The KEGG databases at GenomeNet

The Kyoto Encyclopedia of Genes and Genomes (KEGG) is the primary database resource of the Japanese GenomeNet service (http://www.genome.ad.jp/) for understanding higher order functional meanings and utilities of the cell or the organism from its genome information. KEGG consists of the PATHWAY database for the computerized knowledge on molecular interaction networks such as pathways and complexes, the GENES database for the information about genes and proteins generated by genome sequencing projects, and the LIGAND database for the information about chemical compounds and chemical reactions that are relevant to cellular processes. In addition to these three main databases, limited amounts of experimental data for microarray gene expression profiles and yeast two-hybrid systems are stored in the EXPRESSION and BRITE databases, respectively. Furthermore, a new database, named SSDB, is available for exploring the universe of all protein coding genes in the complete genomes and for identifying functional links and ortholog groups. The data objects in the KEGG databases are all represented as graphs and various computational methods are developed to detect graph features that can be related to biological functions. For example, the correlated clusters are graph similarities which can be used to predict a set of genes coding for a pathway or a complex, as summarized in the ortholog group tables, and the cliques in the SSDB graph are used to annotate genes. The KEGG databases are updated daily and made freely available (http://www.genome.ad.jp/kegg/).     

PGDB: a curated and integrated database of genes related to the prostate

The Prostate Gene Database (PGDB: http://www.ucsf.edu/pgdb) is a curated and integrated database of genes or genomic loci related to the human prostate and prostatic diseases. Currently, PGDB covers genes involved in a number of molecular and genetic events of the prostate including gene amplification, mutation, gross deletion, methylation, polymorphism, linkage and over-expression, as published in the literature. Genes that are specifically expressed in prostate, as evidenced by analysis of data from expressed sequence tags (ESTs) and serial analysis of gene expression (SAGE), are also included. There are a total of 165 unique entries in the database. Users can either browse or query the PGDB through a web interface. For each gene, in addition to basic gene information and rich cross-references to other databases, inclusive and relevant literature references are provided to support the inclusion of the gene in the database. Detailed expression data calculated from the UniGene and SAGEmap databases are also presented.     

ABRF-PRG05: De Novo Peptide Sequence Determination

A common request of proteomics core facilities is protein identification. However, in some instances primary sequence information for the protein in question is not present in public databases. In other cases, the amino acid sequence of a protein may differ in some way from the sequence predicted from the gene sequence in a database as a result of gene mutation, gene splicing, and/or multiple posttranslational modifications. Thus, it may be necessary to determine the sequence of one or more peptides de novo in order to identify and/or adequately characterize the protein of interest. The primary goal of this study was to give participating laboratories an opportunity to evaluate their proficiency in sequencing unknown peptides that are not included in any published database. Samples containing 3–6 pmol each of five synthetic peptides with amino acid sequences that were not present in public databases were sent to 106 laboratories. One nonstandard amino acid was present in one of the peptides. From a comparison of the results obtained by different strategies, participating laboratories will be able to gauge their own capabilities and establish realistic expectations for the approaches that can be used for this determination.     

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     

JESAM: CORBA software components to create and publish EST alignments and clusters

MOTIVATION: Expressed Sequence Tags (ESTs) are cheap, easy and quick to obtain relative to full genomic sequencing and currently sample more eukaryotic genes than any other data source. They are particularly useful for developing Sequence Tag Sites (STSs for mapping), polymorphism discovery, disease gene hunting, mass spectrometer proteomics, and most ironically for finding genes and predicting gene structure after the great effort of genomic sequencing. However, ESTs have many problems and the public EST databases contain all the errors and high redundancy intrinsic to the submitted data so it is often found that derived database views, which reduce both errors and redundancy, are more effective starting points for research than the original raw submissions. Existing derived views such as EST cluster databases and consensus databases have never published supporting evidence or intermediary results leading to difficulties trusting, correcting, and customizing the final published database. These difficulties have lead many groups to wastefully repeat the complex intermediary work of others in order to offer slightly different final views. A better approach might be to discover the most expensive common calculations used by all the approaches and then publish all intermediary results. Given a globally accessible database with a suitable component interface, like the JESAM software described in this paper, the creation of customized EST-derived databases could be achieved with minimum effort. RESULTS: Databases of EST and full-length mRNA sequences for four model organisms have been self-compared by searching for overlaps consistent with contiguity. The sequence comparisons are performed in parallel using a PVM process farm and previous results are stored to allow incremental updates with minimal effort. The overlap databases have been published with CORBA interfaces to enable flexible global access as demonstrated by example Java applet browsers. Simple cDNA supercluster databases built as alignment database clients are themselves published via CORBA interfaces browsable with prototypical applets. A comparison with UniGene Mouse and Rat databases revealed undesirable features in both and the advantages of contrasting perspectives on complex data. AVAILABILITY: The software is packaged as two Jar files available from: URL: http://corba.ebi.ac.uk/EST/jesam/jesam. html. One jar contains all the Java source code, and the other contains all the C, C++ and IDL code. Links to working examples of the alignment and cluster viewers (if remote firewall permits) can be found at http://corba.ebi.ac.uk/EST. All the Washington University mouse EST traces are available for browsing at the same URL.     

