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At least 289 records · Page 16Linked to original sources
PubMed2005

New ways to admire zebrafish: progress in functional genomics research methodology.

The main challenge of the post-genomic era is to functionally characterize genes identified by the genome sequencing projects. Model organisms, including zebrafish, are indispensable for this demanding task. Zebrafish has been successfully incorporated into large-scale genetic screens due to the optical clarity of the embryos and their accessibility to various experimental techniques throughout development. The attractiveness of the zebrafish as a model organism is enhanced by the availability of continuously improving genomic tools and methodologies for functional characterization of the gene. This article will highlight the current techniques used in the field, with the focus on transgenesis.

Animals↗
PubMed2005

Zebrafish and Xenopus tadpoles: small animal models to study angiogenesis and lymphangiogenesis.

Small vertebrate organisms have emerged as key players in the post-genomic era for the functional characterization of novel genes on a high-throughput scale. In this context, the zebrafish embryos and Xenopus tadpoles represent attractive and valuable models to rapidly identify and characterize novel genes involved in angiogenesis and lymphangiogenesis-a significant task with a consequent impact on the design of more effective therapeutic strategies. The advantages of these two models will be discussed in the present review.

Animals↗
PubMed2005

WormBase: a comprehensive data resource for Caenorhabditis biology and genomics.

WormBase (http://www.wormbase.org), the model organism database for information about Caenorhabditis elegans and related nematodes, continues to expand in breadth and depth. Over the past year, WormBase has added multiple large-scale datasets including SAGE, interactome, 3D protein structure datasets and NCBI KOGs. To accommodate this growth, the International WormBase Consortium has improved the user interface by adding new features to aid in navigation, visualization of large-scale datasets, advanced searching and data mining. Internally, we have restructured the database models to rationalize the representation of genes and to prepare the system to accept the genome sequences of three additional Caenorhabditis species over the coming year.

Animals↗
PubMed2002

Computational function assignment for potential drug targets: from single genes to cellular systems.

Biomedical science is currently undergoing an epoch-marking transition from its classical phase to the post-genome era. The outstanding success of world-wide genome sequencing efforts, evidenced by the recent publication of the draft of the human genome, together with the completion of several genomes of eukaryotic model organisms and the availability of microbial genome sequences, is opening up data sources of unprecedented scale for drug discovery. Furthermore, the elucidation of genome expression states through transcriptomic and proteomic techniques is playing a crucial role in the characterisation of disease at the molecular level. At the same time, our still very limited knowledge of the biological functions of genes and proteins at different levels of cellular organisation is preventing full exploitation of the available data. This review will discuss current computational techniques for function prediction based on the sequence-structure-function paradigm. Newly emerging approaches aimed at gaining an expanded understanding of function through integration of data from various sources and modelling of complex cellular systems will also be highlighted.

Amino Acid Sequence↗
PubMed2003

Mouse genetic approaches to access pathways important in retinal function.

The laboratory mouse is a premier research tool for understanding human biology and disease. With the availability of the first complete draft sequences of the mouse genome and ongoing large scale screens for new mouse mutations to serve as novel models, the usefulness of the mouse will only increase. The vision research field, in particular, which has identified many human diseases for which no models are yet available, will benefit from the newly developed tools in mouse molecular genetics.

Animals↗
PubMed2002

Segmental paralogy in the human genome: a large-scale triplication on 1p, 6p, and 21q.

Few cases of large-scale segmental paralogy have been reported in the human genome. We have identified a large (approximately 500 kb) segment on human chromosome (HC) 21 (21q22) that is triplicated on HC 1 (1p35) and HC 6 (6p12-21). We also identified a new member of CLIC (Chloride Intracellular Channel) family on 21q, namely CLIC6. All three segments appear to include three functional members of three different gene families: DSCR1-like (Down Syndrome Candidate Region 1-like), CLIC, and AML/Runt (Acute Myeloid Leukemia/Runt). Molecular evolution analysis shows a common evolutionary origin for the triplicated regions. This finding of a further large-scale genomic triplication that went undetected at previously systematic automated searches provides a new model for gene divergence study and underlines the need for new tools to effectively detect inter-chromosomal similarity. An algorithm to overcome current limitations is proposed.

Amino Acid Sequence↗
PubMed2006

Exploring candidate genes for human brain diseases from a brain-specific gene network.

