1,721,045 research outputs found
Modelling and Simulation of Gene and Cell Regulation and Metabolic Pathways
No full textCollado-Vides J, Hofestädt R, Mavrovouniotis M, Michal G, eds. Modelling and Simulation of Gene and Cell Regulation and Metabolic Pathways. Dagstuhl-Seminar-Report. 1998;215
COLOMBOS: an ever expanding collection of bacterial expression compendia.
No full textCOLOMBOS is an publically available access portal to comprehensive organism-specific cross-platform expression compendia for bacterial organisms. It provides a suite of tools for exploring, analyzing, and visualizing the data within these compendia. The expression compendia themselves are built based on a propriety methodology that is unique in directly
combining the data from different technological platforms. COLOMBOS also incorporate extensive
annotations for both genes and experimental conditions; these heterogeneous data are functionally integrated in the analysis tools to interactively browse and query the compendia not only for specific genes or experiments, but also metabolic pathways, transcriptional regulation mechanisms, experimental conditions, biological processes, etc. Several improvements have been made. Content wise, we have invested in the development of a
compendia creation and management system that has enabled us to greatly expand existing compendia (Escherichia coli, Bacillus subtilis, and Salmonella Typhimurium) as well as add compendia for other species. Additionally, the current version supports the inclusion of RNAseq data. Functionally, we have revamped the interface with new interactive visualization and analysis tools, a bicluster tree algortihm for discovering complex coexpression patterns around a set of query genes, and inclusion of noise models for measurement errors, enabling analysis of differential
expression with measures of statistical significance.
This work is relevant to a large community of microbiologists by facilitating the use of publicly
available genome-wide expression data to support their research, as well as providing a useful resource for top-down systems biology application
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
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)
Information 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
COLOMBOS v2.0: an ever expanding collection of bacterial expression compendia
Get PDFThe COLOMBOS database (http://www.colombos.net) features comprehensive organism-specific cross-platform gene expression compendia of several bacterial model organisms and is supported by a fully interactive web portal and an extensive web API. COLOMBOS was originally published in PLoS One, and COLOMBOS v2.0 includes both an update of the expression data, by expanding the previously available compendia and by adding compendia for several new species, and an update of the surrounding functionality, with improved search and visualization options and novel tools for programmatic access to the database. The scope of the database has also been extended to incorporate RNA-seq data in our compendia by a dedicated analysis pipeline. We demonstrate the validity and robustness of this approach by comparing the same RNA samples measured in parallel using both microarrays and RNA-seq. As far as we know, COLOMBOS currently hosts the largest homogenized gene expression compendia available for seven bacterial model organism
Structural properties of prokaryotic promoter regions correlate with functional features.
Get PDFThe 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
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
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