High resolution assembly and characterization of genomes of Canadian isolates of Salmonella Enteritidis
© Ogunremi et al.; licensee BioMed Central Ltd. 2014
Received: 20 December 2013
Accepted: 14 August 2014
Published: 25 August 2014
There is a need to characterize genomes of the foodborne pathogen, Salmonella enterica serovar Enteritidis (SE) and identify genetic information that could be ultimately deployed for differentiating strains of the organism, a need that is yet to be addressed mainly because of the high degree of clonality of the organism. In an effort to achieve the first characterization of the genomes of SE of Canadian origin, we carried out massively parallel sequencing of the nucleotide sequence of 11 SE isolates obtained from poultry production environments (n = 9), a clam and a chicken, assembled finished genomes and investigated diversity of the SE genome.
The median genome size was 4,678,683 bp. A total of 4,833 chromosomal genes defined the pan genome of our field SE isolates consisting of 4,600 genes present in all the genomes, i.e., core genome, and 233 genes absent in at least one genome (accessory genome). Genome diversity was demonstrable by the presence of 1,360 loci showing single nucleotide polymorphism (SNP) in the core genome which was used to portray the genetic distances by means of a phylogenetic tree for the SE isolates. The accessory genome consisted mostly of previously identified SE prophage sequences as well as two, apparently full- sized, novel prophages namely a 28 kb sequence provisionally designated as SE-OLF-10058 (3) prophage and a 43 kb sequence provisionally designated as SE-OLF-10012 prophage.
The number of SNPs identified in the relatively large core genome of SE is a reflection of substantial diversity that could be exploited for strain differentiation as shown by the development of an informative phylogenetic tree. Prophage sequences can also be exploited for SE strain differentiation and lineage tracking. This work has laid the ground work for further studies to develop a readily adoptable laboratory test for the subtyping of SE.
KeywordsSalmonella Enteritidis Genomes Core Accessory Single nucleotide polymorphism Subtyping Tracking
Salmonella Enteritidis (SE) has emerged as the most commonly isolated serovar of foodborne Salmonella in humans over the last two decades [1–3]. SE belongs to a larger group of pathogens known as non-typhoidal Salmonella which ranked the most hazardous when a number of health indices were used to assess the 14 most burdensome foodborne bacteria, viruses and parasites causing diseases in humans [4, 5]. In Canada, the proportion of human salmonellosis caused by SE increased from 13% in 2003 to 38% in 2010 . In the US, an outbreak of SE in 2010 resulted in an estimated 1,939 human illnesses (http://www.cdc.gov/salmonella/enteritidis/) and the largest egg recall in the country’s history involving over 500 million shell eggs (http://www.fda.gov/Safety/Recalls/MajorProductRecalls/ucm223522.htm).
Early comparative analysis of SE with the serovar Typhimurium, the latter being one of the best studied Salmonella serovars because of its enduring importance as a human pathogen and wide host range among vertebrates, has led to the identification of metabolic pathways and virulence genes for SE. The application of DNA methodologies especially the adoption of DNA-DNA hybridization technique as the reference method of establishing relationships among organisms [8, 9] provided insight on the genetic relatedness among many Salmonella serovars . Consequently, the majority of Salmonella serovars - 2,587 at the last count  - including serovar Enteritidis, were classified into a single species, namely: S. enterica. The remaining 23 known serovars, which are typically but not exclusively isolated from cold blooded animals, belong to the second species known as S. bongori. This re-designation of a majority of Salmonella organisms previously known by their species designation into a single species because of observed genetic similarities conflicted with historical and behavioural differences observed in their host ranges. Now commonly described by their serovar designations, organisms belonging to the S. enterica species fall into three broad groups based on the ability to either infect only a single type of vertebrate host (e.g., serovars Typhi and Pullorum of humans and poultry, respectively), or a limited number of hosts usually including humans (e.g., serovars Dublin and Choleraesuis) or an extensive host range (e.g., serovars Enteritidis and Typhimurium). Other notable differences were observed among serovars with similar host range patterns including cultural growth patterns and biochemical test results in the laboratory. The ensuing conundrum was that observed genetic similarities among serovars was at variance with considerable behavioural differences and this has now led to an urgent need to develop a robust subspecies and sub-serovar level classification . To compound the situation, it is clear that isolates of the serovar Enteritidis show an even more remarkable and striking genetic similarity with one another to the extent that existing phenotypic and genotypic bacterial typing tools have proven inadequate to assess their degree of relatednesss [14–16]. Despite these reported similarities a number of other studies have identified differences among isolates of Enteritidis in animal infection trials, cell invasion assays, growth rates and ability to contaminate and survive within eggs [17–19]. The above underscores the need for a much deeper insight into the biology of the SE which has become accentuated by its new prominence as a foodborne pathogen of humans. The increasing success of a pathogen that displays as much clonality as SE is intriguing and not easily explained from a biological perspective. An effective immune response of a vertebrate host against clonal bacteria would almost certainly protect the host against further exposure to the same or similar strains. It is therefore to the advantage of the pathogen to have a capacity and means of evading the host’s immune system by changing its antigenic properties. Thus, clonality of a pathogen may ordinarily be seen as a disadvantage from an evolutionary viewpoint. Yet, this has not impeded the success of SE as a thriving pathogen. From the perspective of a food microbiology laboratory, the clonality of SE has made it extremely difficult to demonstrate strain relatedness in an accurate and reproducible manner using available analytical methods. The need to track an organism and to cluster related strains are key elements in the effort to control human outbreaks of SE by identifying the sources of infections and prevent further exposures, e.g., by food recall procedures. Control of SE has been hampered by the low discriminatory potential of the available subtyping tests for sub-serovar classification such as phage typing and pulsed field gel electrophoresis (PFGE).
The explosive growth in massively parallel sequencing techniques fueled in part by cost affordability, coupled with the development and increasing expertise in the field and application of bioinformatics [20, 21], have created an unprecedented opportunity to further understand the biology of SE and to explore genomics-based solutions for outbreak investigations . Despite a growing body of literature on the application of genomics and bioinformatics to organisms of the genus Salmonella, the serovar Enteritidis has until recently received little attention; only one finished SE genome is available in the public domain . The recent addition to GenBank of 106 draft genomes mainly from US isolates, albeit predominantly belonging to a single PFGE type [5, 24] dramatically improves on the available Enteritidis genome data and now provides the resource to carry out a comprehensive comparison of isolates from different parts of the world. The comparison of two SE isolates belonging to the same phage type (PT13), but different PFGE types demonstrated diversity of SE isolates at the level of single nucleotide polymorphisms or SNPs .
