Bioinformatics analysis of gene banks provides a treasure trove for the functional genomist.
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Animals↗
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Bioinformatics for characterisation of allergens, allergenicity and allergic crossreactivity.
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Algorithms↗
List update processing (LUP)--solving the sequence database update problem.
Sequence databases of today require frequent updating. Mirror procedures to copy incrementally updated databases as cumulative sets are the preferred method and can be implemented by straightforward scripting. However, limited bandwidth of networks and the increase of data require more powerful paradigms to reduce the workload reliably. We suggest the List Update Processing (LUP) principle. The system has been implemented on an experimental basis to update the Swiss EMBnet Node (BioComputing Basel, CH) with data from the European Bioinformatics Institute (EMBL Outstation, Hinxton Hall, UK). The results obtained from the prototype suggest to expand the system to several sites.
CD-ROM↗
The cis-Pro touch-turn: a rare motif preferred at functional sites.
A new motif of three-dimensional (3D) protein structure is described, called the cis-Pro touch-turn. In this four-residue, three-peptide motif, the central peptide is cis. Residue 2, which precedes the proline, has phi, psi values either in the "prePro region" of the Ramachandran plot near -130 degrees, 75 degrees or in the Lalpha region near +60 degrees, +60 degrees. The Calpha(1)-Calpha(4) distance is 4-5 A and the two flanking peptides lie parallel to one another, making van der Waals contact rather than a hydrogen bond. Apparently, this arrangement is locally unfavorable and therefore rare, usually occurring only if needed for biological function. Of the 12 examples in a 500-protein database, cis-Pro touch-turns are found at the catalytic sites of pectate lyase, Ni-Fe hydrogenase, glucoamylase, xylanase, and opine dehydrogenase and at the primary binding sites of ribonuclease H, type I DNA polymerase, ribotoxin, and phage gene 3 protein. In each of these protein families, the touch-turns serve different roles; their functional importance is supported by conservation and mutagenesis data. In analyzing the conservation patterns of these 3D motifs, new methods for in-depth quality evaluation of the structural bioinformatic data are employed to distinguish between significant exceptions and errors
Amino Acid Motifs↗
MaGe: a microbial genome annotation system supported by synteny results.
Magnifying Genomes (MaGe) is a microbial genome annotation system based on a relational database containing information on bacterial genomes, as well as a web interface to achieve genome annotation projects. Our system allows one to initiate the annotation of a genome at the early stage of the finishing phase. MaGe's main features are (i) integration of annotation data from bacterial genomes enhanced by a gene coding re-annotation process using accurate gene models, (ii) integration of results obtained with a wide range of bioinformatics methods, among which exploration of gene context by searching for conserved synteny and reconstruction of metabolic pathways, (iii) an advanced web interface allowing multiple users to refine the automatic assignment of gene product functions. MaGe is also linked to numerous well-known biological databases and systems. Our system has been thoroughly tested during the annotation of complete bacterial genomes (Acinetobacter baylyi ADP1, Pseudoalteromonas haloplanktis, Frankia alni) and is currently used in the context of several new microbial genome annotation projects. In addition, MaGe allows for annotation curation and exploration of already published genomes from various genera (e.g. Yersinia, Bacillus and Neisseria). MaGe can be accessed at http://www.genoscope.cns.fr/agc/mage.
Computational Biology↗
Challenges in integrating biological data sources.
Scientific data of importance to biologists reside in a number of different data sources, such as GenBank, GSDB, SWISS-PROT, EMBL, and OMIM, among many others. Some of these data sources are conventional databases implemented using database management systems (DBMSs) and others are structured files maintained in a number of different formats (e.g., ASN.1 and ACE). In addition, software packages such as sequence analysis packages (e.g., BLAST and FASTA) produce data and can therefore be viewed as data sources. To counter the increasing dispersion and heterogeneity of data, different approaches to integrating these data sources are appearing throughout the bioinformatics community. This paper surveys the technical challenges to integration, classifies the approaches, and critiques the available tools and methodologies.
Chromosomes, Artificial, Yeast↗
Identification of two hERR2-related novel nuclear receptors utilizing bioinformatics and inverse PCR.