Expressed sequence tags (ESTs) from the marine red alga Gracilaria gracilis

Expressed sequence tags (ESTs) are partial sequences of cDNAs, and can be used to characterize gene expression in organisms or tissues. We have constructed a 200-sequence EST database from vegetative thalli of Gracilaria gracilis, the first ESTs reported from any alga. This database contains recognizable ESTs corresponding to genes of carbohydrate metabolism (seven), amino acid metabolism (three), photosynthesis (five), nucleic acid synthesis, repair and processing (three), protein synthesis (14), protein degradation (six), cellular maintenance and stress response (three), other identifiable protein-coding genes (13) and 146 sequences for which significant matches were not found in existing sequence databases. We have already used this EST database to recover genes of carbohydrate biosynthesis from G. gracilis. This revised version was published online in August 2006 with corrections to the Cover Date.     

FlyNets and GIF-DB, two internet databases for molecular interactions in Drosophila melanogaster.

GIF-DB and FlyNets are two WWW databases describing molecular (protein-DNA, protein-RNA and protein-protein) interactions occuring in the fly Drosophila melanogaster (http://gifts.univ-mrs.fr/GIFTS_home_page.html ). GIF-DB is a specialised database which focuses on molecular interactions involved in the process of embryonic pattern formation, whereas FlyNets is a new and more general database, the long-term goal of which is to report on any published molecular interaction occuring in the fly. The information content of both databases is distributed in specific lines arranged into an EMBL- (or GenBank-) like format. These databases achieve a high level of integration with other databases such as FlyBase, EMBL, GenBank and SWISS-PROT through numerous hyperlinks. In addition, we also describe SOS-DGDB, a new collection of annotated Drosophila gene sequences, in which binding sites for regulatory proteins are directly visible on the DNA primary sequence and hyperlinked both to GIF-DB and TRANSFAC database entries.     

A database of unique protein sequence identifiers for proteome studies

In proteome studies, identification of proteins requires searching protein sequence databases. The public protein sequence databases (e.g., NCBInr, UniProt) each contain millions of entries, and private databases add thousands more. Although much of the sequence information in these databases is redundant, each database uses distinct identifiers for the identical protein sequence and often contains unique annotation information. Users of one database obtain a database-specific sequence identifier that is often difficult to reconcile with the identifiers from a different database. When multiple databases are used for searches or the databases being searched are updated frequently, interpreting the protein identifications and associated annotations can be problematic. We have developed a database of unique protein sequence identifiers called Sequence Globally Unique Identifiers (SEGUID) derived from primary protein sequences. These identifiers serve as a common link between multiple sequence databases and are resilient to annotation changes in either public or private databases throughout the lifetime of a given protein sequence. The SEGUID Database can be downloaded (http://bioinformatics.anl.gov/SEGUID/) or easily generated at any site with access to primary protein sequence databases. Since SEGUIDs are stable, predictions based on the primary sequence information (e.g., pI, Mr) can be calculated just once; we have generated approximately 500 different calculations for more than 2.5 million sequences. SEGUIDs are used to integrate MS and 2-DE data with bioinformatics information and provide the opportunity to search multiple protein sequence databases, thereby providing a higher probability of finding the most valid protein identifications.     