In our study, we used massively parallel sequencing and bioinformatics tools to sequence and characterize the chromosomes of SE strains of different PFGE and phage types isolated from poultry environments and potential food sources that did not enter the food chain in Canada. Analyses of the genomes and comparison to the reference strain SE P125109, an isolate from the United Kingdom, confirmed the similarities among different isolates of SE and reinforced the organism’s clonal nature. Simultaneously, we demonstrated inherent diversity among a number of SE isolates at many SNP loci and in their prophage content, paving the way for the development of tests that could be used to differentiate lineages and to subtype isolates of SE for the purpose of tracking through biological systems including foods, the environment and infected humans.
Genomic DNA sequencing
Whole genome analysis of the chromosome of Salmonella Enteritidis isolates of Canadian origin
GENOME ASSEMBLY STATISTICS (NG50 contig size)
GENOME SIZE (bp)
NUMBER OF GENES
Illumina (HiSeq), mate paired
Illumina (HiSeq), mate paired
Illumina (HiSeq), mate paired
Illumina (HiSeq), mate paired
Illumina (HiSeq), mate paired
454 shot gun
454, shot gun
454, paired end
454, paired end
454, paired end
454, paired end
SalmonellaEnteritidis genome assembly
The Illumina mate pair reads assembled into an average of 14 contigs per genome (10 -18 contigs; n = 5 genomes) and collectively covered approximately 99% (98.7 - 98.8%) of the corresponding genome maps. The next best assemblies were observed for the Roche 454 paired libraries which had an average of 40 contigs (36 - 45 contigs; n = 4 genomes) all of which assembled into a single chromosome covering almost the entire genome length (99.9%). The shot gun Roche 454 libraries covered 97.6% or 99.0% of the genome over 59 or 63 contigs, respectively (n = 2 genomes). The DNA fragments missing from the reference assembled molecules but present in the genome map were readily identified among the de novo assembled contigs developed using the same raw reads. Notable among the DNA fragments present in both the de novo assembled chromosome and the genome map, but absent in the reference assembled genome, was a contiguous stretch covering approximately 36 kb which was present in 10 of the 11 field isolates and was found to code for the ELPHis prophage (see under Discussion). A second unique DNA sequence of 28 kb was detected in three of our genomes (SE 7, 10 and 11). This prophage sequence has not been previously described for Enteritidis and is now provisionally designated as SE-OLF-10058(3) phage, after the laboratory identification number of one of the three genomes in which it was found. A third unique prophage sequence, 43 kb, was found in only one genome and has been provisionally designated as SE-OLF-10012 phage.
The ≤1% disagreement between the genome map and the corresponding set of de novo contigs consisted of two types of gaps: those located between contigs following scaffolding into a single molecule using Newbler assembler (Roche paired end) or following concatenation of contigs according to the order predicted by genome maps (Illumina and Roche shot gun), and those gaps present within contigs due to the inability of the assembly algorithm to determine an accurate order of sequence. The overlay of the reference assembled genome with alignment of de novo contigs and genome maps allowed the identified gaps to be resolved and filled with the corresponding parts of the reference assembly that did not show any discrepancy with the genome map. The gaps were due to sequences subsequently identified as repeating elements and were eventually localized on the genomes. Seven gaps were each attributed to sequences of about 4.4 - 5.4 kb which were found to code for the ribosomal DNA (rDNA). The raw reads for the rDNA sequences typically assembled into three or parts of three separate contigs each containing the coding sequences for the three ribosomal RNA subunits, i.e., 23S, 16S and 5S rRNA, all belonging to rRNA rrn operon, known to occur seven times in the chromosome of all members of the Salmonella genus . A second set of two or three gaps, depending on the genome, was due to the sequence of an oxaloacetate decarboxylase gene. The third type of repetitive elements affecting genome assembly was due to the cytochrome c oxidase operon which occurred at least twice in each genome and consisted of many but variable numbers of ccm genes. Occasionally, a specific gene that has not been duplicated in the genome, e.g., SseI, failed to assemble into a bigger contig and in the case of one genome, a transfer RNA (tRNA-Lys) occupied a gap site. To verify our hybrid assembly strategy and ensure all identified gaps were closed, we sequenced a total of 20 amplicons covering inter- and intra-contig gaps in one of the genomes (SE 2). The amplicon sizes as estimated on an agarose gel were in agreement with the expected gap sizes in all cases (±50 bp). Sanger sequencing of the amplicons showed excellent agreement with the genome sequences derived from the use of reference assembled molecules as part of the hybrid assembly strategey. As the basis of developing a high resolution genome assembly, the de novo procedure was very successful as judged by NG50 values of >125,000 bp for each of the genomes (Table 1).
Apart from our use of genome maps  and additional Sanger sequencing to ensure the completeness of the genomes assembled using the hybrid approach as described above, we also compared the entire nucleotide sequence of one of our isolates with a sequence of the same isolate generated with the Pacific Biosciences single molecule sequencing protocol followed by error correction of the assembled long reads at the Genome Quebec Sequencing Centre, Montreal. The two molecules had 99.98% nucleotide match and a complete agreement of their in silico maps (Ogunremi et al., manuscript in preparation).
To estimate the genome coverage, raw nucleotide reads from both Illumina and 454 sequencing platforms were trimmed and filtered. Sequences that were overabundantly represented in the raw reads which would have inflated coverage estimates were removed. Sequences showing extremely high or extremely low coverage would have artificially altered the actual coverage estimate if included. Fully assembled genomes had a median size of 4,678,683 bp (range = 4,671,261 to 4,710,936 bp; Table 1). We estimated the genome coverage to be 205× for the Illumina reads (range 169 - 229; n = 5) and 30× for the 454 reads (26 - 44; n = 6).
Genome annotation using xBASE, BaSys and RAST led to the identification of genes present in each genome (Additional file 2). The number of gene coding sequences ranged from 4,684 – 4,745 indicating about 1% variation among the SE genomes (Table 1; Additional file 1). The pan genome of SE was estimated to consist of 4,833 genes (n = 11 genomes) with the core genome comprising of 4,600 genes (95%) and just 233 genes (5%) defining the accessory genome. Prophage sequences constituted the majority of our accessory SE genome (141 of 233 genes or 61%) as determined by genome annotation software and confirmed by BLAST analysis (Additional file 2). Five prophages or prophage remnants of 4.0, 8.0, 11.5, 14.8 and 42.0 kb were found in all genomes as well as in the reference P125109 strain. A sixth prophage sequence of size 8.2 kb was absent in one of the field genomes (SE 11) but was present in all of the remaining genomes including the reference genome (Figure 1). Prophage diversity was observed in the variable number of prophages present among the isolates and in the sequence composition of prophages when present in multiple isolates (Additional file 2). Two novel prophage sequences were also identified. A 28kb prophage fragment, provisionally designated as OLF-10058 (3) was observed in three genomes, namely SE 7, 10 and 11 (Figure 1 and Additional file 2). The second unique prophage designated as SE OLF-10012 prophage is a 43 kb sequence found in the clam isolate (SE 9; Figure 1 and Additional file 2).
