Otherwise, use primers for amplification after DNA denaturation, clone into specific vectors, and finally constructing single stranded DNA library. These single stranded DNAs would be fixed by 28um beads which are buried in emulsion. The biggest feature of emulsion PCR is the formation of a large number of independent reaction space for DNA amplification.
The key technology is to separate different beats using the characteristics of emulsion. The basic process is as follows. Before sample DNA amplification, aqueous solution with all components of PCR reaction will be infused into the surface of mineral oil with high-speed rotation, and it forms numerous small water droplets wrapped by mineral oil.
One small droplet forms an independent PCR reaction space. Ideally, each small drop of water contains only one DNA template and one bead. On the surface of beads, which are wrapped by small water droplets, there are complementary oligos to match those adapters, so the single stranded DNA can specifically bind to the beads.
At the same time, incubation system contains PCR reagents to ensure that each small DNA fragment fixed on the bead can be the unique template for amplification. The second produces a series of identical values for each base in a homopolymer, as in Titanium, but otherwise builds on the same Bayesian approach as the GS20 algorithm.
Compared to the quality scores assigned to Titanium by the Roche analysis pipeline, our quality scores are lower. As GS20 appears to use a fixed table mapping each flow value to a set of qualities, there is also a third option of assigning qualities from a table derived from GS20 data.
Bayes' theorem requires both the prior probability for each homopolymer length and the conditional probability for a flow value given a certain homopolymer length. In contrast to Margulies et al.
This allows us to assess the quality of our simulated data as accurately as possible. When position-specific empirical distributions are used in Flowsim, we also use these for quality score calculation. We used Flowsim to generate synthetic data sets, using our empirical distributions as the flow model. Each of the 20 distributions was used for 10 flow cycles 40 flows , giving a realistic degradation of quality along the sequence.
We also simulated data sets using flow cycles, simulating a hypothetical generation with twice the read length of the current Titanium generation. The E. We have performed both de-novo and reference-based assembly using Newbler assembler version 2.
Our results indicate that Flowsim can be useful to estimate the quality of an assembly that can be expected from using Titanium to shotgun sequence a genome. However, the assemblies resulting from our simulations were consistently better in terms of contig sizes through the N50 summarizing statistic, see Table 3 for the simulated data sets than for the real ones. There may also be other factors such as possible biases in terms of genome coverage in the experimental protocols used to generate the shotgun libraries for Titanium sequencing.
Further work will include exploring such biases and other sources of variability as well as characterizing their influence on the simulation accuracy of Flowsim. Also Flowsim will be extended to include simulation of paired-reads, which will be of high value for simulation and planning of projects for de-novo whole-genome sequencing. This study aims to sketch the opportunities that arise from analyzing pyrosequencing raw data, culminating in the use of empirical distributions.
The empirical distributions give us a very realistic picture of the underlying characteristics of the light signal values that are later translated into DNA sequences.
In contrast, earlier approaches to modeling flow data have built on parametric distributions, and the same distributions were used for whole reads, without respect to flow or read positions. Our findings and the empirical distributions are based on large amounts of data from three different species E. The empirical flow value distributions are very similar, and we have not observed any factors which influence the shape of the distributions apart from the generation. Thus, we have a good reason to believe that the distributions used in Flowsim are representative.
The flow values that result from sequencing exhibit many interesting characteristics and artifacts, and we do not address them all here. Some of these are generation-specific, some of them have remained stable over the years, and some of them only appear on one certain plate, for one certain species or in one lab.
One known artifact, exact or almost-exact duplicates, has been not only described for metagenomics in the literature Gomez-Alvarez et al. We do emulate the degradation in empirical flow distributions, and we also calculate the corresponding quality scores. In contrast, we neglect some of the artifacts that we have observed in the empirical distributions, but are not able to interpret properly yet, such as for example: shifts in peaks that lead to systematic over- or under-calls, jumps, neighboring peaks, i.
These are particularly strong for the noise distribution with a neighboring peak around 1 and the 1-distribution with neighboring peaks around 0. Analyzing the corresponding data including the related alignments we found that the subpeaks are likely to be caused by real biological differences. This will be explored further in a separate study. In this context, we also performed a weak smoothing process that helped to reduce subpeaks and jumps.