Community annotation in biology

   

Attempts to engage the scientific community to annotate biological data (such as protein/gene function) stored in databases have not been overly successful. There are several hypotheses on why this has not been successful but it is not clear which of these hypotheses are correct. In this study we have surveyed 50 biologists (who have recently published a paper characterizing a gene or protein) to better understand what would make them interested in providing input/contributions to biological databases. Based on our survey two things become clear: a) database managers need to proactively contact biologists to solicit contributions; and b) potential contributors need to be provided with an easy-to-use interface and clear instructions on what to annotate. Other factors such as 'reward' and 'employer/funding agency recognition' previously perceived as motivators was found to be less important. Based on this study we propose community annotation projects should devote resources to direct solicitation for input and streamlining of the processes or interfaces used to collect this input.     

HSPVdb—the Human Short Peptide Variation Database for improved mass spectrometry-based detection of polymorphic HLA-ligands

T cell epitopes derived from polymorphic proteins or from proteins encoded by alternative reading frames (ARFs) play an important role in (tumor) immunology. Identification of these peptides is successfully performed with mass spectrometry. In a mass spectrometry-based approach, the recorded tandem mass spectra are matched against hypothetical spectra generated from known protein sequence databases. Commonly used protein databases contain a minimal level of redundancy, and thus, are not suitable data sources for searching polymorphic T cell epitopes, either in normal or ARFs. At the same time, however, these databases contain much non-polymorphic sequence information, thereby complicating the matching of recorded and theoretical spectra, and increasing the potential for finding false positives. Therefore, we created a database with peptides from ARFs and peptide variation arising from single nucleotide polymorphisms (SNPs). It is based on the human mRNA sequences from the well-annotated reference sequence (RefSeq) database and associated variation information derived from the Single Nucleotide Polymorphism Database (dbSNP). In this process, we removed all non-polymorphic information. Investigation of the frequency of SNPs in the dbSNP revealed that many SNPs are non-polymorphic “SNPs”. Therefore, we removed those from our dedicated database, and this resulted in a comprehensive high quality database, which we coined the Human Short Peptide Variation Database (HSPVdb). The value of our HSPVdb is shown by identification of the majority of published polymorphic SNP- and/or ARF-derived epitopes from a mass spectrometry-based proteomics workflow, and by a large variety of polymorphic peptides identified as potential T cell epitopes in the HLA-ligandome presented by the Epstein–Barr virus cells.     

EcoGene: a genome sequence database for Escherichia coli K-12

The EcoGene database provides a set of gene and protein sequences derived from the genome sequence of Escherichia coli K-12. EcoGene is a source of re-annotated sequences for the SWISS-PROT and Colibri databases. EcoGene is used for genetic and physical map compilations in collaboration with the Coli Genetic Stock Center. The EcoGene12 release includes 4293 genes. EcoGene12 differs from the GenBank annotation of the complete genome sequence in several ways, including (i) the revision of 706 predicted or confirmed gene start sites, (ii) the correction or hypothetical reconstruction of 61 frame-shifts caused by either sequence error or mutation, (iii) the reconstruction of 14 protein sequences interrupted by the insertion of IS elements, and (iv) pre-dictions that 92 genes are partially deleted gene fragments. A literature survey identified 717 proteins whose N-terminal amino acids have been verified by sequencing. 12 446 cross-references to 6835 literature citations and s are provided. EcoGene is accessible at a new website: http://bmb.med.miami.edu/EcoGene/EcoWeb. Users can search and retrieve individual EcoGene GenePages or they can download large datasets for incorporation into database management systems, facilitating various genome-scale computational and functional analyses.     

Mycobacteriophage genome database

Mycobacteriophage genome database (MGDB) is an exclusive repository of the 64 completely sequenced mycobacteriophages with annotated information. It is a comprehensive compilation of the various gene parameters captured from several databases pooled together to empower mycobacteriophage researchers. The MGDB (Version No.1.0) comprises of 6086 genes from 64 mycobacteriophages classified into 72 families based on ACLAME database. Manual curation was aided by information available from public databases which was enriched further by analysis. Its web interface allows browsing as well as querying the classification. The main objective is to collect and organize the complexity inherent to mycobacteriophage protein classification in a rational way. The other objective is to browse the existing and new genomes and describe their functional annotation. AVAILABILITY: The database is available for free at http://mpgdb.ibioinformatics.org/mpgdb.php.     