Single nucleotide polymorphisms
Pairwise comparison of Salmonella Enteritidis genomes based on the number of single nucleotide polymorphic loci
Evolutionary relationships among the SE isolates
Characteristics of eleven field isolates of Salmonella Enteritidis obtained in Canada and used in this study
YEAR OF ISOLATION
PROVINCE (SAMPLING LOCATION)
Poultry hatchery environment
Poultry hatchery environment
Poultry hatchery environment
Poultry hatchery environment
Poultry hatchery environment
Poultry hatchery environment
Poultry hatchery environment
13 or 1b
Poultry hatchery environment
Poultry hatchery environment
We used two different sequencing platforms in this study, namely Illumina and Roche 454. The relative strengths and weaknesses of massively parallel sequencing platforms have been well described in numerous publications [21, 27]. In this study, both the Illumina and 454 platforms performed adequately well. The sizes of all eleven genomes were very similar all falling within a narrow range (mean ± SD) with no detectable size bias towards one sequencing platform or the other; our largest and smallest genomes, albeit not so disparate, were both sequenced with the Roche 454 platform. We, however, observed a significant difference in the ease of assembling depending on the type of library: paired end Roche 454 libraries were readily assembled into a single scaffold using the Newbler software. Raw reads from the Illumina’s mate pair or Roche 454 shot gun libraries were de novo assembled into multiple contigs following an initial effort and required further work to attain a single scaffold. Nevertheless, gaps of indeterminate nucleotides were still present in the single scaffold from the paired end 454 Roche sequence reads, thus requiring further analysis to achieve a finished genome by employing an innovative hybrid strategy to achieve high quality genome assembly.
Our effort to arrange contigs into a high quality assembly and resolve gaps present in the scaffolds benefitted greatly from the availability of genome (optical) maps. Knowledge of the order and orientation of Nco I fragments in each genome helped to confirm the arrangement of the contigs or scaffolds developed from the de novo assembly of raw reads. Predictably, a number of fragments found in the genome map were found missing in the reference assembled genome because of template bias. However, these fragments were found in the de novo assembled contigs. Indeed, de novo assembly by itself led to identification of approximately 99% of the chromosome of each of the 11 genomes studied while further comparison with the respective genome maps identified the approximate size of any gaps in these assemblies. The genome map also provided the needed confidence to employ parts of the reference assembled molecule to fill any missing gaps by using areas with identical patterns of Nco I restriction sites in the genome map and reference assembled molecule, a strategy that would otherwise have raised some doubts because of the bias inherent in reference assembled molecule. The general genetic similarity in the chromosome of SE and ability to generate good reference assemblies which, although may suffer the bias of the reference molecule, were nevertheless verified against optical maps and by Sanger sequencing and provided an advantage in facilitating the filling of gaps encountered during the course of producing finished genomes. We observed that a high number of genes were shared among our isolates, i.e., core genes at 95%, which was higher than estimates for Salmonella Paratyphi A (87.5%, n = 5 genomes , Streptococcus agalactiae (approximately 80%, n = 8 genomes; ), or Listeria monocytogenes (approx. 60%, n =28 genomes, ; 68%, n = 60 genomes . Our genome annotation results indicate that the SE accessory genome pool consisted largely of prophages. The presence of a contiguous fragment in our genome map which was not present in the reference assembled genome led to further analysis including de novo assembly and the subsequent identification in 10 of 11 genomes of the recently described ELPHis prophage . One of the significant observations in this study is how misleading and severely limiting is a sole reliance on reference assembled molecules to infer the genetic structure or to study diversity of any bacterial organism, even for an organism as clonal as SE. For example, we would have altogether missed the preponderance of the ELPHis prophage in our isolates if we relied only on a reference assembly strategy. Our hybrid assembly procedure was also useful in identifying a unique 28 kb fragment coding for bacteriophage P2 which was present in three of our field isolates (SE 7, 10 and 11; Figure 1 and Additional file 2) which we designate OLF-10058(3). This prophage, which has not been reported for the serovar Enteritidis, showed a high degree of identity (98%) with gp19 protein-containing phages present in each of Salmonella serovar Newport , serovar Hirschfeldii - formerly Paratyphi C , Paratyphi – formerly Paratyphi A , Heidelberg , and Salmonella bongori. Significant matches (86-93% identity) in areas covering less than half the length of the phage were also observed with elements in non-Salmonella organisms such as Escherichia coli, Enterobacter cloacae and Cronobacter sakazakii, but a unique contribution appeared to have come from serovar Typhimurium  with a high match (93%) which overlapped a majority of the phage sequence (18 kb of the 28 kb). Another unique prophage designated as SE OLF-10012 was found in the isolate obtained from a marine source (SE 9) and an intact copy of this prophage has not previously been reported in SE although as much as 7.6kb of the 43 kb prophage (17%) matched sequences annotated as prophage proteins in the SE reference P125109 strain . A much larger portion of the prophage sequence (63%) had a significant match with S. bongori sequence which although largely un-annotated, contained an identified phage tail fibre sequence . Our study suggests that variable prophage combinations may occur in SE strains thus providing an opportunity for subtyping SE. Many prophage encoded genes are transcriptionally silent, acquire mutations that convert them into pseudogenes and in time undergo degenerative changes and become phage remnants . In addition, the persistence of prophage sequences in the genome even though they could contain different modules as a result of gene degradation in an isolate  could be exploited for lineage tracking. Collectively, these changes could be analyzed for the purpose of tracking a strain. We are currently pursuing a prophage-based subtyping procedure as an adjunct to a newly developed single nucleotide polymporphism-based (SNP-PCR) method (manuscript under preparation) which together should lead to a comprehensive description of the evolutionary map for SE in Canada. Isolates that are closest on our SNP-based phylogenetic tree (Figure 2) had identical or similar prophage sequence composition (Figure 1).
