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Background: Recent findings indicated that a correlation between genomic % AT and genome size within strains of microbial species was predominantly associated with the uptake of foreign DNA. One species however, Chlamydia trachomatis, defied any explanation. In the present study 79 fully sequenced C. trachomatis genomes, representing ocular- (nine strains), urogenital- (36 strains) and lymphogranuloma venereum strains (LGV, 22 strains), in three pathogroups, in addition to 12 laboratory isolates, were scrutinized with the intent of elucidating the positive correlation between genomic AT content and genome size., Results: The average size difference between the strains of each pathogroup was largely explained by the incorporation of genetic fragments. These fragments were slightly more AT rich than their corresponding host genomes, but not enough to justify the difference in AT content between the strains of the smaller genomes lacking the fragments. In addition, a genetic region predominantly found in the ocular strains, which had the largest genomes, was on average more GC rich than the host genomes of the urogenital strains (58.64% AT vs. 58.69% AT), which had the second largest genomes, implying that the foreign genetic regions cannot alone explain the association between genome size and AT content in C. trachomatis. 23,492 SNPs were identified for all 79 genomes, and although the SNPs were on average slightly GC rich (~47% AT), a significant association was found between genome-wide SNP AT content, for each pathogroup, and genome size (p < 0.001, R (2) = 0.86) in the C. trachomatis strains., Conclusions: The correlation between genome size and AT content, with respect to the C. trachomatis pathogroups, was explained by the incorporation of genetic fragments unique to the ocular and/or urogenital strains into the LGV- and urogential strains in addition to the genome-wide SNP AT content differences between the three pathogroups.

Motivation: The explosion of whole-genome sequencing (WGS) as a tool in the mapping and understanding of genomes has been accompanied by an equally massive report of tools and pipelines for the analysis of DNA copy number variation (CNV). Most currently available tools are designed specifically for human genomes, with comparatively little literature devoted to CNVs in prokaryotic organisms. However, there are several idiosyncrasies in prokaryotic WGS data. This work proposes a step-by-step approach for detection and quantification of copy number variants specifically aimed at prokaryotes., Results: After aligning WGS reads to a reference genome, we count the individual reads in a sliding window and normalize these counts for bias introduced by differences in GC content. We then investigate the coverage in two fundamentally different ways: (i) Employing a Hidden Markov Model and (ii) by repeated sampling with replacement (bootstrapping) on each individual gene. The latter bypasses the complex problem of breakpoint determination. To demonstrate our method, we apply it to real and simulated WGS data and benchmark it against two popular methods for CNV detection. The proposed methodology will in some cases represent a significant jump in accuracy from other current methods., Availability and Implementation: CNOGpro is written entirely in the R programming language and is available from the CRAN repository (http://cran.r-project.org) under the GNU General Public License., (© The Author 2015. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.)

The upstream region of coding genes is important for several reasons, for instance locating transcription factor, binding sites, and start site initiation in genomic DNA. Motivated by a recently conducted study, where multivariate approach was successfully applied to coding sequence modeling, we have introduced a partial least squares (PLS) based procedure for the classification of true upstream prokaryotic sequence from background upstream sequence. The upstream sequences of conserved coding genes over genomes were considered in analysis, where conserved coding genes were found by using pan-genomics concept for each considered prokaryotic species. PLS uses position specific scoring matrix (PSSM) to study the characteristics of upstream region. Results obtained by PLS based method were compared with Gini importance of random forest (RF) and support vector machine (SVM), which is much used method for sequence classification. The upstream sequence classification performance was evaluated by using cross validation, and suggested approach identifies prokaryotic upstream region significantly better to RF (p-value < 0.01) and SVM (p-value < 0.01). Further, the proposed method also produced results that concurred with known biological characteristics of the upstream region.

