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PubMed2001

Bioinformatic tools for DNA/protein sequence analysis, functional assignment of genes and protein classification.

The development of efficient DNA sequencing methods has led to the achievement of the DNA sequence of entire genomes from (to date) 55 prokaryotes, 5 eukaryotic organisms and 10 eukaryotic chromosomes. Thus, an enormous amount of DNA sequence data is available and even more will be forthcoming in the near future. Analysis of this overwhelming amount of data requires bioinformatic tools in order to identify genes that encode functional proteins or RNA. This is an important task, considering that even in the well-studied Escherichia coli more than 30% of the identified open reading frames are hypothetical genes. Future challenges of genome sequence analysis will include the understanding of gene regulation and metabolic pathway reconstruction including DNA chip technology, which holds tremendous potential for biomedicine and the biotechnological production of valuable compounds. The overwhelming volume of information often confuses scientists. This review intends to provide a guide to choosing the most efficient way to analyze a new sequence or to collect information on a gene or protein of interest by applying current publicly available databases and Web services. Recently developed tools that allow functional assignment of genes, mainly based on sequence similarity of the deduced amino acid sequence, using the currently available and increasing biological databases will be discussed.

Computational Biology↗
PubMed2006

Recent developments of the chemistry development kit (CDK) - an open-source java library for chemo- and bioinformatics.

The Chemistry Development Kit (CDK) provides methods for common tasks in molecular informatics, including 2D and 3D rendering of chemical structures, I/O routines, SMILES parsing and generation, ring searches, isomorphism checking, structure diagram generation, etc. Implemented in Java, it is used both for server-side computational services, possibly equipped with a web interface, as well as for applications and client-side applets. This article introduces the CDK's new QSAR capabilities and the recently introduced interface to statistical software.

Computational Biology↗
PubMed2006

Integrative proteomics: structure, function, and interaction report on the 3rd joint meeting of the British Society for Proteome Research and the European Bioinformatics Institute, July 2006.

This report summarizes the highlights of the recent British Society for Proteome Research (BSPR) meeting jointly organized with the European Bioinformatics Institute (EBI) which was held at the Wellcome Trust Genome Campus, Hinxton, Cambridge, UK in July 2006. This was the third annual scientific meeting organized by the BSPR and EBI and the theme of this years meeting was Integrative Proteomics: Structure, function and interaction. A wealth of local and overseas speakers were invited to discuss both their own work and specific challenges present in modern day proteomic based experiments.

Computational Biology↗
PubMed2004

Microarray-based gene expression profiling of hematologic malignancies: basic concepts and clinical applications.

Each cell in our body contains a set of tens of thousands of genes, out of which a set of several thousands determines the cell's characteristics. The deciphering of the sequence of the human genome combined with the technical feasibility to simultaneously measure the gene expression levels of thousands of genes had revolutionized our understanding of cellular processes. This ability has great significance in our comprehension of the mechanisms that bring about diseases in general and hematologic malignancies in particular. Several new high-throughput technologies, commonly referred as microarrays, enable us to perform such measurements and concurrently, bioinformatic and statistical tools were developed to analyze the data obtained by using microarrays. In this review we present examples of analyses of hematologic malignancies using microarrays which contribute to refinement of diagnosis, identification of novel disease subtypes and of relationships between diseases that were previously considered to be unrelated, prediction of response to treatment and identification of genes and pathways linked to pathogenesis, thus defining targets to rational therapy.

Databases as Topic↗
PubMed2001

Mining DNA microarray data using a novel approach based on graph theory.

The recent demonstration that biochemical pathways from diverse organisms are arranged in scale-free, rather than random, systems [Jeong et al., Nature 407 (2000) 651-654], emphasizes the importance of developing methods for the identification of biochemical nexuses--the nodes within biochemical pathways that serve as the major input/output hubs, and therefore represent potentially important targets for modulation. Here we describe a bioinformatics approach that identifies candidate nexuses for biochemical pathways without requiring functional gene annotation; we also provide proof-of-principle experiments to support this technique. This approach, called Nexxus, may lead to the identification of new signal transduction pathways and targets for drug design.

Apoptosis↗
PubMed2003

Talisman--rapid application development for the grid.

In order to make use of the emerging grid and network services offered by various institutes and mandated by many current research projects, some kind of user accessible client is required. In contrast with attempts to build generic workbenches, Talisman is designed to allow a bioinformatics expert to rapidly build custom applications, immediately visible using standard web technology, for users who wish to concentrate on the biology of their problem rather than the informatics aspects. As a component of the MyGrid project, it is intended to allow access to arbitrary resources, including but not limited to relational, object and flat file data sources, analysis programs and grid based storage, tracking and distributed annotation systems.

