16S rRNA gene amplicon sequencing of drinking water.
Ontology highlight
ABSTRACT:
PROVIDER: PRJEB26658 | ENA |
REPOSITORIES: ENA
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Project description:Contaminated aquifer (Dusseldorf-Flinger, Germany) templates extracted from 5 sediment depths ranging between 6.4 and 8.4 m below ground and over 3 years of sampling were amplified for amplicon pyrosequencing using the primers Ba27f (5’-aga gtt tga tcm tgg ctc ag-3’) and Ba519r (5’- tat tac cgc ggc kgc tg-3’), extended as amplicon fusion primers with respective primer A or B adapters, key sequence and multiplex identifiers (MID) as recommended by 454/Roche. Amplicons were purified and pooled as specified by the manufacturer. Emulsion PCR (emPCR), purification of DNA-enriched beads and sequencing run were performed following protocols and using a 2nd generation pyrosequencer (454 GS FLX Titanium, Roche) as recommended by the developer. Quality filtering of the pyrosequencing reads was performed using the automatic amplicon pipeline of the GS Run Processor (Roche), with a slight modification concerning the valley filter (vfScanAllFlows false instead of TiOnly) to extract the sequences. Demultiplexed raw reads were furhter trimmed for quality and lenght (>250 bp).
2012-11-11 | GSE25345 | GEO
Project description:Contaminated aquifer (Dusseldorf-Flinger, Germany) templates extracted from 5 sediment depths ranging between 6.4 and 8.4 m below ground and over 3 years of sampling were amplified for amplicon pyrosequencing using the primers Ba27f (5’-aga gtt tga tcm tgg ctc ag-3’) and Ba519r (5’- tat tac cgc ggc kgc tg-3’), extended as amplicon fusion primers with respective primer A or B adapters, key sequence and multiplex identifiers (MID) as recommended by 454/Roche. Amplicons were purified and pooled as specified by the manufacturer. Emulsion PCR (emPCR), purification of DNA-enriched beads and sequencing run were performed following protocols and using a 2nd generation pyrosequencer (454 GS FLX Titanium, Roche) as recommended by the developer. Quality filtering of the pyrosequencing reads was performed using the automatic amplicon pipeline of the GS Run Processor (Roche), with a slight modification concerning the valley filter (vfScanAllFlows false instead of TiOnly) to extract the sequences. Demultiplexed raw reads were furhter trimmed for quality and lenght (>250 bp). 15 samples examined in total from important plume zones of the aquifer sampled in Feb. 2006, Sep. 2008 and Jun. 2009 (5 every year of sampling).
2012-11-11 | E-GEOD-25345 | biostudies-arrayexpress
Project description:Microbial amplicon sequencing studies are an important tool in biological and biomedical research. Widespread 16S rRNA gene microbial surveys have shed light on the structure of many ecosystems inhabited by bacteria, including the human body. However, specialized software and algorithms are needed to convert raw sequencing data into biologically meaningful information (i.e. tables of bacterial counts). While different bioinformatic pipelines are available in a rapidly changing and improving field, users are often unaware of limitations and biases associated with individual pipelines and there is a lack of agreement regarding best practices. Here, we compared six bioinformatic pipelines for the analysis of amplicon sequence data: three OTU-level flows (QIIME-uclust, MOTHUR, and USEARCH-UPARSE) and three ASV-level (DADA2, Qiime2-Deblur, and USEARCH-UNOISE3). We tested workflows with different quality control options, clustering algorithms, and cutoff parameters on a mock community as well as on a large (N = 2170) recently published fecal sample dataset from the multi-ethnic HELIUS study. We assessed the sensitivity, specificity, and degree of consensus of the different outputs. DADA2 offered the best sensitivity, at the expense of decreased specificity compared to USEARCH-UNOISE3 and Qiime2-Deblur. USEARCH-UNOISE3 showed the best balance between resolution and specificity. OTU-level USEARCH-UPARSE and MOTHUR performed well, but with lower specificity than ASV-level pipelines. QIIME-uclust produced large number of spurious OTUs as well as inflated alpha-diversity measures and should be avoided in future studies. This study provides guidance for researchers using amplicon sequencing to gain biological insights.
