Genomic base composition ranges from less than 25% AT to more than 85% AT in prokaryotes. Since only a small fraction of prokaryotic genomes is not protein coding even a minor change in genomic base composition will induce profound protein changes. We examined how amino acid and codon frequencies were distributed in over 2000 microbial genomes and how these distributions were affected by base compositional changes. In addition, we wanted to know how genome-wide amino acid usage was biased in the different genomes and how changes to base composition and mutations affected this bias. To carry this out, we used a Generalized Additive Mixed-effects Model (GAMM) to explore non-linear associations and strong data dependences in closely related microbes; principal component analysis (PCA) was used to examine genomic amino acid- and codon frequencies, while the concept of relative entropy was used to analyze genomic mutation rates.
We found that genomic amino acid frequencies carried a stronger phylogenetic signal than codon frequencies, but that this signal was weak compared to that of genomic %AT. Further, in contrast to codon usage bias (CUB), amino acid usage bias (AAUB) was differently distributed in AT- and GC-rich genomes in the sense that AT-rich genomes did not prefer specific amino acids over others to the same extent as GC-rich genomes. AAUB was also associated with relative entropy; genomes with low AAUB contained more random mutations as a consequence of relaxed purifying selection than genomes with higher AAUB.
Genomic base composition has a substantial effect on both amino acid- and codon frequencies in bacterial genomes. While phylogeny influenced amino acid usage more in GC-rich genomes, AT-content was driving amino acid usage in AT-rich genomes. We found the GAMM model to be an excellent tool to analyze the genomic data used in this study.
Today, there are more than a hundred times as many sequenced prokaryotic genomes than were present in the year 2000. The economical sequencing of genomic DNA has facilitated a whole new approach to microbial genomics. The real power of genomics is manifested through comparative genomics that can reveal strain specific characteristics, diversity within species and many other aspects. However, comparative genomics is a field not easily entered into by scientists with few computational skills. The CMG-biotools package is designed for microbiologists with limited knowledge of computational analysis and can be used to perform a number of analyses and comparisons of genomic data.
The CMG-biotools system presents a stand-alone interface for comparative microbial genomics. The package is a customized operating system, based on Xubuntu 10.10, available through the open source Ubuntu project. The system can be installed on a virtual computer, allowing the user to run the system alongside any other operating system. Source codes for all programs are provided under GNU license, which makes it possible to transfer the programs to other systems if so desired. We here demonstrate the package by comparing and analyzing the diversity within the class Negativicutes, represented by 31 genomes including 10 genera. The analyses include 16S rRNA phylogeny, basic DNA and codon statistics, proteome comparisons using BLAST and graphical analyses of DNA structures.
This paper shows the strength and diverse use of the CMG-biotools system. The system can be installed on a vide range of host operating systems and utilizes as much of the host computer as desired. It allows the user to compare multiple genomes, from various sources using standardized data formats and intuitive visualizations of results. The examples presented here clearly shows that users with limited computational experience can perform complicated analysis without much training.
The preferred habitat of a given bacterium can provide a hint of which types of enzymes of potential industrial interest it might produce. These might include enzymes that are stable and active at very high or very low temperatures. Being able to accurately predict this based on a genomic sequence, would thus allow for an efficient and targeted search for production organisms, reducing the need for culturing experiments.
This study found a total of 40 protein families useful for distinction between three thermophilicity classes (thermophiles, mesophiles and psychrophiles). The predictive performance of these protein families were compared to those of 87 basic sequence features (relative use of amino acids and codons, genomic and 16S rDNA AT content and genome size). When using naïve Bayesian inference, it was possible to correctly predict the optimal temperature range with a Matthews correlation coefficient of up to 0.68. The best predictive performance was always achieved by including protein families as well as structural features, compared to either of these alone. A dedicated computer program was created to perform these predictions.
This study shows that protein families associated with specific thermophilicity classes can provide effective input data for thermophilicity prediction, and that the naïve Bayesian approach is effective for such a task. The program created for this study is able to efficiently distinguish between thermophilic, mesophilic and psychrophilic adapted bacterial genomes.