Computational Biology↗
PubMed2005

An ant colony optimisation algorithm for the 2D and 3D hydrophobic polar protein folding problem.

BACKGROUND: The protein folding problem is a fundamental problems in computational molecular biology and biochemical physics. Various optimisation methods have been applied to formulations of the ab-initio folding problem that are based on reduced models of protein structure, including Monte Carlo methods, Evolutionary Algorithms, Tabu Search and hybrid approaches. In our work, we have introduced an ant colony optimisation (ACO) algorithm to address the non-deterministic polynomial-time hard (NP-hard) combinatorial problem of predicting a protein's conformation from its amino acid sequence under a widely studied, conceptually simple model - the 2-dimensional (2D) and 3-dimensional (3D) hydrophobic-polar (HP) model. RESULTS: We present an improvement of our previous ACO algorithm for the 2D HP model and its extension to the 3D HP model. We show that this new algorithm, dubbed ACO-HPPFP-3, performs better than previous state-of-the-art algorithms on sequences whose native conformations do not contain structural nuclei (parts of the native fold that predominantly consist of local interactions) at the ends, but rather in the middle of the sequence, and that it generally finds a more diverse set of native conformations. CONCLUSIONS: The application of ACO to this bioinformatics problem compares favourably with specialised, state-of-the-art methods for the 2D and 3D HP protein folding problem; our empirical results indicate that our rather simple ACO algorithm scales worse with sequence length but usually finds a more diverse ensemble of native states. Therefore the development of ACO algorithms for more complex and realistic models of protein structure holds significant promise.

Algorithms↗
PubMed2006

Structural e-bioinformatics and drug design.

Nowadays the in silico scenario for drug design is totally dependent on structural biology and structural bioinformatics. A myriad of free bioinformatics applications and services have been posted on the web. This mini-review mentions web sites that are useful in structure-based drug design. The information is given in a logical manner, following the drug design process i.e. characterization of a protein target, modelling the protein using sequence homology, optimization of the protein structure and finally docking of small ligands into the active site.

Amino Acid Sequence↗
PubMed2005

Two-dimensional gel electrophoresis maps of the proteome and phosphoproteome of primitively cultured rat mesangial cells.

Mesangial cells (MC) play an important role in maintaining the structure and function of the glomerulus. The proliferation of MC is a prominent feature of many kinds of glomerular disease. The first reference 2-DE maps of rat mesangial cells (RMC), stained with silver staining or Pro-Q Diamond dye, have been established here to describe the proteome and phosphoproteome of RMC, respectively. A total of 157 selected protein spots, corresponding to 118 unique proteins, have been identified by MALDI-TOF-MS or LC-ESI-IT-MS/MS, in which 37 protein spots representing 28 unique proteins have also been stained with Pro-Q Diamond, indicating that they are in phosphorylated forms. All the identified proteins were bioinformatically annotated in detail according to their physiochemical characteristics, subcellular location, and function. Most of the separated or identified protein spots are distributed in the area of mass 10-70 kDa and pI 5.0-8.0. The identified proteins include mainly cytoplasmic and nuclear proteins and some mitochondrial, endoplasmic reticulum, and membrane proteins. These proteins are classified into different functional groups such as structure and mobility proteins (21.2%), metabolic enzymes (16.9%), protein folding and metabolism proteins (13.6%), signaling proteins (14.4%), heat-shock proteins (7.6%), and other functional proteins (12.7%). While structure and mobility proteins are mostly represented by protein spots with high abundance, signaling proteins are mostly represented by protein spots with relatively low abundance. Such a 2-DE database for RMC, especially with many signaling proteins and phosphoproteins characterized, will provide a valuable resource for comparative proteomics analysis of normal and pathologic conditions affecting MC function or pathologic progress.

Animals↗
PubMed2003

Diversity of polyketide synthase gene sequences in Aspergillus species.

Fungal polyketide synthases are responsible for the biosynthesis of several mycotoxins and other secondary metabolites. The aim of our work was to investigate the diversity of polyketide synthases in Aspergillus species using two approaches: PCR amplification using oligonucleotide primers, and bioinformatics. Ketosynthase domain probes amplified DNA fragments of about 700 bp in each examined isolate. Sequences of these domains were aligned and analyzed by phylogenetic methods. The ketosynthase domain sequences were highly diverse indicating that they most probably represent polyketide synthases responsible for different functions. A. albertensis and A. niger ketosynthase domain sequences clustered together with sequences of genes required for pigment biosynthesis (wA) in A. nidulans and P. patulum, while the ketosynthase domain sequence of A. muricatus was most closely related to an A. parasiticus wA type domain sequence, and those of the A. ochraceus isolates formed a distinct clade on the tree. These sequences were highly homologous to an A. terreus naphthopyrone synthase gene. An Aspergillus fumigatus genomic database was also searched for ketosynthase domain sequences, which have been included in the phylogenetic analysis. Altogether 14 putative ketosynthase domain sequences were identified. Clustering of the ketosynthase domain sequences correlated well with the type of metabolites produced by the corresponding polyketide synthases. At least 8 clusters with putative ketosynthase domain sequences of unknown function have been identified. Further studies are in progress to clarify the role of some of the identified polyketide synthase genes.