Identification of novel nuclear receptors based on the highly conserved DNA-binding domain (DBD) has previously depended mainly on low stringency hybridization of cDNA libraries and degenerate PCR. Establishment of the expressed sequence tag (EST) database in recent years has provided an alternative approach for the discovery of novel members of gene families. The rate-limiting step is the conversion of ESTs to full-length cDNA. This article describes the identification of two novel nuclear receptors (hERRbeta2 and hERRgamma2) related to human estrogen-receptor-related receptor 2 (hERR2) by mining the EST database and retrieving of full-length cDNA via inverse PCR on subdivided primary cDNA library pools. The deduced protein sequences of hERRbeta2 and hERRgamma2 contain 500 and 458 amino acid (aa) residues respectively. Sequence analysis revealed that hERRbeta2 and hERRgamma2 respectively share 95% and 77% overall aa sequence identity with hERR2. However, the extra C-terminal domain in hERRbeta2 and extra N-terminal domain in hERRgamma2 are not present in the closely related hERR2 or mouse ERR2 (mERR2). Extensive sequence verification revealed that hERR2 previously reported as a human gene is actually a rat gene, whereas hERRbeta2 is the true human ortholog of hERR2 and mERR2. Tissue distribution studies showed that hERRgamma2 was expressed in a broader panel of tissues at a higher level than hERRbeta2. hERRbeta2 was mapped to cytogenetic locus 14q24.3 approximately -14q31, a region containing multiple loci involved in genetic diseases, including Alzheimer and diabetes. hERRgamma2 was mapped to 1q32. Given the high sequence homology between hERRbeta2 and mERR2, the two receptors may have similar biological function in vivo.
Amino Acid Sequence↗
pTARGET: a web server for predicting protein subcellular localization.
The pTARGET web server enables prediction of nine distinct protein subcellular localizations in eukaryotic non-plant species. Predictions are made using a new algorithm [C. Guda and S. Subramaniam (2005) pTARGET [corrected] a new method for predicting protein subcellular localization in eukaryotes. Bioinformatics, 21, 3963-3969], which is primarily based on the occurrence patterns of location-specific protein functional domains in different subcellular locations. We have implemented a relational database, PreCalcDB, to store pre-computed prediction results for all eukaryotic non-plant protein sequences in the public domain that includes about 770,000 entries. Queries can be made by entering protein sequences or by uploading a file containing up to 5000 protein sequences in FASTA format. Prediction results for queries with matching entries in the PreCalcDB will be retrieved instantly; while for the missing ones new predictions will be computed and sent by email. Pre-computed predictions can also be downloaded for complete proteomes of Saccharomyces cerevisiae, Caenorhabditis elegans, Drosophila, Mus musculus and Homo sapiens. The server, its documentation and the data are accessible from http://bioinformatics.albany.edu/~ptarget.
Algorithms↗
Bioinformatics of the glycoside hydrolase family 57 and identification of catalytic residues in amylopullulanase from Thermococcus hydrothermalis.
Fifty-nine amino acid sequences belonging to family 57 (GH-57) of the glycoside hydrolases were collected using the CAZy server, Pfam database and blast tools. Owing to the sequence heterogeneity of the GH-57 members, sequence alignments were performed using mainly manual methods. Likewise, five conserved regions were identified, which are postulated to be GH-57 consensus motifs. In the 659 amino acid-long 4-alpha-glucanotransferase from Thermococcus litoralis, these motifs correspond to 13_HQP (region I), 76_GQLEIV (region II), 120_WLTERV (region III), 212_HDDGEKFGVW (region IV), and 350_AQCNDAYWH (region V). The third and fourth conserved regions contain the catalytic nucleophile and the proton donor, respectively. Based on our sequence alignment, residues Glu291 and Asp394 were proposed as the nucleophile and proton donor, respectively, in a GH-57 amylopullulanase from Thermococcus hydrothermalis. To validate this prediction, site-directed mutagenesis was performed. The results of this work reveal that both residues are critical for the pullulanolytic and amylolytic activities of the amylopullulanase. Therefore, these data support the prediction and strongly suggest that the bifunctionality of the amylopullulanase is determined by a single catalytic centre. Despite this positive validation, our alignment also reveals that certain GH-57 members do not possess the Glu and Asp corresponding to the predicted GH-57 catalytic residues. However, the sequences concerned by this anomaly encode putative proteins for which no biochemical or enzymatic data are yet available. Finally, the evolutionary trees generated for GH-57 reveal that the entire family can be divided into several subfamilies that may reflect the different enzyme specificities.
Amino Acid Sequence↗
Genomics. University company to exploit heart data.