Furthermore, the image analysis software implements a set of quality filters that sets trimming coordinates to identify the high-quality part of each read. In addition, some reads are eliminated entirely based on quality metrics. Although these filters are documented Roche Applied Science, , the documentation is not sufficient to re-implement them, and the current version of Flowsim does not attempt to simulate them.
We hope to address this in a future release Fig. De novo and reference-based N50 for E. Both real and simulated data were assembled using Newbler v2. In conclusion, our simulator produces sufficiently realistic files as we model all important phenomena that we have observed. Furthermore, Flowsim allows the user to specify many of its parameters, making it adaptable to new real or hypothetical generations. Notur is acknowledged for access to the Titan cluster in Oslo.
Google Scholar. Oxford University Press is a department of the University of Oxford. It furthers the University's objective of excellence in research, scholarship, and education by publishing worldwide. Sign In or Create an Account. Sign In. Advanced Search. Search Menu. Article Navigation. Close mobile search navigation Article Navigation. Volume Article Contents Abstract.
Characteristics of pyrosequencing data—enabling realistic simulation with flowsim. Oxford Academic. Animesh Sharma. Inge Jonassen. Select Format Select format. Permissions Icon Permissions. Abstract Motivation: The commercial launch of pyrosequencing in was a milestone in genome sequencing in terms of performance and cost.
Table 1. Data basis for building the empirical distributions. SFF files. Escherichia coli. Dicentrarchus labrax. Number of reads a 1 1 2 Average read length a Open in new tab. Open in new tab Download slide. Massively parallel pyrosequencing has been widely used in studies with these purposes. One of the main focuses of these studies has been to define a core microbiome across individuals The core microbiome can be understood as those microbial taxa but also genes that are shared by all or the great majority of humans.
Some authors have even used this term to mean the taxa present in most individuals. Most studies have focused exclusively on a given site and will be reviewed in the next section. In a comprehensive study that compared the human microbiome from diverse sites, Costello et al. Sampled sites included the oral cavity, gut, and skin surfaces. Overall, the detected taxa comprised representatives of 22 bacterial phyla.
Each body site was found to harbor a unique microbiota and a group of dominant taxa that remained stable over time. Low-abundance taxa varied significantly. A high interindividual variability was found within sites. The gut contains the largest number of microorganisms associated with the human body and is regarded as one of the most densely populated microbial ecosystems on this planet 66 , In a study by Turnbaugh et al. Findings were also compared to communities from the gut and other body sites of related and unrelated individuals.
The total diversity of species-level bacterial phylotypes in the reads obtained from each twin was about Most species-level phylotypes were present at low abundance. A comparison across 27 body habitats demonstrated high levels of diversity. The combined data from the 27 body sites revealed an estimated 4, species-level phylotypes. A study of rural children in Burkina Faso and in Italy analyzed approximately , sequences from the V5—V6 regions of the 16S rRNA gene and showed that the Bacteroidetes phylum was far more abundant in microbiomes of African children, with a unique abundance of bacteria from the genus Prevotella and Xylanibacter Most Bacteroidetes representatives detected in the African children are known to have genes for cellulose and xylan hydrolysis, indicating that they are possibly involved in obtaining energy from the plant-rich diet.
Moreover, Enterobacteriaceae taxa were significantly underrepresented in African children when compared to Italian children. These findings pointed to the fact that microbiomes vary geographically with their hosts, and diet may be one factor involved with such a variation. The microbial community structures of individuals with normal weight, morbidly obese, or postgastric-bypass surgery were investigated in a study using pyrosequencing Analysis of approximately , sequences spanning the V6 region of the 16S rRNA gene demonstrated that members of the Firmicutes phylum were dominant in normal-weight and obese individuals, but significantly decreased in postgastric-bypass individuals.
The latter had a proportional increase in Gammaproteobacteria. Prevotellaceae were highly enriched in the obese individuals. Three other families, namely Coriobacteriaceae phylum Actinobacteria , Erysipelotrichaceae phylum Firmicutes , and Alcaligenaceae phylum Proteobacteria , were also more abundant in obese subjects. Verrucomicrobia were generally rare in obeses but abundant in the other individuals.
