compounds, biochemical reactions, metabolic pathways, proteins, genes, nucleic acid sequences, features on protein and nucleic-acid sequences, organisms, organism taxonomies, and controlled vocabularies. As an application example, we applied BioWarehouse to determine the fraction of biochemically characterized enzyme activities for which no sequences exist in the public sequence databases. The answer is that no sequence exists for 36% of enzyme activities for which EC numbers have been assigned. These gaps in sequence data significantly limit the accuracy of genome annotation and metabolic pathway prediction, and are a barrier for metabolic engineering. Complex queries of this type provide examples of the value of the data warehousing approach to bioinformatics research. CONCLUSION: BioWarehouse embodies significant progress on the database integration problem for bioinformatics.

Computational Biology↗

PubMed2006

EVEREST: automatic identification and classification of protein domains in all protein sequences.

BACKGROUND: Proteins are comprised of one or several building blocks, known as domains. Such domains can be classified into families according to their evolutionary origin. Whereas sequencing technologies have advanced immensely in recent years, there are no matching computational methodologies for large-scale determination of protein domains and their boundaries. We provide and rigorously evaluate a novel set of domain families that is automatically generated from sequence data. Our domain family identification process, called EVEREST (EVolutionary Ensembles of REcurrent SegmenTs), begins by constructing a library of protein segments that emerge in an all vs. all pairwise sequence comparison. It then proceeds to cluster these segments into putative domain families. The selection of the best putative families is done using machine learning techniques. A statistical model is then created for each of the chosen families. This procedure is then iterated: the aforementioned statistical models are used to scan all protein sequences, to recreate a library of segments and to cluster them again. RESULTS: Processing the Swiss-Prot section of the UniProt Knoledgebase, release 7.2, EVEREST defines 20,230 domains, covering 85% of the amino acids of the Swiss-Prot database. EVEREST annotates 11,852 proteins (6% of the database) that are not annotated by Pfam A. In addition, in 43,086 proteins (20% of the database), EVEREST annotates a part of the protein that is not annotated by Pfam A. Performance tests show that EVEREST recovers 56% of Pfam A families and 63% of SCOP families with high accuracy, and suggests previously unknown domain families with at least 51% fidelity. EVEREST domains are often a combination of domains as defined by Pfam or SCOP and are frequently sub-domains of such domains. CONCLUSION: The EVEREST process and its output domain families provide an exhaustive and validated view of the protein domain world that is automatically generated from sequence data. The EVEREST library of domain families, accessible for browsing and download at 1, provides a complementary view to that provided by other existing libraries. Furthermore, since it is automatic, the EVEREST process is scalable and we will run it in the future on larger databases as well. The EVEREST source files are available for download from the EVEREST web site.

Cluster Analysis↗

PubMed2006

MetaBasis: a web-based database containing metadata on software tools and databases in the field of bioinformatics.

UNLABELLED: We have developed an integrated web-based relational database information system, which offers an extensive search functionality of validated entries containing available bioinformatics computing resources. This system, called MetaBasis, aims to provide the bioinformatics community, and especially newcomers to the field, with easy access to reliable bioinformatics databases and tools. MetaBasis is focused on non-commercial and open-source software tools. AVAILABILITY: http://metabasis.bioacademy.gr/

Computational Biology↗

PubMed2006

Anatomical reasoning in the informatics age: Principles, ontologies, and agendas.

Reasoning about anatomy shares historical scientific roots with formal logic and artificial intelligence. With advances in computer-based intelligent programming, high-level biological structural knowledge may be exploited directly for biomedical research, clinical tasks, and educational applications. We consider the special nature of anatomical domain knowledge, emphasizing the complex concepts and semantics that must be represented in the development of ontologies, formally structured databases of biological information. We review the evolution of the fundamental scientific principles of logic and artificial intelligence needed for building machines that can make use of anatomical knowledge. We look at methods for compiling ontologies and compare the structural designs of the Foundational Model of Anatomy and Open GALEN ontologies. We further consider issues related to mapping developing anatomy resources with other biological ontologies in genomics, proteomics, and physiology. Although early results are promising, considerable resources and continuing effort must be committed to completing and extending anatomical ontologies for the ultimate success of computer-based anatomical reasoning. Anat Rec (Part B: New Anat) 289B:72-84, 2006. (c) 2006 Wiley-Liss, Inc.

Artificial Intelligence↗