4,660 research outputs found
BClass: A Bayesian Approach Based on Mixture Models for Clustering and Classification of Heterogeneous Biological Data
No full textBased on mixture models, we present a Bayesian method (called BClass) to classify biological entities (e.g. genes) when variables of quite heterogeneous nature are analyzed. Various statistical distributions are used to model the continuous/categorical data commonly produced by genetic experiments and large-scale genomic projects. We calculate the posterior probability of each entry to belong to each element (group) in the mixture. In this way, an original set of heterogeneous variables is transformed into a set of purely homogeneous characteristics represented by the probabilities of each entry to belong to the groups. The number of groups in the analysis is controlled dynamically by rendering the groups as 'alive' and 'dormant' depending upon the number of entities classified within them. Using standard Metropolis-Hastings and Gibbs sampling algorithms, we constructed a sampler to approximate posterior moments and grouping probabilities. Since this method does not require the definition of similarity measures, it is especially suitable for data mining and knowledge discovery in biological databases. We applied BClass to classify genes in RegulonDB, a database specialized in information about the transcriptional regulation of gene expression in the bacterium Escherichia coli. The classification obtained is consistent with current knowledge and allowed prediction of missing values for a number of genes. BClass is object-oriented and fully programmed in Lisp-Stat. The output grouping probabilities are analyzed and interpreted using graphical (dynamically linked plots) and query-based approaches. We discuss the advantages of using Lisp-Stat as a programming language as well as the problems we faced when the data volume increased exponentially due to the ever-growing number of genomic projects.
Gene Regulation and Metabolism. Post-Genomic Computational Approaches
No full textCollado-Vides J, Hofestädt R, eds. Gene Regulation and Metabolism. Post-Genomic Computational Approaches. Cambridge, Mass.: MIT Press; 2002
04281 Abstracts Collection – Integrative Bioinformatics - Aspects of the Virtual Cell
No full textFrom 04.07.04 to 09.07.04, the Dagstuhl Seminar 04281 ``Integrative Bioinformatics - Aspects of the Virtual Cell'' was held in the International Conference and Research Center (IBFI), Schloss Dagstuhl.
During the seminar, several participants presented their current
research, and ongoing work and open problems were discussed. Abstracts of
the presentations given during the seminar as well as abstracts of
seminar results and ideas are put together in this paper. The first section
describes the seminar topics and goals in general.
Links to extended abstracts or full papers are provided, if available
Modeling and simulation of gene regulation and metabolic pathways
No full textCollado-Vides J, Hofestädt R, Mavrovouniotis M, Michal G. Modeling and simulation of gene regulation and metabolic pathways. BioSystems. 1999;49(1):79-82
Modelling and Simulation of Gene and Cell Regulation
No full textCollado-Vides J, Hofestädt R, Löffler M, Mavrovouniotis M, eds. Modelling and Simulation of Gene and Cell Regulation. Dagstuhl-Seminar-Report. 1995;(130)
Modelling and Simulation of Gene and Cell Regulation and Metabolic Pathways (Dagstuhl Seminar 98251)
No full textInformation Fusion and Metabolic Network Control
No full textFreier A, Hofestädt R, Lange M, Scholz U. Information Fusion and Metabolic Network Control. In: Collado-Vides J, Hofestädt R, eds. Gene Regulation and Metabolism. Cambridge, Mass.: MIT Press; 2002: 49-84
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Comparing the fine structure of promoter regions across bacterial species
No full textThe selective mechanisms operating in regulatory regions of bacterial genomes are poorly understood. In Escherichia coli, we have found that regulatory regions contain high densities of overlapping and probably competing promoter-like signals, in contrast to coding regions and regions located between convergently-transcribed genes. Functional promoter sites identified experimentally are often found in the subregions of highest density of signals, even when individual sites with higher binding affinity for RNA polymerase exist elsewhere within the region [Huerta & Collado-Vides 2003]. In order to explore whether this trend discovered in E. coli promoter regions is common in other bacterial species, we conducted similar analyses for a representative set containing 40 additional genomes belonging to different genera across all major bacterial phyla. This comparison is validated by the fact that RNAPs are evolutionarily conserved across all bacteria
Structural properties of prokaryotic promoter regions correlate with functional features.
No full textThe structural properties of the DNA molecule are known to play a critical role in transcription. In this paper, the structural profiles of promoter regions were studied within the context of their diversity and their function for eleven prokaryotic species; Escherichia coli, Klebsiella pneumoniae, Salmonella Typhimurium, Pseudomonas auroginosa, Geobacter sulfurreducens Helicobacter pylori, Chlamydophila pneumoniae, Synechocystis sp., Synechoccocus elongates, Bacillus anthracis, and the archaea Sulfolobus solfataricus. The main anchor point for these promoter regions were transcription start sites identified through high-throughput experiments or collected within large curated databases. Prokaryotic promoter regions were found to be less stable and less flexible than the genomic mean across all studied species. However, direct comparison between species revealed differences in their structural profiles that can not solely be explained by the difference in genomic GC content. In addition, comparison with functional data revealed that there are patterns in the promoter structural profiles that can be linked to specific functional loci, such as sigma factor regulation or transcription factor binding. Interestingly, a novel structural element clearly visible near the transcription start site was found in genes associated with essential cellular functions and growth in several species. Our analyses reveals the great diversity in promoter structural profiles both between and within prokaryotic species. We observed relationships between structural diversity and functional features that are interesting prospects for further research to yet uncharacterized functional loci defined by DNA structural properties
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