It is believed that large numbers of genes are involved in common human brain diseases. Here, we propose a novel computational strategy for simultaneously identifying multiple candidate genes for genetic human brain diseases from a brain-specific gene network-level perspective. By integrating diverse genomic and proteomic datasets based on Bayesian statistical model, we built a large-scale human brain-specific gene network. Based on this network and minor prior knowledge of a specific brain disease, we can effectively identify multiple candidate genes for this disease. When four known Alzheimer's disease genes were used as the prior knowledge, among the top 46 high-scoring genes that we have found, 37 were previously reported to be associated with Alzheimer's disease. And the higher score a gene has, the more likely this gene is a disease-related one. The results suggest that the proposed method is effective, convenient, and applicable in the future genetic studies.

Alzheimer Disease↗
PubMed2004

Exploring protein fold space by secondary structure prediction using data distribution method on Grid platform.

MOTIVATION: Since the newly developed Grid platform has been considered as a powerful tool to share resources in the Internet environment, it is of interest to demonstrate an efficient methodology to process massive biological data on the Grid environments at a low cost. This paper presents an efficient and economical method based on a Grid platform to predict secondary structures of all proteins in a given organism, which normally requires a long computation time through sequential execution, by means of processing a large amount of protein sequence data simultaneously. From the prediction results, a genome scale protein fold space can be pursued. RESULTS: Using the improved Grid platform, the secondary structure prediction on genomic scale and protein topology derived from the new scoring scheme for four different model proteomes was presented. This protein fold space was compared with structures from the Protein Data Bank, database and it showed similarly aligned distribution. Therefore, the fold space approach based on this new scoring scheme could be a guideline for predicting a folding family in a given organism.

Computing Methodologies↗
PubMed2004

Mathematical models in microbial systems biology.

Systems biology aims at an understanding of the genotype-phenotype relations brought about by cellular networks. Mathematical models as formal representations are central for handling the associated complexity. Recently, model-based analysis of microorganisms has begun, for instance, to reveal functional modules in metabolic and transcriptional networks, to predict cellular behavior from genome-scale physicochemical constraints, and to suggest novel design principles for well-studied bacterial subsystems such as chemotaxis. Guided by common themes such as modularity, optimality and robustness, iterative model development promises further progress towards a system-level understanding.

Adaptation, Physiological↗
PubMed2000

A polymer, random walk model for the size-distribution of large DNA fragments after high linear energy transfer radiation.

DNA double-strand breaks (DSBs) produced by densely ionizing radiation are not located randomly in the genome: recent data indicate DSB clustering along chromosomes. Stochastic DSB clustering at large scales, from > 100 Mbp down to < 0.01 Mbp, is modeled using computer simulations and analytic equations. A random-walk, coarse-grained polymer model for chromatin is combined with a simple track structure model in Monte Carlo software called DNAbreak and is applied to data on alpha-particle irradiation of V-79 cells. The chromatin model neglects molecular details but systematically incorporates an increase in average spatial separation between two DNA loci as the number of base-pairs between the loci increases. Fragment-size distributions obtained using DNAbreak match data on large fragments about as well as distributions previously obtained with a less mechanistic approach. Dose-response relations, linear at small doses of high linear energy transfer (LET) radiation, are obtained. They are found to be non-linear when the dose becomes so large that there is a significant probability of overlapping or close juxtaposition, along one chromosome, for different DSB clusters from different tracks. The non-linearity is more evident for large fragments than for small. The DNAbreak results furnish an example of the RLC (randomly located clusters) analytic formalism, which generalizes the broken-stick fragment-size distribution of the random-breakage model that is often applied to low-LET data.

Animals↗
PubMed1998

How to interpret an anonymous bacterial genome: machine learning approach to gene identification.