The main conclusions of this study were that despite interindividual differences, obesity and gastric-bypass clearly affected the intestinal microbiome. In an analysis of the influence of host genotype, environmental exposure, and host adiposity on the gut microbiome, Turnbaugh et al. The authors analyzed about 10, near full-length and 2 million partial V2 and V6 regions 16S rRNA gene sequences as well as more than 2 Gb from the microbiomes of individuals.
The gut microbial community of each individual varied in the specific bacterial taxa detected. One interesting finding from this study is that a core microbiome at the species level was not observed.
Instead, a wide array of microbial genes was shared among individuals, comprising a core microbiome at a functional level. Obese individuals were associated with changes in the microbiota at the phylum level and a significant decrease in diversity. The composition of the gut microbiota in type-2 diabetic individuals was compared to non-diabetic individuals controls by a study using pyrosequencing targeting the V4 region of the 16S rRNA gene Analysis of nearly , sequences showed that the proportion of members of the Firmicutes phylum was significantly higher in the controls when compared with diabetics.
Class Betaproteobacteria was highly enriched in diabetics compared to non-diabetic subjects and positively correlated with plasma glucose. The authors concluded that type-2 diabetes may be associated with changes in the gut microbiome composition, especially at the phylum and class levels. Finegold et al. At the phylum level, members of Bacteroidetes were found at high levels in the severely autistic group, whereas members of Firmicutes were dominant in controls.
At the species level, Desulfovibrio species and Bacteroides vulgatus occurred in significantly higher numbers in severely autistic children than in controls. Higher bacterial diversity was disclosed in the feces of autistic individuals when compared with controls. The authors emphasized that it remains uncertain whether autism leads to changes in the gut microbiota or the changed microbiota exerts any influence on the disease or its syndromes. Some studies have used pyrosequencing to investigate the impact of systemic antibiotic therapy on the gut microbiome.
Dethlefsen et al. Ciprofloxacin treatment influenced the abundance of about one-third of the bacterial taxa in the gut, decreasing the community diversity. The magnitude of this effect varied among individuals. Despite disturbance, the community membership had largely returned to the pretreatment state within 4 weeks, which is suggestive of community resilience.
However, several taxa failed to recover within 6 months. In a further study, Dethlefsen and Relman 63 examined the distal gut microbiota of individuals over 10 months, which included two courses of therapy with ciprofloxacin. Analysis of more than 1. One week following the end of each antibiotic course, the bacterial communities started to return to their initial state.
However, return was frequently incomplete. In all individuals, the composition of the gut microbiota was stabilized by the end of the study, even though it was altered from its initial state. The short- and long-term effects of clarithromycin and metronidazole treatment on the microbiota of the lower intestine and throat were investigated by Jakobsson et al.
While the microbial communities of untreated control subjects were relatively stable over time, a decreased diversity of the gut and throat microbiomes was apparent immediately after the antibiotic therapy. A dramatic decline in Actinobacteria in both feces and throat was observed immediately after treatment. Although the bacterial diversity subsequently returned to the pretreatment states, perturbation remained in some cases for up to 4 years following the antibiotic therapy.
In conclusion, the authors reported that a common 1-week antibiotic treatment regimen with clarithromycin and metronidazole resulted in a long-term impact in both throat and gut microbiomes. Considering the whole area available for colonization, the skin is certainly one of the largest microbial habitats associated with the human body. In the study by Costello et al. Separate clusters were evident for sites on the trunk and legs, which were dominated by Staphylococcus species armpits and soles of the feet or Corynebacterium species navel and back of the knees.
The highest levels of bacterial diversity were observed in the forearms, palms, index fingers, back of the knees, and soles of the feet. The bacterial communities on the palm surfaces of both the dominant and non-dominant hands of undergraduate students were assessed using pyrosequencing Analysis of more than , sequences spanning the V1—V2 regions of the 16S rRNA gene revealed a high bacterial diversity that was higher in women than in men.
