Illumina MiSeq 16S rRNA gene profiling sequences from drinking water systems
Ontology highlight
ABSTRACT:
PROVIDER: PRJNA529765 | ENA |
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| SRR8870400_1.fastq.gz | Fastqsanger.gz | |||
| SRR8870400_2.fastq.gz | Fastqsanger.gz | |||
| SRR8870410_1.fastq.gz | Fastqsanger.gz | |||
| SRR8870410_2.fastq.gz | Fastqsanger.gz | |||
| SRR8870420_1.fastq.gz | Fastqsanger.gz |
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Project description:BackgroundTo take advantage of affordable high-throughput next-generation sequencing technologies to characterize microbial community composition often requires the development of improved methods to overcome technical limitations inherent to the sequencing platforms. Sequencing low sequence diversity libraries such as 16S rRNA amplicons has been problematic on the Illumina MiSeq platform and often generates sequences of suboptimal quality.ResultsHere we present an improved dual-indexing amplification and sequencing approach to assess the composition of microbial communities from clinical samples using the V3-V4 region of the 16S rRNA gene on the Illumina MiSeq platform. We introduced a 0 to 7 bp "heterogeneity spacer" to the index sequence that allows an equal proportion of samples to be sequenced out of phase.ConclusionsOur approach yields high quality sequence data from 16S rRNA gene amplicons using both 250 bp and 300 bp paired-end MiSeq protocols and provides a flexible and cost-effective sequencing option.
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Project description:BACKGROUND:The bacterial 16S rRNA gene has historically been used in defining bacterial taxonomy and phylogeny. However, there are currently no high-throughput methods to sequence full-length 16S rRNA genes present in a sample with precision. RESULTS:We describe a method for sequencing near full-length 16S rRNA gene amplicons using the high throughput Illumina MiSeq platform and test it using DNA from human skin swab samples. Proof of principle of the approach is demonstrated, with the generation of 1,604 sequences greater than 1,300 nt from a single Nano MiSeq run, with accuracy estimated to be 100-fold higher than standard Illumina reads. The reads were chimera filtered using information from a single molecule dual tagging scheme that boosts the signal available for chimera detection. CONCLUSIONS:This method could be scaled up to generate many thousands of sequences per MiSeq run and could be applied to other sequencing platforms. This has great potential for populating databases with high quality, near full-length 16S rRNA gene sequences from under-represented taxa and environments and facilitates analyses of microbial communities at higher resolution.
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Project description:The development of high-throughput sequencing technologies has revolutionized the field of microbial ecology via the sequencing of phylogenetic marker genes (e.g. 16S rRNA gene amplicon sequencing). Denoising, the removal of sequencing errors, is an important step in preprocessing amplicon sequencing data. The increasing popularity of the Illumina MiSeq platform for these applications requires the development of appropriate denoising methods.The newly proposed denoising algorithm IPED includes a machine learning method which predicts potentially erroneous positions in sequencing reads based on a combination of quality metrics. Subsequently, this information is used to group those error-containing reads with correct reads, resulting in error-free consensus reads. This is achieved by masking potentially erroneous positions during this clustering step. Compared to the second best algorithm available, IPED detects double the amount of errors. Reducing the error rate had a positive effect on the clustering of reads in operational taxonomic units, with an almost perfect correspondence between the number of clusters and the theoretical number of species present in the mock communities.Our algorithm IPED is a powerful denoising tool for correcting sequencing errors in Illumina MiSeq 16S rRNA gene amplicon sequencing data. Apart from significantly reducing the error rate of the sequencing reads, it has also a beneficial effect on their clustering into operational taxonomic units. IPED is freely available at http://science.sckcen.be/en/Institutes/EHS/MCB/MIC/Bioinformatics/ .