In this report we address the problem of accurate statistical modeling of DNA sequences, either coding or noncoding, for a bacterial species whose genome (or a large portion) was sequenced but not yet characterized experimentally. Availability of these models is critical for successful solution of the genome annotation task by statistical methods of gene finding. We present the method, GeneMark-Genesis, which learns the parameters of Markov models of protein-coding and noncoding regions from anonymous bacterial genomic sequence. These models are subsequently used in the GeneMark and GeneMark.hmm gene-finding programs. Although there is basically one model of a noncoding region for a given genome, several models of protein-coding region are automatically obtained by GeneMark-Genesis. The diversity of protein-coding models reflects the diversity of oligonucleotide compositions, particularly the diversity of codon usage strategies observed in genes from one and the same genome. In the simplest and the most important case, there are just two gene models-typical and atypical ones. We show that the atypical model allows one to predict genes that escape identification by the typical model. Many genes predicted by the atypical model appear to be horizontally transferred genes. The early versions of GeneMark-Genesis were used for annotating the genomes of Methanoccocus jannaschii and Helicobacter pylori. We report the results of accuracy testing of the full-scale version of GeneMark-Genesis on 10 completely sequenced bacterial genomes. Interestingly, the GeneMark.hmm program that employed the typical and atypical models defined by GeneMark-Genesis was able to predict 683 new atypical genes with 176 of them confirmed by similarity search.

Algorithms↗
PubMed2004

Plasmodium interspersed repeats: the major multigene superfamily of malaria parasites.

Functionally related homologues of known genes can be difficult to identify in divergent species. In this paper, we show how multi-character analysis can be used to elucidate the relationships among divergent members of gene superfamilies. We used probabilistic modelling in conjunction with protein structural predictions and gene-structure analyses on a whole-genome scale to find gene homologies that are missed by conventional similarity-search strategies and identified a variant gene superfamily in six species of malaria (Plasmodium interspersed repeats, pir). The superfamily includes rif in P.falciparum, vir in P.vivax, a novel family kir in P.knowlesi and the cir/bir/yir family in three rodent malarias. Our data indicate that this is the major multi-gene family in malaria parasites. Protein localization of products from pir members to the infected erythrocyte membrane in the rodent malaria parasite P.chabaudi, demonstrates phenotypic similarity to the products of pir in other malaria species. The results give critical insight into the evolutionary adaptation of malaria parasites to their host and provide important data for comparative immunology between malaria parasites obtained from laboratory models and their human counterparts.

Amino Acid Motifs↗
PubMedDate not supplied

Detection of protein coding sequences using a mixture model for local protein amino acid sequence.

Locating protein coding regions in genomic DNA is a critical step in accessing the information generated by large scale sequencing projects. Current methods for gene detection depend on statistical measures of content differences between coding and noncoding DNA in addition to the recognition of promoters, splice sites, and other regulatory sites. Here we explore the potential value of recurrent amino acid sequence patterns 3-19 amino acids in length as a content statistic for use in gene finding approaches. A finite mixture model incorporating these patterns can partially discriminate protein sequences which have no (detectable) known homologs from randomized versions of these sequences, and from short (< or = 50 amino acids) non-coding segments extracted from the S. cerevisiea genome. The mixture model derived scores for a collection of human exons were not correlated with the GENSCAN scores, suggesting that the addition of our protein pattern recognition module to current gene recognition programs may improve their performance.

Algorithms↗
PubMed2001

Genomics, complexity and drug discovery: insights from Boolean network models of cellular regulation.

The completion of the first draft of the human genome sequence has revived the old notion that there is no one-to-one mapping between genotype and phenotype. It is now becoming clear that to elucidate the fundamental principles that govern how genomic information translates into organismal complexity, we must overcome the current habit of ad hoc explanations and instead embrace novel, formal concepts that will involve computer modelling. Most modelling approaches aim at recreating a living system via computer simulation, by including as much details as possible. In contrast, the Boolean network model reviewed here represents an abstraction and a coarse-graining, such that it can serve as a simple, efficient tool for the extraction of the very basic design principles of molecular regulatory networks, without having to deal with all the biochemical details. We demonstrate here that such a discrete network model can help to examine how genome-wide molecular interactions generate the coherent, rule-like behaviour of a cell - the first level of integration in the multi-scale complexity of the living organism. Hereby the various cell fates, such as differentiation, proliferation and apoptosis, are treated as attractor states of the network. This modelling language allows us to integrate qualitative gene and protein interaction data to explain a series of hitherto non-intuitive cell behaviours. As the human genome project starts to reveal the limits of the current simplistic 'one gene - one function - one target' paradigm, the development of conceptual tools to increase our understanding of how the intricate interplay of genes gives rise to a global 'biological observable' will open a new perspective for post-genomic drug target discovery.

Animals↗
PubMed2004

The Pseudomonas aeruginosa PA01 gene collection.