A typical palm hand surface harbored more than unique species-level taxa. A total of 4, unique taxa were detected across all of the hands examined. The most abundant genera on nearly all palm surfaces were Propionibacterium , Streptococcus , Staphylococcus , and Corynebacterium. Even though a core microbiome was observed on the palm surface, pronounced intra- and interindividual variations in the bacterial community composition were reported. Another study from the same group 57 revealed the stability and uniqueness of the skin microbiome among a group of individuals tested for their use of computer keyboards and mouse.
The discrimination between individuals was stronger with the unweighted UniFrac metric than with the weighted metric, suggesting that differences in community membership rather than community structure discriminated best among individuals.
The vagina is known to harbor a resident microbiota that is important for maintaining vaginal health and preventing infections such as bacterial vaginosis, yeast infections, urinary tract infections, and sexually transmitted diseases. In the first study to use pyrosequencing for the analysis of the vaginal microbiome, Sundquist et al. They also detected a significant presence of other genera, including Psychrobacter , Magnetobacterium , Prevotella , Bifidobacterium , and Veillonella.
Another study compared the diversity of the vaginal microbiome in women with bacterial vaginosis as compared to healthy women from China using several culture-independent methods, including pyrosequencing Analysis of ca. Overall, the most predominant bacterial phyla were Firmicutes, Bacteroidetes, Actinobacteria, and Fusobacteria. Of these, Firmicutes was the most dominant in healthy individuals, whereas Firmicutes, Bacteroidetes, Actinobacteria, and Fusobacteria constituted the complex vaginal microbiota in the vaginosis group.
The vaginal bacterial diversity in women with vaginosis was remarkably higher when compared with the healthy controls. The vaginal microbiome of asymptomatic North American women from four ethnic groups white, black, Hispanic, and Asian was evaluated by pyrosequencing of V1—V2 regions of the 16S rRNA gene Data consisting of about , sequences with an average length of bp revealed that the vaginal bacterial communities clustered into five groups. Four were dominated by Lactobacillus iners , Lactobacillus crispatus , Lactobacillus gasseri , or Lactobacillus jensenii.
The fifth one had fewer lactobacilli and higher proportions of anaerobic bacteria, including Prevotella, Dialister, Atopobium, Gardnerella, Megasphaera, Peptoniphilus, Sneathia, Eggerthella , and Finegoldia. Interestingly, all communities contained members known to produce lactic acid, including Lactobacillus, Streptococcus, Megasphaera , and Atopobium. This study found no single core microbiome for the human vagina.
Charlson et al. Analysis of nearly , sequences encompassing the V1—V2 regions demonstrated that the microbiota from smokers was significantly more diverse than non-smokers and clustered separately.
The distributions of several genera were systematically altered by smoking in both the nasopharynx and oropharynx, and there was an enrichment of obligate anaerobes in the oropharynx. The authors concluded that distinct regions of the human upper respiratory tract contain typical microbial communities, with altered patterns observed in smokers.
These have the potential to influence the prevalence of respiratory tract complications in these individuals. Biofilm infections have been considered as the primary impediment to the healing of chronic wounds.
Dowd et al. Obligate anaerobes, including members from the genera Bacteroides, Peptoniphilus, Finegoldia, Anaerococcus , and Peptostreptococcus , also comprised a significant portion of the wound biofilm communities. Other major components of the bacterial communities included Streptococcus , Serratia , Staphylococcus , and Enterococcus. In another study, the same group 84 surveyed samples from three types of chronic wound types: diabetic foot ulcers, venous leg ulcers, and pressure ulcers.
Analysis of , pyrosequencing reads spanning the V4—V6 regions disclosed specific major bacterial communities that were evident in the biofilms of all chronic wound types, including Staphylococcus, Pseudomonas, Peptoniphilus, Enterobacter, Stenotrophomonas, Finegoldia , and Serratia. Specifically, the predominant bacterial taxa in venous leg ulcer biofilms were Enterobacter, Serratia, Stenotrophomonas , and Proteus species.
In diabetic foot ulcers, the predominant genera were Staphylococcus, Peptoniphilus, Rhodopseudomonas, Enterococcus, Veillonella, Clostridium , and Finegoldia. Pressure ulcer biofilms were dominated by species from the genera Peptoniphilus, Serratia, Peptococcus, Streptococcus , and Finegoldia.