Background: There are several studies describing loss of genes through reductive evolution in microbes, but how selective forces are associated with genome expansion due to horizontal gene transfer (HGT) has not received similar attention. The aim of this study was therefore to examine how selective pressures influence genome expansion in 53 fully sequenced and assembled Escherichia coli strains. We also explored potential connections between genome expansion and the attainment of virulence factors. This was performed using estimations of several genomic parameters such as AT content, genomic drift (measured using relative entropy), genome size and estimated HGT size, which were subsequently compared to analogous parameters computed from the core genome consisting of 1729 genes common to the 53 E. coli strains. Moreover, we analyzed how selective pressures (quantified using relative entropy and dN/dS), acting on the E. coli core genome, influenced lineage and phylogroup formation., Results: Hierarchical clustering of dS and dN estimations from the E. coli core genome resulted in phylogenetic trees with topologies in agreement with known E. coli taxonomy and phylogroups. High values of dS, compared to dN, indicate that the E. coli core genome has been subjected to substantial purifying selection over time; significantly more than the non-core part of the genome (p<0.001). This is further supported by a linear association between strain-wise dS and dN values (β = 26.94 ± 0.44, R2~0.98, p<0.001). The non-core part of the genome was also significantly more AT-rich (p<0.001) than the core genome and E. coli genome size correlated with estimated HGT size (p<0.001). In addition, genome size (p<0.001), AT content (p<0.001) as well as estimated HGT size (p<0.005) were all associated with the presence of virulence factors, suggesting that pathogenicity traits in E. coli are largely attained through HGT. No associations were found between selective pressures operating on the E. coli core genome, as estimated using relative entropy, and genome size (p~0.98)., Conclusions: On a larger time frame, genome expansion in E. coli, which is significantly associated with the acquisition of virulence factors, appears to be independent of selective forces operating on the core genome.

Genomic %AT has been found to correlate negatively with genome size in microbes. While microbes with large genomes are often GC rich and free living, AT-rich bacteria tend to be host associated with smaller genomes. With over 2000 fully sequenced and assembled microbial genomes available, we explored the relationship among genomic %AT, genome size, relative entropy (a measure associated with genetic drift) and fraction of genome islands (GIs) in microbial species with the genomes of more than 10 strains available. A negative correlation with genome size was found in six out of 12 phylogenetic groups and subphyla and a positive correlation in only two. At the species level, we found a trend of positive correlations between genomic %AT and genome size in eight out of 20 species, while only four showed a negative correlation. Estimated chromosomal fractions of GIs were found to correlate positively with genome size in the strains of 14 out of 18 species and genomic %AT in the strains of seven species (two correlated negatively). Although GIs explain most of the observed positive correlations between genomic %AT and size, Chlamydia trachomatis seem to be an exception; therefore, these findings needs to be further explored., (© 2014 Society for Applied Microbiology and John Wiley & Sons Ltd.)

The ratio of genomic oligonucleotide frequencies relative to the mean genomic AT/GC content has been shown to be similar for closely related species and, therefore, said to reflect a "genomic signature". The genomic signature has been found to be more similar within genomes than between closely related genomes. Furthermore, genomic signatures of closely related organisms are, in turn, more similar than more distantly related organisms. Since the genomic signature is remarkably stable within a genome, it can be extracted from only a fraction of the genomic DNA sequence. Genomic signatures, therefore, have many applications. The most notable examples include recognition of pathogenicity islands in microbial genomes and identification of hosts from arbitrary DNA sequences, the latter being of great importance in metagenomics. What shapes the genomic signature in microbial DNA has been readily discussed, but difficult to pinpoint exactly. Most attempts so far have mainly focused on correlations from in silico data. This mini-review seeks to summarize possible influences shaping the genomic signature and to survey a set of applications.

Background: Classification of bacteria within the genus Brucella has been difficult due in part to considerable genomic homogeneity between the different species and biovars, in spite of clear differences in phenotypes. Therefore, many different methods have been used to assess Brucella taxonomy. In the current work, we examine 32 sequenced genomes from genus Brucella representing the six classical species, as well as more recently described species, using bioinformatical methods. Comparisons were made at the level of genomic DNA using oligonucleotide based methods (Markov chain based genomic signatures, genomic codon and amino acid frequencies based comparisons) and proteomes (all-against-all BLAST protein comparisons and pan-genomic analyses)., Results: We found that the oligonucleotide based methods gave different results compared to that of the proteome based methods. Differences were also found between the oligonucleotide based methods used. Whilst the Markov chain based genomic signatures grouped the different species in genus Brucella according to host preference, the codon and amino acid frequencies based methods reflected small differences between the Brucella species. Only minor differences could be detected between all genera included in this study using the codon and amino acid frequencies based methods. Proteome comparisons were found to be in strong accordance with current Brucella taxonomy indicating a remarkable association between gene gain or loss on one hand and mutations in marker genes on the other. The proteome based methods found greater similarity between Brucella species and Ochrobactrum species than between species within genus Agrobacterium compared to each other. In other words, proteome comparisons of species within genus Agrobacterium were found to be more diverse than proteome comparisons between species in genus Brucella and genus Ochrobactrum. Pan-genomic analyses indicated that uptake of DNA from outside genus Brucella appears to be limited., Conclusions: While both the proteome based methods and the Markov chain based genomic signatures were able to reflect environmental diversity between the different species and strains of genus Brucella, the genomic codon and amino acid frequencies based comparisons were not found adequate for such comparisons. The proteome comparison based phylogenies of the species in genus Brucella showed a surprising consistency with current Brucella taxonomy.