We have analyzed a natural population of the marine bacterium, Alteromonas macleodii, from a single sample of seawater to evaluate the genomic diversity present. We performed full genome sequencing of four isolates and 161 metagenomic fosmid clones, all of which were assigned to A. macleodii by sequence similarity. Out of the four strain genomes, A. macleodii deep ecotype (AltDE1) represented a different genome, whereas AltDE2 and AltDE3 were identical to the previously described AltDE. Although the core genome (∼80%) had an average nucleotide identity of 98.51%, both AltDE and AltDE1 contained flexible genomic islands (fGIs), that is, genomic islands present in both genomes in the same genomic context but having different gene content. Some of the fGIs encode cell surface receptors known to be phage recognition targets, such as the O-chain of the lipopolysaccharide, whereas others have genes involved in physiological traits (e.g., nutrient transport, degradation, and metal resistance) denoting microniche specialization. The presence in metagenomic fosmids of genomic fragments differing from the sequenced strain genomes, together with the presence of new fGIs, indicates that there are at least two more A. macleodii clones present. The availability of three or more sequences overlapping the same genomic region also allowed us to estimate the frequency and distribution of recombination events among these different clones, indicating that these clustered near the genomic islands. The results indicate that this natural A. macleodii population has multiple clones with a potential for different phage susceptibility and exploitation of resources, within a seemingly unstructured habitat.
Alteromonas macleodii; metagenome; population genomics; genomic island; constant-diversity; phage
Escherichia coli exists in commensal and pathogenic forms. By measuring the variation of individual genes across more than a hundred sequenced genomes, gene variation can be studied in detail, including the number of mutations found for any given gene. This knowledge will be useful for creating better phylogenies, for determination of molecular clocks and for improved typing techniques.
We find 3,051 gene clusters/families present in at least 95% of the genomes and 1,702 gene clusters present in 100% of the genomes. The former 'soft core' of about 3,000 gene families is perhaps more biologically relevant, especially considering that many of these genome sequences are draft quality. The E. coli pan-genome for this set of isolates contains 16,373 gene clusters.
A core-gene tree, based on alignment and a pan-genome tree based on gene presence/absence, maps the relatedness of the 186 sequenced E. coli genomes. The core-gene tree displays high confidence and divides the E. coli strains into the observed MLST type clades and also separates defined phylotypes.
The results of comparing a large and diverse E. coli dataset support the theory that reliable and good resolution phylogenies can be inferred from the core-genome. The results further suggest that the resolution at the isolate level may, subsequently be improved by targeting more variable genes. The use of whole genome sequencing will make it possible to eliminate, or at least reduce, the need for several typing steps used in traditional epidemiology.
Escherichia coli; Core-genome; Pan-genome; Phylogeny; Whole genome sequencing; Genetic variation; Comparative genomics; MLST typing; Phylotyping
The thermophilic Campylobacter jejuni and Campylobacter coli are considered weakly clonal populations where incongruences between genetic markers are assumed to be due to random horizontal transfer of genomic DNA. In order to investigate the population genetics structure we extracted a set of 1180 core gene families (CGF) from 27 sequenced genomes of C. jejuni and C. coli. We adopted a principal component analysis (PCA) on the normalized evolutionary distances in order to reveal any patterns in the evolutionary signals contained within the various CGFs.
The analysis indicates that the conserved genes in Campylobacter show at least two, possibly five, distinct patterns of evolutionary signals, seen as clusters in the score-space of our PCA. The dominant underlying factor separating the core genes is the ability to distinguish C. jejuni from C. coli. The genes in the clusters outside the main gene group have a strong tendency of being chromosomal neighbors, which is natural if they share a common evolutionary history. Also, the most distinct cluster outside the main group is enriched with genes under positive selection and displays larger than average recombination rates.
The Campylobacter genomes investigated here show that subsets of conserved genes differ from each other in a more systematic way than expected by random horizontal transfer, and is consistent with differences in selection pressure acting on different genes. These findings are indications of a population of bacteria characterized by genomes with a mixture of evolutionary patterns.
The genus Mycobacterium comprises different species, among them the most contagious and infectious bacteria. The members of the complex Mycobacterium tuberculosis are the most virulent microorganisms that have killed human and other mammals since millennia. Additionally, with the many different mycobacterial sequences available, there is a crucial need for the visualization and the simplification of their data. In this present study, we aim to highlight a comparative genome, proteome and phylogeny analysis between twenty-one mycobacterial (Tuberculosis and non tuberculosis) strains using a set of computational and bioinformatics tools (Pan and Core genome plotting, BLAST matrix and phylogeny analysis).