Amino Acid Sequence↗
PubMed2002

The frameshift signal of HIV-1 involves a potential intramolecular triplex RNA structure.

The cis-acting mRNA elements that promote programmed -1 ribosomal frameshifting present a natural target for the rational design of antiretroviral chemotherapies. It has been commonly accepted that the HIV-1 frameshifting signal is special, because its downstream enhancer element consists of a simple mRNA stem loop rather than a more complex secondary structure such as a pseudoknot. Here we present three lines of evidence, bioinformatic, structural, and genetic, showing that the biologically relevant HIV-1 frameshift signal contains a complex RNA structure that likely includes an extended RNA triple-helix region. We suggest that the potential intramolecular triplex structure is essential for viral propagation and viability, and that small molecules targeted to this RNA structure may possess antiretroviral activities.

Anti-HIV Agents↗
PubMed2006

Functional bioinformatics for Arabidopsis thaliana.

MOTIVATION: The genome of Arabidopsis thaliana, which has the best understood plant genome, still has approximately one-third of its genes with no functional annotation at all from either MIPS or TAIR. We have applied our Data Mining Prediction (DMP) method to the problem of predicting the functional classes of these protein sequences. This method is based on using a hybrid machine-learning/data-mining method to identify patterns in the bioinformatic data about sequences that are predictive of function. We use data about sequence, predicted secondary structure, predicted structural domain, InterPro patterns, sequence similarity profile and expressions data. RESULTS: We predicted the functional class of a high percentage of the Arabidopsis genes with currently unknown function. These predictions are interpretable and have good test accuracies. We describe in detail seven of the rules produced.

Algorithms↗
PubMedDate not supplied

[New genes on human chromosome 7: bioinformatic analysis of a gene cluster from the POLR2J family].

Four independent genes encoding various variants of the hRPB11 subunit of Homo sapiens RNA polymerase II were revealed in human chromosome 7. Three genes (POLR2J1, POLR2J2, and POLR2J3) form a cluster of total length 214530 bp in the genetic locus 7q22.1 on the long arm of chromosome 7 (contig NT_007933). The fourth gene (POLR2J4, 31040 bp) was localized in the cytogenetic locus 7p13 of the short arm of chromosome 7 (contig NT_007819). An analysis enabled us to refine dissimilar experimental data on the mapping of the hRPB11 subunit gene on chromosome 7. In particular, the presence of three sites of its localization according to data on hybridization with fluorescent-labeled probes (the FISH method) was explained. It was established that, upon the expression of the four human POLR2J genes, at least 14 types of mature mRNAs encoding somewhat differing hRPB11 isoforms can be synthesized. Eleven of these mRNAs were revealed (as full-length copies or clearly identifiable fragments) in the available databases of expressed sequence tags and cDNAs. The most probable scheme of origination of the multiple genes of the POLR2J family, as a result of three consecutive segmented duplications increasing in size, was proposed and substantiated. On the basis of the scheme, some assumptions on the pathways of evolution of separate human genes and the mechanisms of generation of protein diversity in higher eukaryotes were made. The English version of the paper: Russian Journal of Bioorganic Chemistry, 2004, vol. 30, no. 6; see also http://www.maik.ru.

Chromosome Mapping↗
PubMedDate not supplied

Mapping molecular responses to xenoestrogens through Gene Ontology and pathway analysis of toxicogenomic data.

The recent sequencing of mammalian genomes has driven the development of genomic technologies, including microarray-based gene expression profiling, that allow simultaneous measurement of the expression levels of thousands of genes. Gene expression profiling applied to toxicology (toxicogenomics) has the potential to reveal, holistically, the molecular pathways and cellular processes that mediate the adverse responses to a toxicant. However, the initial output of a toxicogenomics experiment consists of a list of genes whose expression is altered upon toxicant exposure. In order to interpret these data in a biological context, new bioinformatic methods must be developed to place gene expression changes in the context of the underlying pathways and processes affected. One emerging approach is the application of Gene Ontology (GO) mapping and pathway analysis to gene expression profiling data. The utility of this in mechanistic toxicology will be illustrated using examples in which GO mapping of toxicogenomic data has provided novel insights into the molecular mechanisms induced by exposure to xenoestrogens.

