Efficacy of a 3rd generation high-throughput sequencing platform for analyses of 16S rRNA genes from environmental samples

Mosher, Jennifer J.; Bernberg, Erin; Шевченко, О. Г.; Kan, Jinjun; Kaplan, Louis

doi:10.1016/j.mimet.2013.08.009

Cited by 109 publications

(83 citation statements)

References 32 publications

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“…Amplicons of the16S rDNA regions described previously were sequenced using P6/C4 chemistry on a PacBio RS II instrument (Pacific Biosciences, USA). The quality control for PCR amplifications and sequence preprocessing was performed by the methods described previously by Mosher et al, 2013 [24]. Raw data were processed by the protocol RS_ReadsOfinsert.1.…”

Section: Methodsmentioning

confidence: 99%

Bacterial microbiota of Kazakhstan cheese revealed by single molecule real time (SMRT) sequencing and its comparison with Belgian, Kalmykian and Italian artisanal cheeses

Zheng

et al. 2017

BMC Microbiol

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BackgroundIn Kazakhstan, traditional artisanal cheeses have a long history and are widely consumed. The unique characteristics of local artisanal cheeses are almost completely preserved. However, their microbial communities have rarely been reported. The current study firstly generated the Single Molecule, Real-Time (SMRT) sequencing bacterial diversity profiles of 6 traditional artisanal cheese samples of Kazakhstan origin, followed by comparatively analyzed the microbiota composition between the current dataset and those from cheeses originated from Belgium, Russian Republic of Kalmykia (Kalmykia) and Italy.ResultsAcross the Kazakhstan cheese samples, a total of 238 bacterial species belonging to 14 phyla and 140 genera were identified. Lactococcus lactis (28.93%), Lactobacillus helveticus (26.43%), Streptococcus thermophilus (12.18%) and Lactobacillus delbrueckii (12.15%) were the dominant bacterial species for these samples. To further evaluate the cheese bacterial diversity of Kazakhstan cheeses in comparison with those from other geographic origins, 16S rRNA datasets of 36 artisanal cheeses from Belgium, Russian Republic of Kalmykia (Kalmykia) and Italy were retrieved from public databases. The cheese bacterial microbiota communities were largely different across sample origins. By principal coordinate analysis (PCoA) and multivariate analysis of variance (MANOVA), the structure of the Kazakhstan artisanal cheese samples was found to be different from those of the other geographic origins. Furthermore, the redundancy analysis (RDA) identified 16 bacterial OTUs as the key variables responsible for such microbiota structural difference.ConclusionOur results together suggest that the diversity of bacterial communities in different groups is stratified by geographic region. This study does not only provide novel information on the bacterial microbiota of traditional artisanal cheese of Kazakhstan at species level, but also interesting insights into the bacterial diversity of artisanal cheeses of various geographical origins.Electronic supplementary materialThe online version of this article (doi:10.1186/s12866-016-0911-4) contains supplementary material, which is available to authorized users.

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Section: Methodsmentioning

confidence: 99%

Bacterial microbiota of Kazakhstan cheese revealed by single molecule real time (SMRT) sequencing and its comparison with Belgian, Kalmykian and Italian artisanal cheeses

Zheng

et al. 2017

BMC Microbiol

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“…Pyrosequencing of V1-V3 region of 16S ribosomal RNA genes followed the previous protocol (Mosher et al, 2013): 27F oligonucleotide primer was fused to the 454 Titanium sequencing primer A adapters and reverse primer 534R was fused to variable key tags for multiplexing and to the 454 Titanium sequencing primer B. Samples were denatured (95°C, 5 min), followed by 28 cycles of denaturation (95°C, 30 s), annealing (55°C, 30 s), and extension (68°C, 45 s) with a final extension (68°C, 7 min).…”

Section: Pyrosequencing and Data Processingmentioning

confidence: 99%

A snapshot on spatial and vertical distribution of bacterial communities in the eastern Indian Ocean

et al. 2016

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Besides being critical components of marine food web, microorganisms play vital roles in biogeochemical cycling of nutrients and elements in the ocean. Currently little is known about microbial population structure and their distributions in the eastern Indian Ocean. In this study, we applied molecular approaches including polymerase chain reaction-denaturant gradient gel electrophoresis (PCR-DGGE) and High-Throughput next generation sequencing to investigate bacterial 16S rRNA genes from the equatorial regions and the adjacent Bay of Bengal in the eastern Indian Ocean. In general, Bacteroidetes, Proteobacteria (mainly Alpha, and Gamma), Actinobacteria, Cyanobacteria and Planctomycetes dominated the microbial communities. Horizontally distinct spatial distribution of major microbial groups was observed from PCR-DGGE gel image analyses. However, further detailed characterization of community structures by pyrosequencing suggested a more pronounced stratified distribution pattern: Cyanobacteria and Actinobacteria were more predominant at surface water (25 m); Bacteroidetes dominated at 25 m and 150 m while Proteobacteria (mainly Alphaproteobacteria) occurred more frequently at 75 m water depth. With increasing water depth, the bacterial communities from different locations tended to share high similarity, indicating a niche partitioning for minor groups of bacteria recovered with high throughput sequencing approaches. This study provided the first "snapshot" on biodiversity and spatial distribution of Bacteria in water columns in the eastern Indian Ocean, and the findings further emphasized the potential functional roles of these microbes in energy and resource cycling in the eastern Indian Ocean. . 2016. A snapshot on spatial and vertical distribution of bacterial communities in the eastern Indian Ocean. Acta Oceanologica Sinica, 35(6): 85-93,