Considerably the result of pan and core genome Plotting demonstrated that less than 1250 Mycobacterium gene families are conserved across all species, and a total set of about 20,000 gene families within the Mycobacterium pan-genome of twenty one mycobacterial genomes.
Viewing the BLAST matrix a high similarity was found among the species of the complex Mycobacterium tuberculosis and less conservation is found with other slow growing pathogenic mycobacteria.
Phylogeny analysis based on both protein conservation, as well as rRNA clearly resolve known relationships between slow growing mycobacteria.
Mycobacteria include important pathogenic species for human and animals and the Mycobacterium tuberculosis complex is the most cause of death of the humankind. The comparative genome analysis could provide a new insight for better controlling and preventing these diseases.
BLAST matrix; Comparative genome analysis; Evolution; Mycobacterium tuberculosis; Pan- core genome; Phylogeny
Accurate strain identification is essential for anyone working with bacteria. For many species, multilocus sequence typing (MLST) is considered the “gold standard” of typing, but it is traditionally performed in an expensive and time-consuming manner. As the costs of whole-genome sequencing (WGS) continue to decline, it becomes increasingly available to scientists and routine diagnostic laboratories. Currently, the cost is below that of traditional MLST. The new challenges will be how to extract the relevant information from the large amount of data so as to allow for comparison over time and between laboratories. Ideally, this information should also allow for comparison to historical data. We developed a Web-based method for MLST of 66 bacterial species based on WGS data. As input, the method uses short sequence reads from four sequencing platforms or preassembled genomes. Updates from the MLST databases are downloaded monthly, and the best-matching MLST alleles of the specified MLST scheme are found using a BLAST-based ranking method. The sequence type is then determined by the combination of alleles identified. The method was tested on preassembled genomes from 336 isolates covering 56 MLST schemes, on short sequence reads from 387 isolates covering 10 schemes, and on a small test set of short sequence reads from 29 isolates for which the sequence type had been determined by traditional methods. The method presented here enables investigators to determine the sequence types of their isolates on the basis of WGS data. This method is publicly available at www.cbs.dtu.dk/services/MLST.
Technological advances in high throughput genome sequencing are making whole genome sequencing (WGS) available as a routine tool for bacterial typing. Standardized procedures for identification of relevant genes and of variation are needed to enable comparison between studies and over time. The core genes--the genes that are conserved in all (or most) members of a genus or species--are potentially good candidates for investigating genomic variation in phylogeny and epidemiology.
We identify a set of 2,882 core genes clusters based on 73 publicly available Salmonella enterica genomes and evaluate their value as typing targets, comparing whole genome typing and traditional methods such as 16S and MLST. A consensus tree based on variation of core genes gives much better resolution than 16S and MLST; the pan-genome family tree is similar to the consensus tree, but with higher confidence. The core genes can be divided into two categories: a few highly variable genes and a larger set of conserved core genes, with low variance. For the most variable core genes, the variance in amino acid sequences is higher than for the corresponding nucleotide sequences, suggesting that there is a positive selection towards mutations leading to amino acid changes.
Genomic variation within the core genome is useful for investigating molecular evolution and providing candidate genes for bacterial genome typing. Identification of genes with different degrees of variation is important especially in trend analysis.
We sought to assess whether the concept of relative entropy (information capacity), could aid our understanding of the process of horizontal gene transfer in microbes. We analyzed the differences in information capacity between prokaryotic chromosomes, genomic islands (GI), phages, and plasmids. Relative entropy was estimated using the Kullback-Leibler measure.
Relative entropy was highest in bacterial chromosomes and had the sequence chromosomes > GI > phage > plasmid. There was an association between relative entropy and AT content in chromosomes, phages, plasmids and GIs with the strongest association being in phages. Relative entropy was also found to be lower in the obligate intracellular Mycobacterium leprae than in the related M. tuberculosis when measured on a shared set of highly conserved genes.
We argue that relative entropy differences reflect how plasmids, phages and GIs interact with microbial host chromosomes and that all these biological entities are, or have been, subjected to different selective pressures. The rate at which amelioration of horizontally acquired DNA occurs within the chromosome is likely to account for the small differences between chromosomes and stably incorporated GIs compared to the transient or independent replicons such as phages and plasmids.