Animals↗
PubMed2005

Convergent functional genomics: a Bayesian candidate gene identification approach for complex disorders.

Identifying genes involved in complex neuropsychiatric disorders through classic human genetic approaches has proven difficult. To overcome that barrier, we have developed a translational approach called Convergent Functional Genomics (CFG), which cross-matches animal model microarray gene expression data with human genetic linkage data as well as human postmortem brain data and biological role data, as a Bayesian way of cross-validating findings and reducing uncertainty. Our approach produces a short list of high probability candidate genes out of the hundreds of genes changed in microarray datasets and the hundreds of genes present in a linkage peak chromosomal area. These genes can then be prioritized, pursued, and validated in an individual fashion using: (1) human candidate gene association studies and (2) cell culture and mouse transgenic models. Further bioinformatics analysis of groups of genes identified through CFG leads to insights into pathways and mechanisms that may be involved in the pathophysiology of the illness studied. This simple but powerful approach is likely generalizable to other complex, non-neuropsychiatric disorders, for which good animal models, as well as good human genetic linkage datasets and human target tissue gene expression datasets exist.

Animals↗
PubMed2004

Convergent analysis of cDNA and short oligomer microarrays, mouse null mutants and bioinformatics resources to study complex traits.

Gene expression data sets have recently been exploited to study genetic factors that modulate complex traits. However, it has been challenging to establish a direct link between variation in patterns of gene expression and variation in higher order traits such as neuropharmacological responses and patterns of behavior. Here we illustrate an approach that combines gene expression data with new bioinformatics resources to discover genes that potentially modulate behavior. We have exploited three complementary genetic models to obtain convergent evidence that differential expression of a subset of genes and molecular pathways influences ethanol-induced conditioned taste aversion (CTA). As a first step, cDNA microarrays were used to compare gene expression profiles of two null mutant mouse lines with difference in ethanol-induced aversion. Mice lacking a functional copy of G protein-gated potassium channel subunit 2 (Girk2) show a decrease in the aversive effects of ethanol, whereas preproenkephalin (Penk) null mutant mice show the opposite response. We hypothesize that these behavioral differences are generated in part by alterations in expression downstream of the null alleles. We then exploited the WebQTL databases to examine the genetic covariance between mRNA expression levels and measurements of ethanol-induced CTA in BXD recombinant inbred (RI) strains. Finally, we identified a subset of genes and functional groups associated with ethanol-induced CTA in both null mutant lines and BXD RI strains. Collectively, these approaches highlight the phosphatidylinositol signaling pathway and identify several genes including protein kinase C beta isoform and preproenkephalin in regulation of ethanol- induced conditioned taste aversion. Our results point to the increasing potential of the convergent approach and biological databases to investigate genetic mechanisms of complex traits.

Animals↗
PubMed2004

Prediction of protein subcellular locations by GO-FunD-PseAA predictor.

The localization of a protein in a cell is closely correlated with its biological function. With the explosion of protein sequences entering into DataBanks, it is highly desired to develop an automated method that can fast identify their subcellular location. This will expedite the annotation process, providing timely useful information for both basic research and industrial application. In view of this, a powerful predictor has been developed by hybridizing the gene ontology approach [Nat. Genet. 25 (2000) 25], functional domain composition approach [J. Biol. Chem. 277 (2002) 45765], and the pseudo-amino acid composition approach [Proteins Struct. Funct. Genet. 43 (2001) 246; Erratum: ibid. 44 (2001) 60]. As a showcase, the recently constructed dataset [Bioinformatics 19 (2003) 1656] was used for demonstration. The dataset contains 7589 proteins classified into 12 subcellular locations: chloroplast, cytoplasmic, cytoskeleton, endoplasmic reticulum, extracellular, Golgi apparatus, lysosomal, mitochondrial, nuclear, peroxisomal, plasma membrane, and vacuolar. The overall success rate of prediction obtained by the jackknife cross-validation was 92%. This is so far the highest success rate performed on this dataset by following an objective and rigorous cross-validation procedure.

Algorithms↗
PubMed2003

Statistical bioinformatic methods in microbial genome analysis.

It is probable that, increasingly, genome investigations are going to be based on statistical formalization. This review summarizes the state of art and potentiality of using statistics in microbial genome analysis. First, I focus on recent advances in functional genomics, such as finding genes and operons, identifying gene conversion events, detecting DNA replication origins and analysing regulatory sites. Then I describe how to use phylogenetic methods in genome analysis and methods for genome-wide scanning for positively selected amino acids. I conclude with speculations on the future course of genome statistical modeling.

Computational Biology↗