| S-EPMC6964864 | biostudies-literature
Project description:Background16S rRNA amplicon sequencing is an accurate method of detecting microbial infection without culture. It is unclear if sequencing has additional benefits over routine diagnostic methods for Helicobacter pylori testing.MethodsWe enrolled Mongolian volunteers with dyspepsia. Using routine diagnostic methods, positive H. pylori was defined as positive results on histology/immunohistochemistry, culture, rapid urease test, or serology; negative H. pylori was defined by negative results from all these tests. We performed 16S rRNA sequencing on gastric biopsy specimens and calculated cutoffs for operational taxonomic units (OTUs) and relative abundance (RA) to define positive results using ROC curves.ResultsWe examined 161 individuals with a mean age of 43.6 years, and 64.6% were women. Using routine diagnostic methods, 122 (75.8%) participants were H. pylori positive, the sensitivity and specificity for 16S rRNA sequencing were 94.3% and 82.1% or 93.4% and 82.1% when cutoff values were set to 1113 (OTU number) or 4.4% RA, respectively (both p < .001). When combining the validated values, the concordance rate was high (91.1%); however, 16S rRNA sequencing had additional positive yield in 9 cases (5.6%) compared with routine diagnostic methods, and much greater additional positive yield compared to histopathology/IHC, culture, RUT, serology separately with 12 (7.4%), 37 (23.0%) and 43 (26.7%).Conclusion16S rRNA amplicon sequencing detects potentially important proportion of H. pylori-positive cases that test negative with routine diagnostic methods. The quantitative number of H. pylori can help to understand how it can be changing by diseases and RA give opportunity to understand how H. pylori communicate with other microbiota.
| S-EPMC8122063 | biostudies-literature
Project description:The human impact on natural habitats is increasing the complexity of human-wildlife interactions and leading to the emergence of infectious diseases worldwide. Highly successful synanthropic wildlife species, such as rodents, will undoubtedly play an increasingly important role in transmitting zoonotic diseases. We investigated the potential for recent developments in 16S rRNA amplicon sequencing to facilitate the multiplexing of the large numbers of samples needed to improve our understanding of the risk of zoonotic disease transmission posed by urban rodents in West Africa. In addition to listing pathogenic bacteria in wild populations, as in other high-throughput sequencing (HTS) studies, our approach can estimate essential parameters for studies of zoonotic risk, such as prevalence and patterns of coinfection within individual hosts. However, the estimation of these parameters requires cleaning of the raw data to mitigate the biases generated by HTS methods. We present here an extensive review of these biases and of their consequences, and we propose a comprehensive trimming strategy for managing these biases. We demonstrated the application of this strategy using 711 commensal rodents, including 208 Mus musculus domesticus, 189 Rattus rattus, 93 Mastomys natalensis, and 221 Mastomys erythroleucus, collected from 24 villages in Senegal. Seven major genera of pathogenic bacteria were detected in their spleens: Borrelia, Bartonella, Mycoplasma, Ehrlichia, Rickettsia, Streptobacillus, and Orientia. Mycoplasma, Ehrlichia, Rickettsia, Streptobacillus, and Orientia have never before been detected in West African rodents. Bacterial prevalence ranged from 0% to 90% of individuals per site, depending on the bacterial taxon, rodent species, and site considered, and 26% of rodents displayed coinfection. The 16S rRNA amplicon sequencing strategy presented here has the advantage over other molecular surveillance tools of dealing with a large spectrum of bacterial pathogens without requiring assumptions about their presence in the samples. This approach is therefore particularly suitable to continuous pathogen surveillance in the context of disease-monitoring programs. IMPORTANCE Several recent public health crises have shown that the surveillance of zoonotic agents in wildlife is important to prevent pandemic risks. High-throughput sequencing (HTS) technologies are potentially useful for this surveillance, but rigorous experimental processes are required for the use of these effective tools in such epidemiological contexts. In particular, HTS introduces biases into the raw data set that might lead to incorrect interpretations. We describe here a procedure for cleaning data before estimating reliable biological parameters, such as positivity, prevalence, and coinfection, using 16S rRNA amplicon sequencing on an Illumina MiSeq platform. This procedure, applied to 711 rodents collected in West Africa, detected several zoonotic bacterial species, including some at high prevalence, despite their never before having been reported for West Africa. In the future, this approach could be adapted for the monitoring of other microbes such as protists, fungi, and even viruses.