Single nucleotide polymorphism is one of the commonest forms of genetic variation and appears to be the most promising approach for the genotyping of a highly clonal organism such as SE. A recent study identified 247 chromosomal SNPs differentiating between two isolates of SE . In our study, a comparison of 11 genomes with the reference SE strain led to the identification of 1,360 chromosomal SNPs. A phylogenetic tree constructed using these SNPs clearly demonstrated the genetic distances among the field isolates (Figure 2) despite what appeared to be a profound genetic similarity among them (Figure 1 and Tables 1 and 3). Results of pairwise SNP comparisons (Table 2) provided quantitative estimates of the genetic distances between 12 genomes analysis (11 + reference strain) as visualized by phylogenetic tree (Figure 2). The distribution of our pairwise SNP analysis showed that an isolate from clam was the most genetically distant of our isolates, and differed by an average of 876 SNPs (range 840 – 905) from all the Canadian isolates and the reference genome sourced from the United Kingdom. At the other end of the spectrum, we had two isolates that differed by just 23 SNPs inferring very close genetic similarity and confirmed by the historical metadata which showed that the isolates were obtained from the same poultry establishment one year apart (2008 and 2009). All the other pairwise comparisons occupied a range between 27 – 905 SNPs. The number of SNPs is expected to change as more SE genomes are studied. The distribution of our pairwise SNP count showed three discrete, non-overlapping populations, namely 23 - 134, 201 - 268 and 594 - 905 SNPs probably providing a quantitative estimate of isolates that are closely related, distantly related and unrelated, respectively. A note of caution is necessary. The use of bioinformatics software to infer characteristics of assembled genomes including the presence of SNPs usually require confirmation by other laboratory procedures before adoption for regulatory use because of a possible wide range of implications. Errors from the assembly procedure may translate to spurious SNPs. By the use of a rigorous genome assembly process aided by the availability of genome maps to confirm correct contig orientation, such errors have been minimized in this study. Furthermore, bioinformatics algorithms could have inherent errors which may not be easily obvious to a biologist. Confirmatory analysis either using other software or a wet laboratory approach could serve to detect such errors. All these mean that the use of bioinformatics analysis to deduce properties of a genome, especially at this stage of the development of the field of genomics will benefit from a wet chemistry approach especially for the development of a tool that is expected to serve a regulatory need. This approach is the basis of a separate communication in which we have used SNP-PCR to develop a highly discriminatory molecular subtyping tool for SE (Ogunremi et al., manuscript submitted).
We have developed high resolution genome sequences for the chromosomes of eleven Canadian SE isolates by using a hybrid assembly method of sequence reads which relied on a composite of reference and de novo genome assemblies and comparison with genome maps. The procedure is easy to perform and allows the resolution of gaps that would have been caused by repetitive elements such as the rrn operon. High resolution assembly allows a definite assessment of the high degree of similarity among our field SE genomes and accurate description of the SE genome characteristics including the first report of the core and accessory genomes of SE based on fully assembled molecules. Despite its relatively large size, the core genome of SE shows abundant diversity expressed as SNPs which should allow for strain differentiation. We suggest that pairs of isolates that differ by < 150 SNPs may be closely related while isolates showing up to 600 SNPs or more are unrelated. The accessory genome of SE which consisted mostly of prophage sequences can also be exploited for SE lineage tracking because of variable composition of prophages. The same prophage sequence may show different degree of degeneration in distantly related or unrelated isolates. Whole genome analyses of SE isolates were useful in delineating and quantitatively estimating the genetic distances between isolates. This study has laid the ground work for further studies to develop a readily adoptable laboratory test for the subtyping of SE.
Strains of Salmonellaserovar Enteritidis
Isolates of Salmonella serovar Enteritidis (n = 11) used in the study were retrieved from the Canadian Food Inspection Agency (CFIA) inventory. The isolates were all of Canadian origin and came from poultry environments (n = 9), a clam and a chicken (Table 3). Phage typing was carried out at the Public Health Agency of Canada (PHAC) Salmonella Reference Centre, Guelph as previously described . Pulsed Field Gel Electrophoresis (PFGE) analysis was performed at the CFIA Ottawa Laboratory Fallowfield following an international, standardized protocol (http://www.pulsenetinternational.org/protocols/) involving the use of the restriction enzymes Xba I and Bln I to create signature molecular patterns which were electronically submitted to PulseNet Canada (National Microbiology Laboratory, PHAC, Winnipeg) for PFGE subtype designation.
Genomic DNA was purified from SE culture using the Wizard® Genomic DNA Purification Kit (Promega, Madison, WI). Frozen bacterial glycerol stocks were thawed, inoculated into BHI broth and then incubated at 37°C with agitation at 200 rpm. DNA purified from 1 ml of an overnight culture was assessed for quality by absorbance reading at optical density values of 260 and 280 nm (OD260/280) using a spectrophotometer (DU 730 Beckman Coulter, Mississauga, Canada), and was quantified by Quant-iT PicoGreen dsDNA Assay Kit (Life Technologies, Carlsbad, CA) with a fluorometer (VersaFluor, BioRad Laboratories, Mississauga, Ontario). Whole genome sequencing was performed at the Genome Quebec Sequencing Centre using the Illumina HiSeq 2000 platform (McGill University, Montreal) or at the Public Health Agency of Canada using the Roche 454 platform (National Microbiology Laboratory, Winnipeg).
The Illumina platform was used for isolates SE 1 – 5 (see Table 1) and the steps which consisted of mate pair library construction, DNA sequencing, and raw data processing were carried out according to the manufacturer’s protocol (Illumina, San Diego, CA). The remaining isolates were sequenced on the Roche 454 platforms using shot gun (SE 6-7) or paired end (SE 8 – 11; Table 1) library construction, DNA sequencing and raw read processing as recommended by manufacturer (Roche Diagnostics, GmbH, Mannheim, Germany).