ANDVisio: a new tool for graphic visualization and analysis of literature mined associative gene networks in the ANDSystem

The ANDVisio tool is designed to reconstruct and analyze associative gene networks in the earlier developed Associative Network Discovery System (ANDSystem) software package. The ANDSystem incorporates utilities for automated extraction of knowledge from Pubmed published scientific texts, analysis of factographic databases, also the ANDCell database containing information on molecular-genetic events retrieved from texts and databases. ANDVisio is a new user's interface to the ANDCell database stored in a remote server. ANDVisio provides graphic visualization, editing, search, also saving of associative gene networks in different formats resulting from user's request. The associative gene networks describe semantic relationships between molecular-genetic objects (proteins, genes, metabolites and others), biological processes, and diseases. ANDVisio is provided with various tools to support filtering by object types, relationships between objects and information sources; graph layout; search of the shortest pathway; cycles in graphs.     

Low-coverage massively parallel pyrosequencing of cDNAs enables proteomics in non-model species: comparison of a species-specific database generated by pyrosequencing with databases from related species for proteome analysis of pea chloroplast envelopes

Proteomics is a valuable tool for establishing and comparing the protein content of defined tissues, cell types, or subcellular structures. Its use in non-model species is currently limited because the identification of peptides critically depends on sequence databases. In this study, we explored the potential of a preliminary cDNA database for the non-model species Pisum sativum created by a small number of massively parallel pyrosequencing (MPSS) runs for its use in proteomics and compared it to comprehensive cDNA databases from Medicago truncatula and Arabidopsis thaliana created by Sanger sequencing. Each database was used to identify proteins from a pea leaf chloroplast envelope preparation. It is shown that the pea database identified more proteins with higher accuracy, although the sequence quality was low and the sequence contigs were short compared to databases from model species. Although the number of identified proteins in non-species-specific databases could potentially be increased by lowering the threshold for successful protein identifications, this strategy markedly increases the number of wrongly identified proteins. The identification rate with non-species-specific databases correlated with spectral abundance but not with the predicted membrane helix content, and strong conservation is necessary but not sufficient for protein identification with a non-species-specific database. It is concluded that massively parallel sequencing of cDNAs substantially increases the power of proteomics in non-model species.     

The genetic toxicology of putative nongenotoxic carcinogens

This report examines a group of putative nongenotoxic carcinogens that have been cited in the published literature. Using short-term test data from the U.S. Environmental Protection Agency/International Agency for Research on Cancer genetic activity profile (EPA/IARC GAP) database we have classified these agents on the basis of their mutagenicity emphasizing three genetic endpoints: gene mutation, chromosomal aberration and aneuploidy. On the basis of results of short-term tests for these effects, we have defined criteria for evidence of mutagenicity (and nonmutagenicity) and have applied these criteria in classifying the group of putative nongenotoxic carcinogens. The results from this evaluation based on the EPA/IARC GAP database are presented along with a summary of the short-term test data for each chemical and the relevant carcinogenicity results from the NTP, Gene-Tox and IARC databases. The data clearly demonstrate that many of the putative nongenotoxic carcinogens that have been adequately tested in short-term bioassays induce gene or chromosomal mutations or aneuploidy.     

SWISS-PROT: connecting biomolecular knowledge via a protein database

With the explosive growth of biological data, the development of new means of data storage was needed. More and more often biological information is no longer published in the conventional way via a publication in a scientific journal, but only deposited into a database. In the last two decades these databases have become essential tools for researchers in biological sciences. Biological databases can be classified according to the type of information they contain. There are basically three types of sequence-related databases (nucleic acid sequences, protein sequences and protein tertiary structures) as well as various specialized data collections. It is important to provide the users of biomolecular databases with a degree of integration between these databases as by nature all of these databases are connected in a scientific sense and each one of them is an important piece to biological complexity. In this review we will highlight our effort in connecting biological information as demonstrated in the SWISS-PROT protein database.