The genome of Enterococcus faecalis 62, a commensal isolate from a healthy Norwegian infant, revealed multiple adaptive traits to the gastrointestinal tract (GIT) environment and the milk-containing diet of breast-fed infants. Adaptation to a commensal existence was emphasized by lactose and other carbohydrate metabolism genes within genomic islands, accompanied by the absence of virulence traits.
There are many things that I like about James Shapiro's new book "Evolution: A View from the 21st Century" (FT Press Science, 2011). He begins the book by saying that it is the creation of novelty, and not selection, that is important in the history of life. In the presence of heritable traits that vary, selection results in the evolution of a population towards an optimal composition of those traits. But selection can only act on changes - and where does this variation come from? Historically, the creation of novelty has been assumed to be the result of random chance or accident. And yet, organisms seem 'designed'. When one examines the data from sequenced genomes, the changes appear NOT to be random or accidental, but one observes that whole chunks of the genome come and go. These 'chunks' often contain functional units, encoding sets of genes that together can perform some specific function. Shapiro argues that what we see in genomes is 'Natural Genetic Engineering', or designed evolution: "Thinking about genomes from an informatics perspective, it is apparent that systems engineering is a better metaphor for the evolutionary process than the conventional view of evolution as a select-biased random walk through limitless space of possible DNA configurations" (page 6).
In this review, I will have a look at four topics: 1.) why I think genomics is not the whole story; 2.) my own perspective of E. coli genomics, and how I think it relates to this book; 3.) a brief discussion on "Intelligence, Design, and Evolution"; and finally, 4.) a section "in defense of the central dogma".
Six bacterial genera containing species commonly used as probiotics for human consumption or starter cultures for food fermentation were compared and contrasted, based on publicly available complete genome sequences. The analysis included 19 Bifidobacterium genomes, 21 Lactobacillus genomes, 4 Lactococcus and 3 Leuconostoc genomes, as well as a selection of Enterococcus (11) and Streptococcus (23) genomes. The latter two genera included genomes from probiotic or commensal as well as pathogenic organisms to investigate if their non-pathogenic members shared more genes with the other probiotic genomes than their pathogenic members. The pan- and core genome of each genus was defined. Pairwise BLASTP genome comparison was performed within and between genera. It turned out that pathogenic Streptococcus and Enterococcus shared more gene families than did the non-pathogenic genomes. In silico multilocus sequence typing was carried out for all genomes per genus, and the variable gene content of genomes was compared within the genera. Informative BLAST Atlases were constructed to visualize genomic variation within genera. The clusters of orthologous groups (COG) classes of all genes in the pan- and core genome of each genus were compared. In addition, it was investigated whether pathogenic genomes contain different COG classes compared to the probiotic or fermentative organisms, again comparing their pan- and core genomes. The obtained results were compared with published data from the literature. This study illustrates how over 80 genomes can be broadly compared using simple bioinformatic tools, leading to both confirmation of known information as well as novel observations.
Electronic supplementary material
The online version of this article (doi:10.1007/s00248-011-9948-y) contains supplementary material, which is available to authorized users.
The genus Pseudomonas has gone through many taxonomic revisions over the past 100 years, going from a very large and diverse group of bacteria to a smaller, more refined and ordered list having specific properties. The relationship of the Pseudomonas genus to Azotobacter vinelandii is examined using three genomic sequence-based methods. First, using 16S rRNA trees, it is shown that A. vinelandii groups within the Pseudomonas close to Pseudomonas aeruginosa. Genomes from other related organisms (Acinetobacter, Psychrobacter, and Cellvibrio) are outside the Pseudomonas cluster. Second, pan genome family trees based on conserved gene families also show A. vinelandii to be more closely related to Pseudomonas than other related organisms. Third, exhaustive BLAST comparisons demonstrate that the fraction of shared genes between A. vinelandii and Pseudomonas genomes is similar to that of Pseudomonas species with each other. The results of these different methods point to a high similarity between A. vinelandii and the Pseudomonas genus, suggesting that Azotobacter might actually be a Pseudomonas.
Electronic supplementary material
The online version of this article (doi:10.1007/s00248-011-9914-8) contains supplementary material, which is available to authorized users.
A vast and rich body of information has grown up as a result of the world's enthusiasm for 'omics technologies. Finding ways to describe and make available this information that maximise its usefulness has become a major effort across the 'omics world. At the heart of this effort is the Genomic Standards Consortium (GSC), an open-membership organization that drives community-based standardization activities, Here we provide a short history of the GSC, provide an overview of its range of current activities, and make a call for the scientific community to join forces to improve the quality and quantity of contextual information about our public collections of genomes, metagenomes, and marker gene sequences.