Genome sequence analysis and assembly
The assembly of each SE chromosome from raw genome reads into a single molecule was carried out by using a hybrid strategy that relied on de novo assembly, reference assembly and generation and analysis of genome (optical) maps. Raw reads from the Illumina genome sequencers were trimmed using the fastx toolkit and imported into the CLC Genomics Workbench (CLC bio, Aarhus, Denmark). Raw reads from the 454 Sequencer were processed and trimmed with the GS Reporter application software (Roche Diagnostics GmbH, Mannheim, Germany). De novo assemblies were generated using CLC Genomics (for Illumina and 454 shot gun sequences) or the GS de novo Newbler assembler (454 paired end sequences). A template-dependent or reference assembly version of each genome was also generated with the CLC Genomics software with the aid of the published chromosome sequence of SE strain P125109 phage type 4 . For comparative purposes, other software or procedures were used to generate reference and de novo assemblies (e.g., Lasergene, DNASTAR Inc., Madison, WI; Geneious R6, BioMatters Limited, Auckland, New Zealand; Ray Assembler, Velvet assembler) for each genome (data not shown) but because the outputs were similar to that from CLC Genomics, the results from the latter software were used for further analysis. To assess the accuracy of the nucleotide reads in each contig, scaffold or whole molecule, and to confirm the orientations of contigs and their locations in the chromosome, all of the reference or de novo assemblies were aligned against a genome map generated for each respective chromosome using the Argus optical mapping system (OpGen Inc., Gaithersburg, MD) as previously described . We generated independent maps for each of the eleven SE isolates by isolating high molecular weight genomic DNA from a single bacterial colony using the Argus Sample preparation Kit in conjunction with the Agencourt Genfind v2 DNA isolation kit as described by the manufacturer (OpGen Inc.) and ensured the DNA quality using the Argus QCard kit. After determining the choice of the Nco I restriction enzyme by means of the Argus Enzyme Chooser module (OpGen Inc.), stained genomic DNA was digested and the ensuing fragments were imaged using fluorescence microscopy on the Argus WGM system which automatically documents the fragment sizes in a manner that allowed an orderly arrangement as expected in the chromosome. The genome map was visualized using the MapSolver software (Opgen). Gaps and misalignment between the genome maps and the in silico maps generated from the nucleotide sequence of each candidate chromosome were identified by exploring the alignments of these regions with the corresponding reference assembled molecules generated with up to three different software programs (i.e., CLC Genomics, LaserGene or Geneious). The fragment judged to represent the correct fill for a gap was imported into Clone Manager (Professional edition, Scientific and Educational software, Cary, NC) and joined or concatenated with the genome using Clone manager or Geneious. In all instances, there were agreements between at least two sets of molecules produced by either reference or de novo assembly or genome mapping. The genome map was used as a standard to build up the genome scaffold from de novo assembled contigs however, there were occasions when small-sized DNA fragments, usually less than 1 kb, present as part of de novo contigs were observed either in the reference assembled genome or other Salmonella genomes developed during this study, but absent in the genome maps. To verify that all identified gaps were accurately filled by our hybrid assembly strategy, we developed PCR primers and amplified the fragment sequences spanning all the gaps identified in one of the genomes (SE 2). The sequence composition and size of each amplicon were determined by Sanger sequencing (ABI 3130; Life Technologies, Burlington, Ontario) and compared with the filled gaps which in all cases show full agreement with no detectable ambiguities (data not shown). De novo genome assembly was evaluated by measuring the NG50, the median contig length at which 50% of the total genome length has been covered. Contig lengths > 1000 bp were used for assembly evaluation. The success of the hybrid strategy of assembly was assessed by comparing one of the hybrid assembled genomes (SE 2; Table 1) with a genome sequence for the same isolate generated using the Pacific Biosciences single molecule read platform (courtesy of Dr. Ken Dewar, McGill University-Genome Quebec Sequencing Centre, Montreal, Canada).
Assembled SE genomes were annotated with the open source xBASE bacterial genome annotation pipeline (http://www.xbase.ac.uk/) using the reference SE strain P125109. The xBASE pipeline predicts coding sequences (genes) using the Glimmer software , tRNA genes using tRNAScan-SE  and ribosomal genes with RNAmmer . The annotation process involved a protein BLAST analysis (http://www.ncbi.nlm.nih.gov) of translated coding sequences of the target and reference genomes to obtain the best match as shown by the E-value probability index . The complete list of coding sequences for each genome was exported into Excel (Microsoft) to create a database after sorting the genes using an in-house algorithm. To overcome the potential constraint of using a reference genome in the annotation procedure as required for the xBASE procedure, we used two other programs, namely, Bacterial Annotation System or BaSys (; https://www.basys.ca/) and Rapid Annotation Subsystem Technology, RAST (; http://rast.nmpdr.org/), neither of which required that a reference genome be identified to achieve annotation. The list of coding sequences determined for each genome represented an amalgamation of all open reading frames detected by all three annotation programs. We directly investigated the prophage sequences present in each of the genomes using the prophage finder software . We identified all genes that were present in each of the 11 genomes (i.e., core genome) and compared the number of genes that were found to be absent in at least one of the genomes studied (n ≤ 10; accessory genome).
Single nucleotide polymorphisms
Single nucleotide differences in the genomes compared to the reference SE strain P125109 were detected by means of the SNPsfinder program (; http://snpsfinder.lanl.gov/, Los Alamos National Laboratory, NM). Pairwise comparisons of the genomes were also carried out as a measure of genetic distance between members of each pair. Mapped reads of the genomes were analyzed by means of CLC Genomics software to confirm the presence of SNPs and to evaluate whether an observed nucleotide difference could have been due to a sequencing error. In a number of cases, oligonucleotide primers were developed for the PCR amplification and sequencing of the fragment containing the SNPs.
The SNPs detected by the bioinformatics pipeline analysis were concatenated and used to construct a phylogenetic tree for all eleven genomes and the reference genome for the purpose of representing the spatial relationship and genetic relatedness among the eleven isolates when compared to each other and to the reference genome. The tree was generated by the neighbour joining distance method  applied to all SNPs observed in each genome using CLC Genomics software; 100 bootstrap replicates were used to evaluate the robustness of the predicted phylogeny.
Availability of supporting data
Apart from the data set provided within the article and as additional files, sequence reads and genome assemblies supporting the results are available in the GenBank repository, deposited under the following Bioproject ID PRJNA256209. The 11 assembled genomes are deposited under accession numbers CP009083 – CP009093.
Canadian Food Inspection Agency
Polymerase chain reaction
Public Health Agency of Canada
Pulsed field gel electrophoresis
Single nucleotide polymorphism.
Financial support was provided by the Canada Strategy for Regulating Biotechnology Fund of the Canadian Food Inspection Agency, and the Ontario Ministry of Agriculture, Food and Rural Affairs (OMAFRA).