Each of the wound types revealed marked differences in bacterial community structure. In another study evaluating samples from venous leg ulcers, Wolcott et al. Predominant taxa included a previously uncharacterized bacteroidales, various anaerobes, Staphylococcus , Corynebacterium , and Serratia. Smith et al. Analysis of nearly , sequences longer than bp and including the V1—V3 regions demonstrated that the biofilm associated with decubitus ulcers was polymicrobial in nature and characterized by a great interindividual variability.
A total of genera and predicted species were identified from 49 wounds. Several genera and species were found in high percentage in individual wounds. Species that predominated in several cases were Corynebacterium striatum , Streptococcus agalactiae , Pseudomonas aeruginosa , Anaerococcus species, Serratia marcescens , Staphylococcus aureus , and Enterococcus faecalis. Several anaerobic species were detected.
Several studies have used the pyrosequencing technology to successfully detect pathogens associated with neonatal sepsis 87 , brain abscess 88 , and diarrhea A study 90 described the incorporation of pyrosequencing for routine pathogen identification of atypical isolates in a children's hospital.
The authors concluded that coupled with culture, pyrosequencing-based bacterial identification can be a valuable tool for improved bacterial identification in a pediatric hospital setting.
A number of laboratories are currently working on analyzing the oral microbiome in health and disease. Traditional research questions such as the comparison between the oral microbiomes in healthy and diseased sites in the same oral cavity, in healthy and diseased patients, in diseased patients following treatment procedures, and in patients with normal health and those with systemic diseases such as diabetes mellitus and atherosclerosis are being explored using the pyrosequencing technology.
The intent is to gain better understanding of the pathogenesis of oral microbial disease, of virulence determinants that lead to extensive disease, and lack of response to therapy or the interrelationship of oral and systemic diseases.
Using GS FLX pyrosequencing, an analysis of 71 saliva and 98 supragingival samples revealed a total of 28, unique V6 tag sequences from 22 known phyla However, Despite this high number of OTUs, the bacterial richness estimated from rarefaction curves indicated that the richness is incomplete, that is, these numbers did not reveal all the bacterial taxa present. Further analysis by the same group of children of various ages revealed that in deciduous teeth dentition, Proteobacteria were prominent, but with a change to permanent dentition, Bacteroidetes, the Veillonellaceae family, Spirochaetes, and candidate division TM7 increased Within an individual oral cavity, over 3, sequences comprising over species-level phylotypes and 88— higher taxa genus level or above were found.
The abundant phylotypes and relative abundance in the three oral microbiomes are shown in Fig. Shared abundant phylotypes in three oral microbiomes and their relative abundance. Relative abundance of shared phylotypes within an individual microbiome. Only abundant phylotypes that contributed to at least 0.
The highest taxon in most cases, genus at which the OTU was identified is shown together with the cluster identification number. Different colors indicate three different microbiomes, S1, S2, and S3, respectively. Figure reproduced with permission from Zaura et al. Pyrosequencing combined with UniFrac analysis has also shown that the oral microbiome is relatively stable within the same individual, in three samples collected over 1 month, allowing for subject-specific grouping This finding could potentially lead to future uses of salivary samples in forensic identification of individuals, as was noted with skin samples before.
These findings are typical of the characteristics of complex microbial populations, as would be expected in the oral cavity. Examining microbial niches that are more isolated and localized may reveal less complexity and allow more effective comparisons among patients with different presentations of disease. Therefore, a pyrosequencing analysis was performed recently of microorganisms in seven primary and persistent endodontic infections.
Endodontic infections would be expected to be less complex than microbiomes in saliva or plaque Pyrosequencing yielded a fold increase in depth of coverage compared to Sanger sequencing. Using the RDP-II Classifier, analysis at different taxonomic ranks showed that Sanger sequencing and pyrosequencing yielded 8 versus 13 phyla, 10 versus 22 classes, 11 versus 43 orders, 20 versus 97 families, and 25 versus genera, respectively.
These results showed that even in the relatively isolated root canal environment, significant microbial diversity exists, and that pyrosequencing allowed much better characterization of the endodontic microbiome than did traditional Sanger sequencing. These findings for endodontic infections were confirmed and expanded by another study using pyrosequencing, this one investigating the bacterial diversity of the communities established specifically in the apical root canal DNA extracts from cryopulverized apical root segments of 10 extracted teeth with primary apical periodontitis were subjected to multiplex tag-encoded GS FLX Titanium pyrosequencing.