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“…Mosher and colleagues reported the first sequencing of full-length bacterial 16S rRNA gene amplicons from environmental samples (sediment and rock biofilm) using the PacBio RS SMRT sequencing platform with XL/C2 chemistry in comparison with the Roche 454 GS FLX chemistry [10]. The Roche 454 bacterial 16S rRNA V1V3 gene sequences could be grouped into 57 clusters (operational taxonomic units (OTUs)) at 97% sequence identity, whereas the PacBio bacterial 16S rRNA V1V3 gene sequences were grouped into 594 clusters (OTUs) at 97% sequence identity.…”

Section: Introductionmentioning

confidence: 99%

“…However, only a few studies have investigated the use of PacBio technology for the sequencing of the full-length 16S gene [10–12, 14]. Thus, more information is urgently needed to identify the advantages and disadvantages of PacBio CCS for bacterial full-length 16S rRNA gene sequencing.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of PacBio sequencing for full-length bacterial 16S rRNA gene classification

et al. 2016

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BackgroundCurrently, bacterial 16S rRNA gene analyses are based on sequencing of individual variable regions of the 16S rRNA gene (Kozich, et al Appl Environ Microbiol 79:5112–5120, 2013).This short read approach can introduce biases. Thus, full-length bacterial 16S rRNA gene sequencing is needed to reduced biases. A new alternative for full-length bacterial 16S rRNA gene sequencing is offered by PacBio single molecule, real-time (SMRT) technology. The aim of our study was to validate PacBio P6 sequencing chemistry using three approaches: 1) sequencing the full-length bacterial 16S rRNA gene from a single bacterial species Staphylococcus aureus to analyze error modes and to optimize the bioinformatics pipeline; 2) sequencing the full-length bacterial 16S rRNA gene from a pool of 50 different bacterial colonies from human stool samples to compare with full-length bacterial 16S rRNA capillary sequence; and 3) sequencing the full-length bacterial 16S rRNA genes from 11 vaginal microbiome samples and compare with in silico selected bacterial 16S rRNA V1V2 gene region and with bacterial 16S rRNA V1V2 gene regions sequenced using the Illumina MiSeq.ResultsOur optimized bioinformatics pipeline for PacBio sequence analysis was able to achieve an error rate of 0.007% on the Staphylococcus aureus full-length 16S rRNA gene. Capillary sequencing of the full-length bacterial 16S rRNA gene from the pool of 50 colonies from stool identified 40 bacterial species of which up to 80% could be identified by PacBio full-length bacterial 16S rRNA gene sequencing. Analysis of the human vaginal microbiome using the bacterial 16S rRNA V1V2 gene region on MiSeq generated 129 operational taxonomic units (OTUs) from which 70 species could be identified. For the PacBio, 36,000 sequences from over 58,000 raw reads could be assigned to a barcode, and the in silico selected bacterial 16S rRNA V1V2 gene region generated 154 OTUs grouped into 63 species, of which 62% were shared with the MiSeq dataset. The PacBio full-length bacterial 16S rRNA gene datasets generated 261 OTUs, which were grouped into 52 species, of which 54% were shared with the MiSeq dataset. Alpha diversity index reported a higher diversity in the MiSeq dataset.ConclusionThe PacBio sequencing error rate is now in the same range of the previously widely used Roche 454 sequencing platform and current MiSeq platform. Species-level microbiome analysis revealed some inconsistencies between the full-length bacterial 16S rRNA gene capillary sequencing and PacBio sequencing.Electronic supplementary materialThe online version of this article (doi:10.1186/s12866-016-0891-4) contains supplementary material, which is available to authorized users.

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Efficacy of a 3rd generation high-throughput sequencing platform for analyses of 16S rRNA genes from environmental samples

Cited by 109 publications

References 32 publications

Bacterial microbiota of Kazakhstan cheese revealed by single molecule real time (SMRT) sequencing and its comparison with Belgian, Kalmykian and Italian artisanal cheeses

Bacterial microbiota of Kazakhstan cheese revealed by single molecule real time (SMRT) sequencing and its comparison with Belgian, Kalmykian and Italian artisanal cheeses

A snapshot on spatial and vertical distribution of bacterial communities in the eastern Indian Ocean

Evaluation of PacBio sequencing for full-length bacterial 16S rRNA gene classification

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