Authors are grateful for whole genome sequencing and portion of genome assembly services provided by Morag Graham and Shaun Tyler of the National Microbiology Laboratory, Public Health Agency of Canada, and Ken Dewar, Alfredo Staffa, Mathieu Bourgey and Gary Leveque of the McGill University and Genome Québec Innovation, Montreal. Authors acknowledge the kind assistance of, input from, and fruitful discussions with many collaborators and colleagues including Susan Nadin-Davis, Burton Blais, Catherine Carillo, Vanessa Musca, Teresa Burke, Victoria Arling, Katie Eloranta, Neil Vary, Kristen Hahn, Noriko Goji, Louise Pope, Sam Mohajer, Ray Allain, Mohamed Elmufti (Canadian Food Inspection Agency), Matthew Gilmour (Diagnostic Services of Manitoba), Roger Johnson, Linda Cole, Betty Wilkie, Ketna Mistry, and Ann Perets (OIÉ/WAHO Reference Laboratory for Salmonellosis, PHAC, LFZ, Guelph, Ontario, Canada).
- Vieira AR: WHO global foodborne infections network country databank – a resource to link human and non-human sources of salmonella. Durban, South Africa: ISVEE Conference, http://www.who.int/gfn/activities/CDB_poster_Sept09.pdf Accessed 23 October 2013
- Nesbitt A, Ravel A, Murray R, McCormick R, Savelli C, Finley R, Parmley J, Agunos A, Majowicz SE, Gilmour M: Integrated surveillance and potential sources of Salmonella Enteritidis in human cases in Canada from 2003 to 2009. Epidemiol Infect. 2012, 140: 1757-1772. 10.1017/S0950268811002548.PubMed CentralPubMedView ArticleGoogle Scholar
- Rodrigue DC, Tauxe RV, Rowe B: International increase in Salmonella enteritidis: a new pandemic?. Epidemiol Infect. 1990, 105: 21-27. 10.1017/S0950268800047609.PubMed CentralPubMedView ArticleGoogle Scholar
- Batz MB, Hoffmann S, Morris JG: Ranking the risks: the 10 pathogen-food combinations with the greatest burden on public health. 2011, Emerging Pathogen Institute: University of Florida, GainesvilleGoogle Scholar
- Timme RE, Allard MW, Luo Y, Strain E, Pettengill J, Wang C, Li C, Keys CE, Zheng J, Stones R, Wilson MR, Musser SM, Brown EW: Draft genome sequences of 21 Salmonella enterica serovar Enteritidis strains. J Bacteriol. 2012, 194: 5994-5995. 10.1128/JB.01289-12.PubMed CentralPubMedView ArticleGoogle Scholar
- Landry L, Dutil L: Overview of salmonella enteritidis in Canada. Proceedings of the Canadian salmonella enteritidis symposium and workshop. 1 Dec 2010. 2010, Vancuover, Canada: British Columbia Centre for Disease Control, 3-4. http://www.bccdc.ca/dis-cond/a-z/_s/SalmonellaInfection/SalmonellaSymposium.htm,Google Scholar
- Kuehn BM: Salmonella cases traced to egg producers: findings trigger recall of more than 500 million eggs. JAMA. 2010, 304: 1316-10.1001/jama.2010.1330.PubMedView ArticleGoogle Scholar
- Crosa JH, Brenner DJ, Ewing WH, Falkow S: Molecular relationships among the salmonellae. J Bacteriol. 1973, 115: 307-315.PubMed CentralPubMedGoogle Scholar
- Vandamme PAR: Taxonomy and classification of bacteria. Manual of clinical microbiology. Edited by: Murray PR, Baron EJ, Jorgensen JH, Pfaller MA, Yolken RH. 2003, Washington DC: United States: ASM Press, 271-285.Google Scholar
- Brenner FW, Villar RG, Angulo FJ, Tauxe R, Swaminathan B: Salmonella nomenclature. J Clin Microbiol. 2000, 38: 2465-2467.PubMed CentralPubMedGoogle Scholar
- Guibourdenche M, Roggentin P, Mikoleit M, Fields PI, Bockemühl J, Grimont PAD, Weil F-X: Supplement 2003-2007 (No 47) to the white-Kauffmann-Le minor scheme. Res Microbiol. 2010, 161: 26-29. 10.1016/j.resmic.2009.10.002.PubMedView ArticleGoogle Scholar
- Tindall BJ, Grimont PAD, Garrity GM, Euzeby JP: Nomenclature and taxonomy of the genus Salmonella. Int J Syst Evol Microbiol. 2005, 55: 521-524. 10.1099/ijs.0.63580-0.PubMedView ArticleGoogle Scholar
- Marlony B, Hauser E, Dieckmann R: New approaches in subspecies-level Salmonella classification. Salmonella - from genome to function. Edited by: Porwollik S. 2011, United Kingdom: Caister Academic Press, 1-24.Google Scholar
- Hudson CR, Garcia M, Gast RK, Maurer JJ: Determination of close genetic relatedness of the major Salmonella Enteritidis phage types by pulsed-field gel electrophoresis and DNA sequences analysis of several Salmonella virulence genes. Avian Dis. 2001, 45: 875-886. 10.2307/1592867.PubMedView ArticleGoogle Scholar
- Olson AB, Andrysiak AK, Tracz DM, Guard-Bouldin J, Demczuk W, Ng LK, Maki A, Jamieson F, Gilmour MW: Limited genetic diversity in Salmonella enterica serovar Enteritidis PT13. BMC Microbiol. 2007, 7: 87-10.1186/1471-2180-7-87.PubMed CentralPubMedView ArticleGoogle Scholar
- Marlony B, Junker E, Helmuth R: Multi-locus variable-number tandem repeat analysis for outbreak studies of Salmonella enterica serotype Enteritidis. BMC Microbiol. 2008, 8: 84-10.1186/1471-2180-8-84.View ArticleGoogle Scholar
- Guard J, Morales CA, Fedorka-Cray P, Gast RK: Single nucleotide polymorphisms that differentiate two populations of Salmonella enteritidis within phage type. BMC Res Notes. 2011, 4: 369-10.1186/1756-0500-4-369.PubMed CentralPubMedView ArticleGoogle Scholar
- Guard-Petter J: Variants of smooth Salmonella enterica serovar Enteritidis that grow to higher cell density than the wild type are more virulent. Appl Environ Microbiol. 1998, 64: 2166-2172.PubMed CentralPubMedGoogle Scholar