This month, Boston University, which directs the Framingham Heart Study, a massive government effort begun in 1948 to monitor the cardiovascular health of more than 10,000 residents of this suburb of Boston, announced plans to form a bioinformatics company that will mine the data. The university will own 20% of Framingham Genomic Medicine Inc., which hopes to raise $21 million to begin modernizing the immense database and packaging it in a format that will be valuable to the pharmaceutical industry. The plan raises a host of difficult ethical issues, including patient privacy, academic conflicts of interest, and reciprocal value to the Framingham residents whose medical data will form the basis for the new enterprise.
Bioethics↗
Correcting ligands, metabolites, and pathways.
BACKGROUND: A wide range of research areas in bioinformatics, molecular biology and medicinal chemistry require precise chemical structure information about molecules and reactions, e.g. drug design, ligand docking, metabolic network reconstruction, and systems biology. Most available databases, however, treat chemical structures more as illustrations than as a datafield in its own right. Lack of chemical accuracy impedes progress in the areas mentioned above. We present a database of metabolites called BioMeta that augments the existing pathway databases by explicitly assessing the validity, correctness, and completeness of chemical structure and reaction information. DESCRIPTION: The main bulk of the data in BioMeta were obtained from the KEGG Ligand database. We developed a tool for chemical structure validation which assesses the chemical validity and stereochemical completeness of a molecule description. The validation tool was used to examine the compounds in BioMeta, showing that a relatively small number of compounds had an incorrect constitution (connectivity only, not considering stereochemistry) and that a considerable number (about one third) had incomplete or even incorrect stereochemistry. We made a large effort to correct the errors and to complete the structural descriptions. A total of 1468 structures were corrected and/or completed. We also established the reaction balance of the reactions in BioMeta and corrected 55% of the unbalanced (stoichiometrically incorrect) reactions in an automatic procedure. The BioMeta database was implemented in PostgreSQL and provided with a web-based interface. CONCLUSION: We demonstrate that the validation of metabolite structures and reactions is a feasible and worthwhile undertaking, and that the validation results can be used to trigger corrections and improvements to BioMeta, our metabolite database. BioMeta provides some tools for rational drug design, reaction searches, and visualization. It is freely available at http://www.cmbi.ru.nl/biometa/ provided that the copyright notice of all original data is cited. The database will be useful for querying and browsing biochemical pathways, and to obtain reference information for identifying compounds. However, these applications require that the underlying data be correct, and that is the focus of BioMeta.
Computational Biology↗
Molecular phylogeny of the antiangiogenic and neurotrophic serpin, pigment epithelium derived factor in vertebrates.
BACKGROUND: Pigment epithelium derived factor (PEDF), a member of the serpin family, regulates cell proliferation, promotes survival of neurons, and blocks growth of new blood vessels in mammals. Defining the molecular phylogeny of PEDF by bioinformatic analysis is one approach to understanding the link between its gene structure and its function in these biological processes. RESULTS: From a comprehensive search of available DNA databases we identified a single PEDF gene in all vertebrate species examined. These included four mammalian and six non-mammalian vertebrate species in which PEDF had not previously been described. A five gene cluster around PEDF was found in an approximate 100 kb region in mammals, birds, and amphibians. In ray-finned fish these genes are scattered over three chromosomes although only one PEDF gene was consistently found. The PEDF gene is absent in invertebrates including Drosophila melanogaster (D. melanogaster), Caenorhabditis elegans (C. elegans), and sea squirt (C. intestinalis). The PEDF gene is transcribed in all vertebrate phyla, suggesting it is biologically active throughout vertebrate evolution. The multiple actions of PEDF are likely conserved in evolution since it has the same gene structure across phyla, although the size of the gene ranges from 48.3 kb in X. tropicalis to 2.9 kb in fugu, with human PEDF at a size of 15.6 kb. A strong similarity in the proximal 200 bp of the PEDF promoter in mammals suggests the existence of a possible regulatory region across phyla. Using a non-synonymous/synonymous substitution rate ratio we show that mammalian and fish PEDFs have similar ratios of <0.13, reflecting a strong purifying selection of PEDF gene. A large number of repetitive transposable elements of the SINE and LINE class were found with random distribution in both the promoter and introns of mammalian PEDF. CONCLUSION: The PEDF gene first appears in vertebrates and our studies suggest that the regulation and biological actions of this gene are preserved across vertebrates. This comprehensive analysis of the PEDF gene across phyla provides new information that will aid further characterization of common functional motifs of this serpin in biological processes.
Animals↗
Distributed computing in bioinformatics.
