The 4,639,221-base pair sequence of Escherichia coli K-12 is presented. Of 4288 protein-coding genes annotated, 38 percent have no attributed function. Comparison with five other sequenced microbes reveals ubiquitous as well as narrowly distributed gene families; many families of similar genes within E. coli are also evident. The largest family of paralogous proteins contains 80 ABC transporters. The genome as a whole is strikingly organized with respect to the local direction of replication; guanines, oligonucleotides possibly related to replication and recombination, and most genes are so oriented. The genome also contains insertion sequence (IS) elements, phage remnants, and many other patches of unusual composition indicating genome plasticity through horizontal transfer.

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The Complete Genome Sequence of Escherichia coli K-12

Bacillus subtilis is the best-characterized member of the Gram-positive bacteria. Its genome of 4,214,810 base pairs comprises 4,100 protein-coding genes. Of these protein-coding genes, 53% are represented once, while a quarter of the genome corresponds to several gene families that have been greatly expanded by gene duplication, the largest family containing 77 putative ATP-binding transport proteins. In addition, a large proportion of the genetic capacity is devoted to the utilization of a variety of carbon sources, including many plant-derived molecules. The identification of five signal peptidase genes, as well as several genes for components of the secretion apparatus, is important given the capacity of Bacillus strains to secrete large amounts of industrially important enzymes. Many of the genes are involved in the synthesis of secondary metabolites, including antibiotics, that are more typically associated with Streptomyces species. The genome contains at least ten prophages or remnants of prophages, indicating that bacteriophage infection has played an important evolutionary role in horizontal gene transfer, in particular in the propagation of bacterial pathogenesis.

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The complete genome sequence of the Gram-positive bacterium Bacillus subtilis

Summary: Artemis is a DNA sequence visualization and annotation tool that allows the results of any analysis or sets of analyses to be viewed in the context of the sequence and its six-frame translation. Artemis is especially useful in analysing the compact genomes of bacteria, archaea and lower eukaryotes, and will cope with sequences of any size from small genes to whole genomes. It is implemented in Java, and can be run on any suitable platform. Sequences and annotation can be read and written directly in EMBL, GenBank and GFF format. Availability: Artemis is available under the GNU General Public License from http:// www.sanger.ac.uk/ Software/ Artemis

/pdf/artemis-sequence-visualization-and-annotation-y0ssrzxu4v.pdf

Artemis: sequence visualization and annotation.

Streptomyces coelicolor is a representative of the group of soil-dwelling, filamentous bacteria responsible for producing most natural antibiotics used in human and veterinary medicine. Here we report the 8,667,507 base pair linear chromosome of this organism, containing the largest number of genes so far discovered in a bacterium. The 7,825 predicted genes include more than 20 clusters coding for known or predicted secondary metabolites. The genome contains an unprecedented proportion of regulatory genes, predominantly those likely to be involved in responses to external stimuli and stresses, and many duplicated gene sets that may represent 'tissue-specific' isoforms operating in different phases of colonial development, a unique situation for a bacterium. An ancient synteny was revealed between the central 'core' of the chromosome and the whole chromosome of pathogens Mycobacterium tuberculosis and Corynebacterium diphtheriae. The genome sequence will greatly increase our understanding of microbial life in the soil as well as aiding the generation of new drug candidates by genetic engineering.

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Complete genome sequence of the model actinomycete Streptomyces coelicolor A3(2)

Soils collected across a long-term liming experiment (pH 4.0-8.3), in which variation in factors other than pH have been minimized, were used to investigate the direct influence of pH on the abundance and composition of the two major soil microbial taxa, fungi and bacteria. We hypothesized that bacterial communities would be more strongly influenced by pH than fungal communities. To determine the relative abundance of bacteria and fungi, we used quantitative PCR (qPCR), and to analyze the composition and diversity of the bacterial and fungal communities, we used a bar-coded pyrosequencing technique. Both the relative abundance and diversity of bacteria were positively related to pH, the latter nearly doubling between pH 4 and 8. In contrast, the relative abundance of fungi was unaffected by pH and fungal diversity was only weakly related with pH. The composition of the bacterial communities was closely defined by soil pH; there was as much variability in bacterial community composition across the 180-m distance of this liming experiment as across soils collected from a wide range of biomes in North and South America, emphasizing the dominance of pH in structuring bacterial communities. The apparent direct influence of pH on bacterial community composition is probably due to the narrow pH ranges for optimal growth of bacteria. Fungal community composition was less strongly affected by pH, which is consistent with pure culture studies, demonstrating that fungi generally exhibit wider pH ranges for optimal growth.

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Soil bacterial and fungal communities across a pH gradient in an arable soil.