- Humphrey TJ, Williams A, McAlpine K, Lever MS, Guard-Petter J, Cox JM: Isolates of Salmonella enterica Enteritidis PT4 with enhanced heat and acid tolerance are more virulent in mice and more invasive in chickens. Epidemiol Infect. 1996, 117: 79-88. 10.1017/S0950268800001151.PubMed CentralPubMedView ArticleGoogle Scholar
- Kircher M, Kelso J: High-throughput DNA sequencing-concepts and limitations. BioEssays. 2010, 32: 524-536. 10.1002/bies.200900181.PubMedView ArticleGoogle Scholar
- Loman NJ, Constantinidou C, Chan JZM, Halachev M, Sergeant M, Penn CW, Robinson ER, Pallen MJ: High-throughput bacterial genome sequencing: an embarrassment of choice, a world of opportunity. Nat Rev. 2012, 10: 599-606.Google Scholar
- Leekitcharoenphon P, Nielsen EM, Kaas RS, Lund O, Aarestrup FM: Evaluation of whole genome sequencing for outbreak detection of Salmonella enterica. PLoS ONE. 2014, 9: e87991-10.1371/journal.pone.0087991.PubMed CentralPubMedView ArticleGoogle Scholar
- Thompson NR, Clayton DJ, Windhort D, Vernikos G, Davidson S, Churcher C, Quail MA, Stevens M, Jones MA, Watson M, Barron A, Layton A, Pickard D, Kingsley RA, Bignell A, Clark L, Harris B, Ormond D, Abdellah Z, Brooks K, Cherevach I, Chillingworth T, Woodward J, Norberczak H, Lord A, Arrowsmith C, Jagels K, Moule S, Mungall K, Sanders M, et al: Comparative genome analysis of Salmonella Enteritidis PT 4 and Salmonella Gallinarum 287/91 provides insights into evolutionary and host adaptation pathways. Genome Res. 2008, 18: 1624-1637. 10.1101/gr.077404.108.View ArticleGoogle Scholar
- Allard MW, Luo Y, Strain E, Pettengill J, Timme R, Wang C, Li C, Keys CE, Zheng J, Stones R, Wilson MR, Musser SM, Brown EW: On the evolutionary history, population genetics and diversity among isolates of Salmonella Enteritidis PFGE pattern JEGX011.0004. PLoS ONE. 2013, 8: e55254-10.1371/journal.pone.0055254. 1-21PubMed CentralPubMedView ArticleGoogle Scholar
- Guard J, Shah D, Morales CA, Call D: Evolutionary trend associated with niche specialization as modelled by whole genome analysis of egg-contaminating Salmonella enterica serovar Enteritidis. Salmonella - From genome to function. Edited by: Porwollik S. 2011, United Kingdom: Caister Academic Press, 91-106.Google Scholar
- Lescat M, Calteau A, Hoede C, Barbe V, Touchon M, Rocha E, Tenaillon O, Médigue C, Johnson JR, Denamur E: A module located at a chromosomal integration hot spot is responsible for the multidrug resistance of a reference strain from Escherichia coli clonal group A. Antimicrob Agents Ch. 2009, 53: 2283-2288. 10.1128/AAC.00123-09.View ArticleGoogle Scholar
- Glenn TC: Field guide to next-generation DNA sequencers. Mol Ecol Resour. 2011, 11: 759-769. 10.1111/j.1755-0998.2011.03024.x.PubMedView ArticleGoogle Scholar
- Liu SL, Hessel A, Sanderson KE: Genomic mapping with I-Ceu I, an intron-encoded endonuclease specific for genes for ribosomal RNA in Salmonella spp., Escherichia coli, and other bacteria. Proc Natl Acad Sci U S A. 1993, 90: 6874-6878. 10.1073/pnas.90.14.6874.PubMed CentralPubMedView ArticleGoogle Scholar
- Onmus-Leone F, Hang J, Clifford RJ, Yang Y, Riley MC, Kuschner RA, Waterman PE, Lesho EP: Enhanced de novo assembly of high throughput pyrosequencing data using whole genome mapping. PLoS ONE. 2013, 8: E61762-10.1371/journal.pone.0061762.PubMed CentralPubMedView ArticleGoogle Scholar
- Liang W, Zhao Y, Chen C, Cui X, Yu J, Xiao J, Kan B: Pan-Genomic Analysis Provides Insights into the Genomic Variation and Evolution of Salmonella Paratyphi A. PLoS ONE. 2012, 7: e45346-10.1371/journal.pone.0045346.PubMed CentralPubMedView ArticleGoogle Scholar
- Tettelin H, Masignani V, Cieslewicz MJ, Donati C, Medini D, Ward NL, Angiuoli SV, Crabtree J, Jones AL, Durkin AS, DeBoy RT, Davidsen TM, Mora M, Scarselli M, Margarit y Ros I, Peterson JD, Hauser CR, Sundaram JP, Nelson WC, Madupu R, Brinkac LM, Dodson RJ, Rosovitz MJ, Sullivan SA, Daugherty SC, Haft DH, Selengut J, Gwinn ML, Zhou L, Zafar N, et al: Genome analysis of multiple pathogenic isolates of Streptococcus agalactiae: implications for the microbial “pan-genome. Proc Natl Acad Sci U S A. 2005, 102: 13950-13955. 10.1073/pnas.0506758102.PubMed CentralPubMedView ArticleGoogle Scholar
- Deng X, Phillippy AM, Li Z, Salzberg SL, Zhang W: Probing the pan-genome of Listeria monocytogenes: new insights into intraspecific niche expansion and genomic diversification. BMC Genomics. 2010, 11: 500-520. 10.1186/1471-2164-11-500.PubMed CentralPubMedView ArticleGoogle Scholar
- Kruczkiewicz P: A comparative genomic framework for the in silico design and assessment of molecular typing methods using whole-genome sequence data with application to Listeria monocytogenes. MSc thesis. 2013, University of Lethbridge, Canada: Department of BiologyGoogle Scholar
- Hanna LF, Matthews TD, Dinsdale EA, Hasty D, Edwards R: High-thoroughput identification of horizontal gene transfer: detection of viable phage in Salmonella enterica serovar Enteritidis strain LK5. Appl Environ Microbiol. 2012, 78: 1785-1793. 10.1128/AEM.07241-11.PubMed CentralPubMedView ArticleGoogle Scholar
- Ravel J, Fricke WF, White D, McDermott P, Mammel M, Rosovitz M, Leclerc J, Cebula T, Sebastian Y: Complete genome of Salmonella newport strain SL254. http://www.ncbi.nlm.nih.gov/nuccore/CP006631.1,