This paper provides an overview of methods and current applications of distributed computing in bioinformatics. Distributed computing is a strategy of dividing a large workload among multiple computers to reduce processing time, or to make use of resources such as programs and databases that are not available on all computers. Participating computers may be connected either through a local high-speed network or through the Internet.
Computational Biology↗
Database integration for predisposition genes discovery.
Lightweight and flexible data integration is nowadays a great challenge in biology. Databases size increased exponentially, and biologists need tools to extract and merge interesting data coming from several heterogeneous and distributed sources over the Internet. Furthermore, there is a great need for data mediation on grid computing architectures. This paper is about a demonstration which goal is to show how Mediation, a data integration technique, can help in building such bioinformatics tools. We focused on research of genes functions, especially those involved in the resistance to malaria or those that may be considered as predisposition genes.
Computational Biology↗
Survey of current protein family databases and their application in comparative, structural and functional genomics.
The last two decades have witnessed significant expansions in the databases storing information on the sequences and structures of proteins. This has led to the creation of many excellent protein family resources, which classify proteins according to their evolutionary relationship. These have allowed extensive insights into evolution and particularly how protein function mutates and evolves over time. Such analyses have greatly assisted the inheritance of functional annotations between experimentally characterised and uncharacterised genes. Moreover, the development of bioinformatics tools acts as a companion to the new technologies emerging in biology, such as transcriptomics and proteomics. The latter enable researchers to analyse gene expression profiles and interactions on a genome-wide scale, generating vast datasets of proteins, many of which include experimentally uncharacterised proteins. Protein family/function databases can be used to help interpret this data and allow us to benefit more fully from these technologies. This review aims to summarise the most popular sequence- and structure-based protein family databases. We also cover their application to comparative genomics and the functional annotation of the genomes.
Biological Evolution↗
Integrating forward and reverse proteomics to unravel protein function.
To date, proteomics approaches have aimed to either identify novel proteins or change in protein expression/modification in various organisms under normal or disease conditions. One major aspect of functional proteomics is to identify protein biological properties in a given context, however, forward proteomics approaches alone cannot complete this goal. Indeed, with the increasing successes of such proteomics-based research strategies and the subsequent increasing amounts of proteins identified with unknown molecular functions, approaches allowing for systematic analyses of protein functions are desired. In this review, we propose to depict the complementarities of forward and reverse proteomics approaches in the definite understanding of protein functions. This dual strategy requires a data integration loop which allows for systematic characterization of protein function(s). The details of the integrative process combining both in silico and experimental resources and tools are presented. Altogether, we believe that the integration of forward and reverse proteomics approaches supported by bioinformatics will provide an efficient path towards systems biology.
Animals↗
Genome resources and comparative analysis tools for cardiovascular research.
Disorders of the cardiovascular system are often caused by the interaction of genetic and environmental factors that jointly contribute to individual susceptibility. Genomic data and bioinformatics tools generated from genome projects, coupled with functional verification, offer novel approaches to study both rare single-gene and complex multigenic cardiovascular diseases. These approaches include gene mapping using genome variation, especially single-nucleotide polymorphisms and comparative genomics within and between species. This chapter illustrates the major genome resources, associated bioinformatics tools, and their potential application in cardiovascular research.
Cardiovascular Diseases↗
Predicting membrane protein type by functional domain composition and pseudo-amino acid composition.
Given the sequence of a protein, how can we predict whether it is a membrane protein or non-membrane protein? If it is, what membrane protein type it belongs to? Since these questions are closely relevant to the function of an uncharacterized protein, their importance is self-evident. Particularly, with the explosion of protein sequences entering into databanks and the relatively much slower progress in using biochemical experiments to determine their functions, it is highly desired to develop an automated method that can be used to give a fast answers to these questions. By hybridizing the functional domain (FunD) and pseudo-amino acid composition (PseAA), a new strategy called FunD-PseAA predictor was introduced. To test the power of the predictor, a highly non-homologous data set was constructed where none of proteins has 25% sequence identity to any other. The overall success rates obtained with the FunD-PseAA predictor on such a data set by the jackknife cross-validation test was 85% for the case in identifying membrane protein and non-membrane protein, and 91% in identifying the membrane protein type among the following 5 categories: (1) type-1 membrane protein, (2) type-2 membrane protein, (3) multipass transmembrane protein, (4) lipid chain-anchored membrane protein, and (5) GPI-anchored membrane protein. These rates are much higher than those obtained by the other methods on the same stringent data set, indicating that the FunD-PseAA predictor may become a useful high throughput tool in bioinformatics and proteomics.
Amino Acid Sequence↗