Salmonella enterica is divided into four subspecies containing a large number of different serovars, several of which are important zoonotic pathogens and some show a high degree of host specificity or host preference. We compare 45 sequenced S. enterica genomes that are publicly available (22 complete and 23 draft genome sequences). Of these, 35 were found to be of sufficiently good quality to allow a detailed analysis, along with two Escherichia coli strains (K-12 substr. DH10B and the avian pathogenic E. coli (APEC O1) strain). All genomes were subjected to standardized gene finding, and the core and pan-genome of Salmonella were estimated to be around 2,800 and 10,000 gene families, respectively. The constructed pan-genomic dendrograms suggest that gene content is often, but not uniformly correlated to serotype. Any given Salmonella strain has a large stable core, whilst there is an abundance of accessory genes, including the Salmonella pathogenicity islands (SPIs), transposable elements, phages, and plasmid DNA. We visualize conservation in the genomes in relation to chromosomal location and DNA structural features and find that variation in gene content is localized in a selection of variable genomic regions or islands. These include the SPIs but also encompass phage insertion sites and transposable elements. The islands were typically well conserved in several, but not all, isolates—a difference which may have implications in, e.g., host specificity.
Electronic supplementary material
The online version of this article (doi:10.1007/s00248-011-9880-1) contains supplementary material, which is available to authorized users.
Campylobacter jejuni strain M1 (laboratory designation 99/308) is a rarely documented case of direct transmission of C. jejuni from chicken to a person, resulting in enteritis. We have sequenced the genome of C. jejuni strain M1, and compared this to 12 other C. jejuni sequenced genomes currently publicly available. Compared to these, M1 is closest to strain 81116. Based on the 13 genome sequences, we have identified the C. jejuni pan-genome, as well as the core genome, the auxiliary genes, and genes unique between strains M1 and 81116. The pan-genome contains 2,427 gene families, whilst the core genome comprised 1,295 gene families, or about two-thirds of the gene content of the average of the sequenced C. jejuni genomes. Various comparison and visualization tools were applied to the 13 C. jejuni genome sequences, including a species pan- and core genome plot, a BLAST Matrix and a BLAST Atlas. Trees based on 16S rRNA sequences and on the total gene families in each genome are presented. The findings are discussed in the background of the proven virulence potential of M1.
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).
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.
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.
Bacterial genomes possess varying GC content (total guanines (Gs) and cytosines (Cs) per total of the four bases within the genome) but within a given genome, GC content can vary locally along the chromosome, with some regions significantly more or less GC rich than on average. We have examined how the GC content varies within microbial genomes to assess whether this property can be associated with certain biological functions related to the organism's environment and phylogeny. We utilize a new quantity GCVAR, the intra-genomic GC content variability with respect to the average GC content of the total genome. A low GCVAR indicates intra-genomic GC homogeneity and high GCVAR heterogeneity.
The regression analyses indicated that GCVAR was significantly associated with domain (i.e. archaea or bacteria), phylum, and oxygen requirement. GCVAR was significantly higher among anaerobes than both aerobic and facultative microbes. Although an association has previously been found between mean genomic GC content and oxygen requirement, our analysis suggests that no such association exits when phylogenetic bias is accounted for. A significant association between GCVAR and mean GC content was also found but appears to be non-linear and varies greatly among phyla.
Our findings show that GCVAR is linked with oxygen requirement, while mean genomic GC content is not. We therefore suggest that GCVAR should be used as a complement to mean GC content.
Escherichia coli is an important component of the biosphere and is an ideal model for studies of processes involved in bacterial genome evolution. Sixty-one publically available E. coli and Shigella spp. sequenced genomes are compared, using basic methods to produce phylogenetic and proteomics trees, and to identify the pan- and core genomes of this set of sequenced strains. A hierarchical clustering of variable genes allowed clear separation of the strains into clusters, including known pathotypes; clinically relevant serotypes can also be resolved in this way. In contrast, when in silico MLST was performed, many of the various strains appear jumbled and less well resolved. The predicted pan-genome comprises 15,741 gene families, and only 993 (6%) of the families are represented in every genome, comprising the core genome. The variable or ‘accessory’ genes thus make up more than 90% of the pan-genome and about 80% of a typical genome; some of these variable genes tend to be co-localized on genomic islands. The diversity within the species E. coli, and the overlap in gene content between this and related species, suggests a continuum rather than sharp species borders in this group of Enterobacteriaceae.
