Evaluation of in-field Nanopore sequencing of 16S rRNA genes and metagenomes using mock communities
Ontology highlight
ABSTRACT:
PROVIDER: PRJEB30868 | ENA |
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| ERR3078230.fastq.gz | Fastqsanger.gz | |||
| ERR3078240.fastq.gz | Fastqsanger.gz | |||
| ERR3078231.fastq.gz | Fastqsanger.gz | |||
| ERR3078241.fastq.gz | Fastqsanger.gz | |||
| ERR3078232.fastq.gz | Fastqsanger.gz |
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Project description:Taxonomy identification is fundamental to all microbiology studies. Particularly in metagenomics, which identify the composition of microorganisms using thousands of sequences, its importance is even greater. Identification is inevitably affected by the choice of database. This study was conducted to evaluate the accuracy of three widely used 16S databases, Greengenes, Silva, and EzBioCloud, and to suggest basic guidelines for selecting reference databases. Using public mock community data, each database was used to assign taxonomy and to test its accuracy. We showed that EzBioCloud performs well compared to other existing databases.
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Project description:16S rRNA Gene Amplicon based metagenomes of microbial soil communities in the Antarctic Peninsula
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Project description:While 16S rRNA PCR-Sanger sequencing has paved the way for the diagnosis of culture-negative bacterial infections, it does not provide the composition of polymicrobial infections. We aimed to evaluate the performance of the Nanopore-based 16S rRNA metagenomic approach, using both partial and full-length amplification of the gene, and to explore its feasibility and suitability as a routine diagnostic tool for bacterial infections in a clinical laboratory. Thirty-one culture-negative clinical samples from mono- and polymicrobial infections based on Sanger-sequencing results were sequenced on MinION using both the in-house partial amplification and the Nanopore dedicated kit for the full-length amplification of the 16S rRNA gene. Contamination, background noise definition, bacterial identification, and time-effectiveness issues were addressed. Cost optimization was also investigated with the miniaturized version of the flow cell (Flongle). The partial 16S approach had a greater sensitivity compared to the full-length kit that detected bacterial DNA in only 24/31 (77.4%) samples. Setting a threshold of 1% of total reads overcame the background noise issue and eased the interpretation of clinical samples. Results were obtained within 1 day, discriminated polymicrobial samples, and gave accurate bacterial identifications compared to Sanger-based results. We also found that multiplexing and using Flongle flow cells was a cost-effective option. The results confirm that Nanopore technology is user-friendly as well as cost- and time-effective. They also indicate that 16S rRNA targeted metagenomics is a suitable approach to be implemented for the routine diagnosis of culture-negative samples in clinical laboratories.
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Project description:Nanopore sequencing of mock and swine fecal communities
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Project description:Nanopore sequencing is rapidly becoming more popular for use in various microbiota-based applications. Major limitations of current approaches are that they do not enable de novo species identification and that they cannot be used to verify species assignments. This severely limits applicability of the nanopore sequencing technology in taxonomic applications. Here, we demonstrate the possibility of de novo species identification and verification using hexamer frequencies in combination with k-means clustering for nanopore sequencing data. The approach was tested on the human infant gut microbiota of 3-month-old infants. Using the hexamer k-means approach we identified two new low abundant species associated with vaginal delivery. In addition, we confirmed both the vaginal delivery association for two previously identified species and the overall high levels of bifidobacteria. Taxonomic assignments were further verified by mock community analyses. Therefore, we believe our de novo species identification approach will have widespread application in analyzing microbial communities in the future.
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Project description:ObjectivesThere is much speculation on which hypervariable region provides the highest bacterial specificity in 16S rRNA sequencing. The optimum solution to prevent bias and to obtain a comprehensive view of complex bacterial communities would be to sequence the entire 16S rRNA gene; however, this is not possible with second generation standard library design and short-read next-generation sequencing technology.MethodsThis paper examines a new process using seven hypervariable or V regions of the 16S rRNA (six amplicons: V2, V3, V4, V6-7, V8, and V9) processed simultaneously on the Ion Torrent Personal Genome Machine (Life Technologies, Grand Island, NY). Four mock samples were amplified using the 16S Ion Metagenomics Kit™ (Life Technologies) and their sequencing data is subjected to a novel analytical pipeline.ResultsResults are presented at family and genus level. The Kullback-Leibler divergence (DKL), a measure of the departure of the computed from the nominal bacterial distribution in the mock samples, was used to infer which region performed best at the family and genus levels. Three different hypervariable regions, V2, V4, and V6-7, produced the lowest divergence compared to the known mock sample. The V9 region gave the highest (worst) average DKL while the V4 gave the lowest (best) average DKL. In addition to having a high DKL, the V9 region in both the forward and reverse directions performed the worst finding only 17% and 53% of the known family level and 12% and 47% of the genus level bacteria, while results from the forward and reverse V4 region identified all 17 family level bacteria.ConclusionsThe results of our analysis have shown that our sequencing methods using 6 hypervariable regions of the 16S rRNA and subsequent analysis is valid. This method also allowed for the assessment of how well each of the variable regions might perform simultaneously. Our findings will provide the basis for future work intended to assess microbial abundance at different time points throughout a clinical protocol.
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