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Timothy E Allen

Publications and source records attributed to Timothy E Allen.

6 recordsLinked to original sources
PubMed2006

Long-range periodic patterns in microbial genomes indicate significant multi-scale chromosomal organization.

Genome organization can be studied through analysis of chromosome position-dependent patterns in sequence-derived parameters. A comprehensive analysis of such patterns in prokaryotic sequences and genome-scale functional data has yet to be performed. We detected spatial patterns in sequence-derived parameters for 163 chromosomes occurring in 135 bacterial and 16 archaeal organisms using wavelet analysis. Pattern strength was found to correlate with organism-specific features such as genome size, overall GC content, and the occurrence of known motility and chromosomal binding proteins. Given additional functional data for Escherichia coli, we found significant correlations among chromosome position dependent patterns in numerous properties, some of which are consistent with previously experimentally identified chromosome macrodomains. These results demonstrate that the large-scale organization of most sequenced genomes is significantly nonrandom, and, moreover, that this organization is likely linked to genome size, nucleotide composition, and information transfer processes. Constraints on genome evolution and design are thus not solely dependent upon information content, but also upon an intricate multi-parameter, multi-length-scale organization of the chromosome.

Bacterial Proteins↗
PubMed2005

Immobilization of Escherichia coli RNA polymerase and location of binding sites by use of chromatin immunoprecipitation and microarrays.

The genome-wide location of RNA polymerase binding sites was determined in Escherichia coli using chromatin immunoprecipitation and microarrays (chIP-chip). Cross-linked chromatin was isolated in triplicate from rifampin-treated cells, and DNA bound to RNA polymerase was precipitated with an antibody specific for the beta' subunit. The DNA was amplified and hybridized to "tiled" oligonucleotide microarrays representing the whole genome at 25-bp resolution. A total of 1,139 binding sites were detected and evaluated by comparison to gene expression data from identical conditions and to 961 promoters previously identified by established methods. Of the detected binding sites, 418 were located within 1,000 bp of a known promoter, leaving 721 previously unknown RNA polymerase binding sites. Within 200 bp, we were able to detect 51% (189/368) of the known sigma70-specific promoters occurring upstream of an expressed open reading frame and 74% (273/368) within 1,000 bp. Conversely, many known promoters were not detected by chIP-chip, leading to an estimated 26% negative-detection rate. Most of the detected binding sites could be associated with expressed transcription units, but 299 binding sites occurred near inactive transcription units. This map of RNA polymerase binding sites represents a foundation for studies of transcription factors in E. coli and an important evaluation of the chIP-chip technique.

Base Sequence↗
PubMed2003

Sequence-based analysis of metabolic demands for protein synthesis in prokaryotes.

Constraints-based models for microbial metabolism can currently be constructed on a genome-scale. These models do not account for RNA and protein synthesis. A scalable formalism to describe translation and transcription that can be integrated with the existing metabolic models is thus needed. Here, we developed such a formalism. The fundamental protein synthesis network described by this formalism was analysed via extreme pathway and flux balance analyses. The protein synthesis network exhibited one extreme pathway per messenger RNA synthesized and one extreme pathway per protein synthesized. The key parameters in this network included promoter strengths, messenger RNA half-lives, and the availability of nucleotide triphosphates, amino acids, RNA polymerase, and active ribosomes. Given these parameters, we were able to calculate a cell's material and energy expenditures for protein synthesis using a flux balance approach. The framework provided herein can subsequently be integrated with genome-scale metabolic models, providing a sequence-based accounting of the metabolic demands resulting from RNA and protein polymerization.

Amino Acids↗
PubMed2003

Genome-scale analysis of the uses of the Escherichia coli genome: model-driven analysis of heterogeneous data sets.

The recent availability of heterogeneous high-throughput data types has increased the need for scalable in silico methods with which to integrate data related to the processes of regulation, protein synthesis, and metabolism. A sequence-based framework for modeling transcription and translation in prokaryotes has been established and has been extended to study the expression state of the entire Escherichia coli genome. The resulting in silico analysis of the expression state highlighted three facets of gene expression in E. coli: (i) the metabolic resources required for genome expression and protein synthesis were found to be relatively invariant under the conditions tested; (ii) effective promoter strengths were estimated at the genome scale by using global mRNA abundance and half-life data, revealing genes subject to regulation under the experimental conditions tested; and (iii) large-scale genome location-dependent expression patterns with approximately 600-kb periodicity were detected in the E. coli genome based on the 49 expression data sets analyzed. These results support the notion that a structured model-driven analysis of expression data yields additional information that can be subjected to commonly used statistical analyses. The integration of heterogeneous genome-scale data (i.e., sequence, expression data, and mRNA half-life data) is readily achieved in the context of an in silico model.

Bacterial Proteins↗