We present the pan-genome tree as a tool for visualizing similarities and differences between closely related microbial genomes within a species or genus. Distance between genomes is computed as a weighted relative Manhattan distance based on gene family presence/absence. The weights can be chosen with emphasis on groups of gene families conserved to various degrees inside the pan-genome. The software is available for free as an R-package.
Vibrio taxonomy has been based on a polyphasic approach. In this study, we retrieve useful taxonomic information (i.e. data that can be used to distinguish different taxonomic levels, such as species and genera) from 32 genome sequences of different vibrio species. We use a variety of tools to explore the taxonomic relationship between the sequenced genomes, including Multilocus Sequence Analysis (MLSA), supertrees, Average Amino Acid Identity (AAI), genomic signatures, and Genome BLAST atlases. Our aim is to analyse the usefulness of these tools for species identification in vibrios.
We have generated four new genome sequences of three Vibrio species, i.e., V. alginolyticus 40B, V. harveyi-like 1DA3, and V. mimicus strains VM573 and VM603, and present a broad analyses of these genomes along with other sequenced Vibrio species. The genome atlas and pangenome plots provide a tantalizing image of the genomic differences that occur between closely related sister species, e.g. V. cholerae and V. mimicus. The vibrio pangenome contains around 26504 genes. The V. cholerae core genome and pangenome consist of 1520 and 6923 genes, respectively. Pangenomes might allow different strains of V. cholerae to occupy different niches. MLSA and supertree analyses resulted in a similar phylogenetic picture, with a clear distinction of four groups (Vibrio core group, V. cholerae-V. mimicus, Aliivibrio spp., and Photobacterium spp.). A Vibrio species is defined as a group of strains that share > 95% DNA identity in MLSA and supertree analysis, > 96% AAI, ≤ 10 genome signature dissimilarity, and > 61% proteome identity. Strains of the same species and species of the same genus will form monophyletic groups on the basis of MLSA and supertree.
The combination of different analytical and bioinformatics tools will enable the most accurate species identification through genomic computational analysis. This endeavour will culminate in the birth of the online genomic taxonomy whereby researchers and end-users of taxonomy will be able to identify their isolates through a web-based server. This novel approach to microbial systematics will result in a tremendous advance concerning biodiversity discovery, description, and understanding.
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.
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.
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.
Thirty-two genome sequences of various Vibrionaceae members are compared, with emphasis on what makes V. cholerae unique. As few as 1,000 gene families are conserved across all the Vibrionaceae genomes analysed; this fraction roughly doubles for gene families conserved within the species V. cholerae. Of these, approximately 200 gene families that cluster on various locations of the genome are not found in other sequenced Vibrionaceae; these are possibly unique to the V. cholerae species. By comparing gene family content of the analysed genomes, the relatedness to a particular species is identified for two unspeciated genomes. Conversely, two genomes presumably belonging to the same species have suspiciously dissimilar gene family content. We are able to identify a number of genes that are conserved in, and unique to, V. cholerae. Some of these genes may be crucial to the niche adaptation of this species.
We present an interactive web application for visualizing genomic data of prokaryotic chromosomes. The tool (GeneWiz browser) allows users to carry out various analyses such as mapping alignments of homologous genes to other genomes, mapping of short sequencing reads to a reference chromosome, and calculating DNA properties such as curvature or stacking energy along the chromosome. The GeneWiz browser produces an interactive graphic that enables zooming from a global scale down to single nucleotides, without changing the size of the plot. Its ability to disproportionally zoom provides optimal readability and increased functionality compared to other browsers. The tool allows the user to select the display of various genomic features, color setting and data ranges. Custom numerical data can be added to the plot allowing, for example, visualization of gene expression and regulation data. Further, standard atlases are pre-generated for all prokaryotic genomes available in GenBank, providing a fast overview of all available genomes, including recently deposited genome sequences. The tool is available online from http://www.cbs.dtu.dk/services/gwBrowser. Supplemental material including interactive atlases is available online at http://www.cbs.dtu.dk/services/gwBrowser/suppl/.