- Liu WQ, Feng Y, Wang Y, Zou QH, Chen F, Guo JT, Peng YH, Jin Y, Li YG, Hu SN, Johnston RN, Liu GR, Liu SL: Salmonella Paratyphi C: genetic divergence from Salmonella choleraesuis and pathogenic convergence with Salmonella typhi. PLoS ONE. 2009, 4: e4510-10.1371/journal.pone.0004510.PubMed CentralPubMedView ArticleGoogle Scholar
- McClelland M, Sanderson KE, Clifton SW, Latreille P, Porwollik S, Sabo A, Meyer R, Bieri T, Ozersky P, McLellan M, Harkins CR, Wang C, Nguyen C, Berghoff A, Elliot G, Kohlberg S, Strong C, Du F, Carter J, Kremizki C, Layman D, Leonard S, Sun H, Fulton L, Nash W, Miner T, Minx P, Delehaunty K, Fronick C, Magrini V, et al: Comparison of genome degradation in Paratyphi A and Typhi, human-restriced serovars of Salmonella enterica that cause typhoid. Nat Genet. 2004, 36: 1268-1274. 10.1038/ng1470.PubMedView ArticleGoogle Scholar
- Fricke WF, Mammel MK, McDermott FF, Tartera C, While DG, Leclerc JE, Ravel J, Cebula TA: Salmonella enterica subsp. enterica serovar Heidelberg str. SL 476. Comparative genomes of 28 Salmonella enterica isolates; evidence for CRISPR-mediated adaptive sublineage evolution. J Bacteriol. 2011, 193: 3556-3568. 10.1128/JB.00297-11.PubMed CentralPubMedView ArticleGoogle Scholar
- Marti R, Hagens S, Loessner MJ, Klumpp J: Genome sequence of Salmonella bongori N268-08 - a rare clinical isolate. 2013, http://www.ncbi.nlm.nih.gov/nuccore/CP006608,Google Scholar
- Ren Y, Ren Y, Zhou Z, Guo X, Li Y, Feng L, Wang L: Complete genome sequence of Enterobacter cloacae subsp. cloacae type strain ATCC 134047. J Bacteriol. 2010, 192: 2463-2464. 10.1128/JB.00067-10.PubMed CentralPubMedView ArticleGoogle Scholar
- Power KA, Yan Q, Fox EM, Cooney S, Fanning S: Genome sequence of Cronobacter sakazakii SP291, a persistent thermotolerant isolate derived from a factory producing powdered infant formula. Genome Announc. 2013, 1: e0008213-PubMedView ArticleGoogle Scholar
- Izumiya H, Sekizuka T, Nakaya H, Taguchi M, Oguchi A, Ichikawa N, Nishiko R, Yamazaki S, Fujita N, Watanabe H, Ohnishi M, Kuroda M: Whole-genome analysis of Salmonella enterica serovar Typhimurium T000240 reveals the acquisition of a genomic island involved in multidrug resistance via IS1 derivatives on the chromosome. Antimicrob Agents Ch. 2010, 55: 623-630.View ArticleGoogle Scholar
- Windhorst D: Analysis and characterization of the prophage content in Salmonella Enteritidis. PhD dissertation. 2010, Leibnitz University of Hannover, Germany: Faculty of Natural ScienceGoogle Scholar
- Rabsch W, Truepschuch S, Windhorst D, Gerlach RG: Typing phages and prophages of Salmonella. Salmonella - From genome to function. Edited by: Porwollik S. 2011, United Kingdom: Caister Academic Press, 25-48.Google Scholar
- Ward LR, de Sa JDH, Rowe B: A phage-typing scheme for Salmonella enteritidis. Epidemiol Infect. 1987, 99: 291-294. 10.1017/S0950268800067765.PubMed CentralPubMedView ArticleGoogle Scholar
- Delcher AL, Bratke KA, Powers EC, Salzberg SL: Identifying bacterial genes and endosymbiont DNA with Glimmer. Bioinformatics. 2007, 23: 673-679. 10.1093/bioinformatics/btm009.PubMed CentralPubMedView ArticleGoogle Scholar
- Schattner P, Brooks AN, Lowe TM: The tRNAscan-SE, snoscan and snoGPS web servers for the detection of tRNAs and snoRNAs. Nucleic Acids Res. 2005, 33: W686-W689. 10.1093/nar/gki366.PubMed CentralPubMedView ArticleGoogle Scholar
- Lagesen K, Hallin P, Rodland EA, Staerfeldt HH, Rognes T, Ussery DW: RNAmmer: consistent and rapid annotation of ribosomal RNA genes. Nucleic Acids Res. 2007, 35: 3100-3108. 10.1093/nar/gkm160.PubMed CentralPubMedView ArticleGoogle Scholar
- Altschul SF, Madden TL, Schaffer AA, Zhang J, Zhang Z, Miller W, Lipman DJ: Gapped BLAST and PSI-BLAST: a new generation of protein database search programs. Nucleic Acids Res. 1997, 25: 3389-3402. 10.1093/nar/25.17.3389.PubMed CentralPubMedView ArticleGoogle Scholar
- Van Domselaar GH, Stothard P, Shrivastava S, Cruz JA, Guo A, Dong X, Lu P, Szafron D, Greiner R, Wishart DS: BASys: a web server for automated bacterial genome annotation. Nucleic Acids Res. 2005, 33 (Web Server issue): W455-W459.PubMed CentralPubMedView ArticleGoogle Scholar
- Aziz RK, Bartels D, Best AA, DeJongh M, Disz T, Edwards RA, Formsma K, Gerdes S, Glass EM, Kubal M, Meyer F, Olsen GJ, Olson R, Osterman AL, Overbeek RA, McNeil LK, Paarmann D, Paczian T, Parrello B, Pusch GD, Reich C, Stevens R, Vassieva O, Vonstein V, Wilke A, Zagnitko O: The RAST server: Rapid Annotations using Subsystems Technology. BMC Genomics. 2008, 9: 75-10.1186/1471-2164-9-75.PubMed CentralPubMedView ArticleGoogle Scholar
- Bose M, Barber R: Prophage Finder: a prophage loci prediction tool for prokaryotic genome sequences. In Silico Biol. 2006, 6: 0020-Google Scholar
- Song J, Xu Y, White S, Miller KWP, Wolinsky M: SNPsFinder - a web-based application for genome-wide discovery of single nucleotide polymorphisms in microbial genomes. Bioinformatics. 2005, 21: 2083-2084. 10.1093/bioinformatics/bti176.PubMedView ArticleGoogle Scholar
- Jukes T, Cantor C: Evolution of protein molecules. Mammalian Protein Metabolism. Edited by: Munro HN. 1969, New York: Academic Press, 21-132.View ArticleGoogle Scholar
This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.