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Project description:Flow cytometry (FCM) and 16S rRNA gene sequencing data are commonly used to monitor and characterize microbial differences in drinking water distribution systems. In this study, to assess microbial differences in drinking water distribution systems, 12 water samples from different sources water (groundwater, GW; surface water, SW) were analyzed by FCM, heterotrophic plate count (HPC), and 16S rRNA gene sequencing. FCM intact cell concentrations varied from 2.2 × 10(3) cells/mL to 1.6 × 10(4) cells/mL in the network. Characteristics of each water sample were also observed by FCM fluorescence fingerprint analysis. 16S rRNA gene sequencing showed that Proteobacteria (76.9-42.3%) or Cyanobacteria (42.0-3.1%) was most abundant among samples. Proteobacteria were abundant in samples containing chlorine, indicating resistance to disinfection. Interestingly, Mycobacterium, Corynebacterium, and Pseudomonas, were detected in drinking water distribution systems. There was no evidence that these microorganisms represented a health concern through water consumption by the general population. However, they provided a health risk for special crowd, such as the elderly or infants, patients with burns and immune-compromised people exposed by drinking. The combined use of FCM to detect total bacteria concentrations and sequencing to determine the relative abundance of pathogenic bacteria resulted in the quantitative evaluation of drinking water distribution systems. Knowledge regarding the concentration of opportunistic pathogenic bacteria will be particularly useful for epidemiological studies.
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Project description:In this meta-analysis, 17 rumen epithelial 16S rRNA gene Illumina MiSeq amplicon sequencing data sets were analyzed to identify a core rumen epithelial microbiota and core rumen epithelial OTUs shared between the different studies included. Sequences were quality-filtered and screened for chimeric sequences before performing closed-reference 97% OTU clustering, and de novo 97% OTU clustering. Closed-reference OTU clustering identified the core rumen epithelial OTUs, defined as any OTU present in ≥ 80% of the samples, while the de novo data was randomly subsampled to 10,000 reads per sample to generate phylum- and genus-level distributions and beta diversity metrics. 57 core rumen epithelial OTUs were identified including metabolically important taxa such as Ruminococcus, Butyrivibrio, and other Lachnospiraceae, as well as sulfate-reducing bacteria Desulfobulbus and Desulfovibrio. Two Betaproteobacteria OTUs (Neisseriaceae and Burkholderiaceae) were core rumen epithelial OTUs, in contrast to rumen content where previous literature indicates they are rarely found. Two core OTUs were identified as the methanogenic archaea Methanobrevibacter and Methanomethylophilaceae. These core OTUs are consistently present across the many variables between studies which include different host species, geographic region, diet, age, farm management practice, time of year, hypervariable region sequenced, and more. When considering only cattle samples, the number of core rumen epithelial OTUs expands to 147, highlighting the increased similarity within host species despite geographical location and other variables. De novo OTU clustering revealed highly similar rumen epithelial communities, predominated by Firmicutes, Bacteroidetes, and Proteobacteria at the phylum level which comprised 79.7% of subsampled sequences. The 15 most abundant genera represented an average of 54.5% of sequences in each individual study. These abundant taxa broadly overlap with the core rumen epithelial OTUs, with the exception of Prevotellaceae which were abundant, but not identified within the core OTUs. Our results describe the core and abundant bacteria found in the rumen epithelial environment and will serve as a basis to better understand the composition and function of rumen epithelial communities.
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Project description:There is a growing awareness of the importance of indoor microbiomes for human health. Given their complexity, these microbiomes can only be adequately surveyed using high throughput sequencing techniques. Oxford Nanopore's MinION is the newest third generation sequencing technology on the market. With its many advantages such as portability, user friendliness, simplicity, speed of sequencing and long read length, the technology is now an actual contender to established sequencing platforms. MinION's main disadvantage is a relatively low read accuracy compared to several other platforms, although this is constantly improving. The present study, which appears to be the first of its kind, provides the results of a preliminary analysis of the microbial communities in indoor environments based on 16S rRNA gene amplicon sequencing, using both the Oxford Nanopore Technologies (ONT) MinIOn and the Illumina MiSeq DNA sequencers. At the level of family and above, there was no significant difference between the microbial compositions as revealed by the two platforms. However, at the genus, and particularly at the species level, the ONT MinION reported greater taxonomic resolution than Illumina MiSeq.
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