Pseudomonas aeruginosa, a common inhabitant of soil and water, is an opportunistic pathogen of growing clinical relevance. Its genome, one of the largest among bacteria [5570 open reading frames (ORFs)] approaches that of simple eukaryotes. We have constructed a comprehensive gene collection for this organism utilizing the annotated genome of P. aeruginosa PA01 and a highly automated and laboratory information management system (LIMS)-supported production line. All the individual ORFs have been successfully PCR-amplified and cloned into a recombination-based cloning system. We have isolated and archived four independent isolates of each individual ORF. Full sequence analysis of the first isolate for one-third of the ORFs in the collection has been completed. We used two sets of genes from this repository for high-throughput expression and purification of recombinant proteins in different systems. The purified proteins have been used to set up biochemical and immunological assays directed towards characterization of histidine kinases and identification of bacterial proteins involved in the immune response of cystic fibrosis patients. This gene repository provides a powerful tool for proteome- and genome-scale research of this organism, and the strategies adopted to generate this repository serve as a model for building clone sets for other bacteria.

Bacterial Proteins↗
PubMed2005

Molecular signatures of natural selection.

There is an increasing interest in detecting genes, or genomic regions, that have been targeted by natural selection. The interest stems from a basic desire to learn more about evolutionary processes in humans and other organisms, and from the realization that inferences regarding selection may provide important functional information. This review provides a nonmathematical description of the issues involved in detecting selection from DNA sequences and SNP data and is intended for readers who are not familiar with population genetic theory. Particular attention is placed on issues relating to the analysis of large-scale genomic data sets.

Animals↗
PubMed2003

Assessing the performance of the haplotype block model of linkage disequilibrium.

Several recent studies have suggested that linkage disequilibrium (LD) in the human genome has a fundamentally "blocklike" structure. However, thus far there has been little formal assessment of how well the haplotype block model captures the underlying structure of LD. Here we propose quantitative criteria for assessing how blocklike LD is and apply these criteria to both real and simulated data. Analyses of several large data sets indicate that real data show a partial fit to the haplotype block model; some regions conform quite well, whereas others do not. Some improvement could be obtained by genotyping higher marker densities but not by increasing the number of samples. Nonetheless, although the real data are only moderately blocklike, our simulations indicate that, under a model of uniform recombination, the structure of LD would actually fit the block model much less well. Simulations of a model in which much of the recombination occurs in narrow hotspots provide a much better fit to the observed patterns of LD, suggesting that there is extensive fine-scale variation in recombination rates across the human genome.

Computer Simulation↗
PubMed1998

Method for prediction of protein function from sequence using the sequence-to-structure-to-function paradigm with application to glutaredoxins/thioredoxins and T1 ribonucleases.

The practical exploitation of the vast numbers of sequences in the genome sequence databases is crucially dependent on the ability to identify the function of each sequence. Unfortunately, current methods, including global sequence alignment and local sequence motif identification, are limited by the extent of sequence similarity between sequences of unknown and known function; these methods increasingly fail as the sequence identity diverges into and beyond the twilight zone of sequence identity. To address this problem, a novel method for identification of protein function based directly on the sequence-to-structure-to-function paradigm is described. Descriptors of protein active sites, termed "fuzzy functional forms" or FFFs, are created based on the geometry and conformation of the active site. By way of illustration, the active sites responsible for the disulfide oxidoreductase activity of the glutaredoxin/thioredoxin family and the RNA hydrolytic activity of the T1 ribonuclease family are presented. First, the FFFs are shown to correctly identify their corresponding active sites in a library of exact protein models produced by crystallography or NMR spectroscopy, most of which lack the specified activity. Next, these FFFs are used to screen for active sites in low-to-moderate resolution models produced by ab initio folding or threading prediction algorithms. Again, the FFFs can specifically identify the functional sites of these proteins from their predicted structures. The results demonstrate that low-to-moderate resolution models as produced by state-of-the-art tertiary structure prediction algorithms are sufficient to identify protein active sites. Prediction of a novel function for the gamma subunit of a yeast glycosyl transferase and prediction of the function of two hypothetical yeast proteins whose models were produced via threading are presented. This work suggests a means for the large-scale functional screening of genomic sequence databases based on the prediction of structure from sequence, then on the identification of functional active sites in the predicted structure.

Algorithms↗