| S-EPMC5069956 | biostudies-literature
Project description:Analysis of microbiome data involves identifying co-occurring groups of taxa associated with sample features of interest (e.g., disease state). Elucidating such relations is often difficult as microbiome data are compositional, sparse, and have high dimensionality. Also, the configuration of co-occurring taxa may represent overlapping subcommunities that contribute to sample characteristics such as host status. Preserving the configuration of co-occurring microbes rather than detecting specific indicator species is more likely to facilitate biologically meaningful interpretations. Additionally, analyses that use taxonomic relative abundances to predict the abundances of different gene functions aggregate predicted functional profiles across taxa. This precludes straightforward identification of predicted functional components associated with subsets of co-occurring taxa. We provide an approach to explore co-occurring taxa using "topics" generated via a topic model and link these topics to specific sample features (e.g., disease state). Rather than inferring predicted functional content based on overall taxonomic relative abundances, we instead focus on inference of functional content within topics, which we parse by estimating interactions between topics and pathways through a multilevel, fully Bayesian regression model. We apply our methods to three publicly available 16S amplicon sequencing datasets: an inflammatory bowel disease dataset, an oral cancer dataset, and a time-series dataset. Using our topic model approach to uncover latent structure in 16S rRNA amplicon surveys, investigators can (1) capture groups of co-occurring taxa termed topics; (2) uncover within-topic functional potential; (3) link taxa co-occurrence, gene function, and environmental/host features; and (4) explore the way in which sets of co-occurring taxa behave and evolve over time. These methods have been implemented in a freely available R package: https://cran.r-project.org/package=themetagenomics, https://github.com/EESI/themetagenomics.
| S-EPMC6905537 | biostudies-literature
Project description:Conventional wisdom holds that PCR amplification for sequencing should employ pooled replicate reactions to reduce bias due to jackpot effects and chimera formation. However, modern amplicon data analysis employs methods that may be less sensitive to such artifacts. Here we directly compare results from single versus triplicate reactions for 16S amplicon sequencing and find no significant impact of adopting a less labor-intensive single-reaction protocol.
| S-EPMC7030937 | biostudies-literature
Project description:Data on the 16S rRNA gene amplicon sequences from Pteris vittata rhizosphere soils are reported. The following phyla were recorded in arsenic-rich soils: Actinobacteria (59%), Proteobacteria (26%), Chloroflexi (17%), and Acidobacteria (9%). Actinobacteria (45%), Proteobacteria (22%), Chloroflexi (10%), and Acidobacteria (11%) were in natural-mineral soils.
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Project description:Insights into plant endosphere bacterial diversity and exploration of their bioincentives in the formulation of biofertilizers promise to avert ecological disturbances. Here, we presented the sequence dataset of the endophytic bacterial community from the roots and stems of sunflower obtained from farmlands in Itsoseng and Lichtenburg, North West Province of South Africa, using 16S rRNA gene amplicon sequencing. The climatic conditions of this region are characterized by an annual rainfall of about 600 mm and a temperature range of 3 to 21°C during winter and 22 to 34°C during summer. The genomic DNA was obtained from 1 g of each macerated sample using commercial DNA kits (DNeasy® Plant Mini kit, Qiagen, USA). The DNA was amplified through polymerase chain reaction at the V4 region using the specific forward and reverse primers. Amplicon sequencing was performed on the Miseq Illumina platform. Sequence read processing was performed using QIIME 1 16S-based pipeline implemented on Nephele microbial bioinformatics platform using default parameters. The sequence has been deposited in the Sequence Read Archive (SRA) of the National Center for Biotechnology Information (NCBI) with assigned Bioproject accession numbers. The data reveals the endophytic bacterial community structure and functions in sunflower cultivated in organic and nonorganic soils at growing and flowering stages.
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