The seqinR package for the R environment is a library of utilities to retrieve and analyse biological sequences. It provides an interface between i) the R language and environment for statistical computing and graphics and ii) the ACNUC sequence retrieval system for nucleotide and protein sequence databases such as GenBank, EMBL, SWISS-PROT. ACNUC is very efficient in providing direct access to subsequences of biological interest (e.g. protein coding regions, tRNA or rRNA coding regions) present in GenBank and in EMBL. Thanks to a simple query language, it is then easy under R to select sequences of interest and then use all the power of the R environment to analyze them. The ACNUC databases can be locally installed but they are more conveniently accessed through a web server to take advantage of centralized daily updates. The aim of this paper is to provide a handout on basic sequence analyses under seqinR with a special focus on multivariate methods.

SeqinR 1.0-2: A Contributed Package to the R Project for Statistical Computing Devoted to Biological Sequences Retrieval and Analysis

Analyses of the genomes of three prokaryotes, Escherichia coli, Bacillus subtilis, and Haemophilus influenzae, revealed a new type of genomic compartmentalization of base frequencies. There was a departure from intrastrand equifrequency between A and T or between C and G, showing that the substitution patterns of the two strands of DNA were asymmetric. The positions of the boundaries between these compartments were found to coincide with the origin and terminus of chromosome replication, and there were more A-T and C-G deviations in intergenic regions and third codon positions, suggesting that a mutational bias was responsible for this asymmetry. The strand asymmetry was found to be due to a difference in base compositions of transcripts in the leading and lagging strands. This difference is sufficient to affect codon usage, but it is small compared to the effects of gene expressivity and amino-acid composition.

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Asymmetric substitution patterns in the two DNA strands of bacteria.

A new model in which the maximum microbial specific growth rate ((mu)(infmax)) is described as a function of pH and temperature is presented. The seven parameters of this model are the three cardinal pH parameters (the pH below which no growth occurs, the pH above which no growth occurs, and the pH at which the (mu)(infmax) is optimal), the three cardinal temperature parameters (the temperature below which no growth occurs, the temperature above which no growth occurs, and the temperature at which the (mu)(infmax) is optimal), and the specific growth rate at the optimum temperature and optimum pH. The model is a combination of the cardinal temperature model with inflection and the cardinal pH model (CPM). The CPM was compared with the models of Wijtzes et al. and Zwietering et al. by using previously published data sets. The models were compared on the basis of the usual criteria (simplicity, biological significance and minimum number of parameters, applicability, quality of fit, minimum structural correlations, and ease of initial parameter estimation), and our results justified the choice of the CPM. Our combined model was constructed by using the hypothesis that the temperature and pH effects on the (mu)(infmax) are independent. An analysis of this new model with an Escherichia coli O157:H7 data set showed that there was a good correspondence between observed and calculated (mu)(infmax) values. The potential and convenience of the model are discussed.

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Convenient Model To Describe the Combined Effects of Temperature and pH on Microbial Growth.

An Unexpected Correlation between Cardinal Temperatures of Microbial Growth Highlighted by a New Model

G:C pairs are more stable than A:T pairs because they have an additional hydrogen bond. This has led to many studies on the correlation between the guanine+cytosine (G+C) content of nucleic acids and temperature over the last 20 years. We collected the optimal growth temperatures (Topt) and the G+C contents of genomic DNA; 23S, 16S, and 5S ribosomal RNAs; and transfer RNAs for 764 prokaryotic species. No correlation was found between genomic G+C content and Topt, but there were striking correlations between the G+C content of ribosomal and transfer RNA stems and Topt. Two explanations have been proposed—neutral evolution and selection pressure—for the approximate equalities of G and C (respectively, A and T) contents within each strand of DNA molecules. Our results do not support the notion that selection pressure induces complementary oligonucleotides in close proximity and therefore numerous secondary structures in prokaryotic DNA, as the genomic G+C content does not behave in the same way as that of folded RNA with respect to optimal growth temperature.

/pdf/relationships-between-genomic-g-c-content-rna-secondary-qjkkafjga9.pdf

Jean R. Lobry

Papers

SeqinR 1.0-2: A Contributed Package to the R Project for Statistical Computing Devoted to Biological Sequences Retrieval and Analysis

Asymmetric substitution patterns in the two DNA strands of bacteria.

Convenient Model To Describe the Combined Effects of Temperature and pH on Microbial Growth.

An Unexpected Correlation between Cardinal Temperatures of Microbial Growth Highlighted by a New Model

Relationships Between Genomic G+C Content, RNA Secondary Structures, and Optimal Growth Temperature in Prokaryotes