Background: Recently there has been an explosion in the availability of bacterial genomic sequences, making possible now an analysis of genomic signatures across more than 800 hundred different bacterial chromosomes, from a wide variety of environments.Using genomic signatures, we pair-wise compared 867 different genomic DNA sequences, taken from chromosomes and plasmids more than 100,000 base-pairs in length. Hierarchical clustering was performed on the outcome of the comparisons before a multinomial regression model was fitted. The regression model included the cluster groups as the response variable with AT content, phyla, growth temperature, selective pressure, habitat, sequence size, oxygen requirement and pathogenicity as predictors., Results: Many significant factors were associated with the genomic signature, most notably AT content. Phyla was also an important factor, although considerably less so than AT content. Small improvements to the regression model, although significant, were also obtained by factors such as sequence size, habitat, growth temperature, selective pressure measured as oligonucleotide usage variance, and oxygen requirement., Conclusion: The statistics obtained using hierarchical clustering and multinomial regression analysis indicate that the genomic signature is shaped by many factors, and this may explain the varying ability to classify prokaryotic organisms below genus level.

The genus Burkholderia consists of a number of very diverse species, both in terms of lifestyle (which varies from category B pathogens to apathogenic soil bacteria and plant colonizers) and their genetic contents. We have used 56 publicly available genomes to explore the genomic diversity within this genus, including genome sequences that are not completely finished, but are available from the NCBI database. Defining the pan- and core genomes of species results in insights in the conserved and variable fraction of genomes, and can verify (or question) historic, taxonomic groupings. We find only several hundred genes that are conserved across all Burkholderia genomes, whilst there are more than 40,000 gene families in the Burkholderia pan-genome. A BLAST matrix visualizes the fraction of conserved genes in pairwise comparisons. A BLAST atlas shows which genes are actually conserved in a number of genomes, located and visualized with reference to a chosen genome. Genomic islands are common in many Burkholderia genomes, and most of these can be readily visualized by DNA structural properties of the chromosome. Trees that are based on relatedness of gene family content yield different results depending on what genes are analyzed. Some of the differences can be explained by errors in incomplete genome sequences, but, as our data illustrate, the outcome of phylogenetic trees depends on the type of genes that are analyzed., (Copyright © 2009 S. Karger AG, Basel.)

Background: The increasing number of sequenced prokaryotic genomes contains a wealth of genomic data that needs to be effectively analysed. A set of statistical tools exists for such analysis, but their strengths and weaknesses have not been fully explored. The statistical methods we are concerned with here are mainly used to examine similarities between archaeal and bacterial DNA from different genomes. These methods compare observed genomic frequencies of fixed-sized oligonucleotides with expected values, which can be determined by genomic nucleotide content, smaller oligonucleotide frequencies, or be based on specific statistical distributions. Advantages with these statistical methods include measurements of phylogenetic relationship with relatively small pieces of DNA sampled from almost anywhere within genomes, detection of foreign/conserved DNA, and homology searches. Our aim was to explore the reliability and best suited applications for some popular methods, which include relative oligonucleotide frequencies (ROF), di- to hexanucleotide zero'th order Markov methods (ZOM) and 2.order Markov chain Method (MCM). Tests were performed on distant homology searches with large DNA sequences, detection of foreign/conserved DNA, and plasmid-host similarity comparisons. Additionally, the reliability of the methods was tested by comparing both real and random genomic DNA., Results: Our findings show that the optimal method is context dependent. ROFs were best suited for distant homology searches, whilst the hexanucleotide ZOM and MCM measures were more reliable measures in terms of phylogeny. The dinucleotide ZOM method produced high correlation values when used to compare real genomes to an artificially constructed random genome with similar %GC, and should therefore be used with care. The tetranucleotide ZOM measure was a good measure to detect horizontally transferred regions, and when used to compare the phylogenetic relationships between plasmids and hosts, significant correlation (R2 = 0.4) was found with genomic GC content and intra-chromosomal homogeneity., Conclusion: The statistical methods examined are fast, easy to implement, and powerful for a number of different applications involving genomic sequence comparisons. However, none of the measures examined were superior in all tests, and therefore the choice of the statistical method should depend on the task at hand.