The most represented, abundant, and prevalent phyla were Proteobacteria, Firmicutes, Bacteroidetes, Fusobacteria, and Actinobacteria.
The majority of species-level phylotypes occurred at low levels. The mean number of species-level phylotypes per sample was 37, ranging from 13 to A great interindividual variation in the composition of the apical microbiota was evident. In another study of the endodontic microbiota using pyrosequencing, Santos et al.
Members of Fusobacteria were much more prevalent in acute than in chronic cases. Twenty genera were exclusively detected in acute infections and 18 in chronic infections. Acute infections were significantly more diverse than chronic infections. Although a high interindividual variation in bacterial communities was observed, many samples tended to group together according to the type of infection acute or chronic , which may suggest the existence of patterns related to disease severity.
The root canal environment is normally sterile, and therefore, the use of this highly sensitive and powerful technology to identify microbial resistance to endodontic treatment modalities is likely to be very important in future, particularly with the growing need to reach a high degree of root canal disinfection in preparation for regenerative endodontic procedures A comparison was recently reported between microbial analysis of oral samples using pyrosequencing and targeted identification by DNA microarray with the human oral microbe identification microarray HOMIM Oral lavage samples were collected from 20 individuals.
Concordance by the two methods and correlations were high for common genera Streptococcus , Veillonella , Leptotrichia , Prevotella , and Haemophilus.
This again shows that the data revealed by pyrosequencing add depth of coverage and better definition of diversity of the sample compared to other microbial analysis methods. It is salient to point out that although pyrosequencing revealed more taxa, HOMIM identifies taxa at the species level and specifically those for which there are probes.
In the realm of the relationship of oral and systemic disease, a recent study used pyrosequencing to examine the relationship between oral periodontal swab , gut, and atherosclerotic plaque samples in 15 atherosclerosis and 15 control patients Unweighted UniFrac analysis revealed strong clustering by body site indicating distinct overall microbial communities in the three locations.
Although the OTU analysis did not show differences between the oral samples in atherosclerotic and control individuals, OTUs from three genera were found to be in the same consensus lineage between oral and atheroma samples: Veillonella 7 OTUs from 11 patients , Streptococcus 17 OTUs from 6 patients , and Propionibacterium 4 OTUs from 8 patients. As discussed before, the oral metagenome can be analyzed beyond the 16S rRNA gene data.
This has been carried out using both the Roche pyrosequencing and Illumina GA iiX platforms to pooled plaque samples of volunteers The authors concluded that using the 31 phylogenetic marker genes for community profiling may yield more accurate estimates than 16S rRNA-based assays because of the presence of a single copy for each marker per microbial genome. Finally, the oral fungal microbiome mycobiome was investigated in 20 healthy individuals by pyrosequencing The oral fungal microbiome was found to be diverse as revealed by the presence of 74 culturable and 11 non-culturable fungal genera.
The advent of massively parallel pyrosequencing has allowed the development of large population-based studies of the microbiome of diverse human body sites. The results brought about by these studies have significantly contributed to refining and augmenting the knowledge of the community membership and structure in and on the human body in healthy and diseased conditions.
As most of the oral infectious diseases are now regarded as biofilm-related polymicrobial infections, these high-throughput technologies have the potential to revolutionize our knowledge of the oral microbiome in the sense that specific patterns related to health or disease may be identified.
This can have important implications in terms of accurate diagnosis and more effective preventive and therapeutic measures. Additionally, these methods can be used for metagenomic analyses, permitting not only to identify the microbial species present but also to screen their genomes for functional roles and pathogenic potential in the communities.
Despite the limitations of pyrosequencing, namely the cost, need for large amounts of DNA, complexity of analysis, and relatively short reads, the increasing availability of massive computing power and the efficiency of data generation and analysis are likely to render this technology a major player in the field of microbiology in the near future. There is an obvious tendency for still greater advances and decreased costs. Therefore, one may envision the possibility of NGS technologies being established as the main diagnostic means for microbial infections in clinical laboratories.
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