Dataset Information

PhylOTU: a high-throughput procedure quantifies microbial community diversity and resolves novel taxa from metagenomic data.

ABSTRACT: Microbial diversity is typically characterized by clustering ribosomal RNA (SSU-rRNA) sequences into operational taxonomic units (OTUs). Targeted sequencing of environmental SSU-rRNA markers via PCR may fail to detect OTUs due to biases in priming and amplification. Analysis of shotgun sequenced environmental DNA, known as metagenomics, avoids amplification bias but generates fragmentary, non-overlapping sequence reads that cannot be clustered by existing OTU-finding methods. To circumvent these limitations, we developed PhylOTU, a computational workflow that identifies OTUs from metagenomic SSU-rRNA sequence data through the use of phylogenetic principles and probabilistic sequence profiles. Using simulated metagenomic data, we quantified the accuracy with which PhylOTU clusters reads into OTUs. Comparisons of PCR and shotgun sequenced SSU-rRNA markers derived from the global open ocean revealed that while PCR libraries identify more OTUs per sequenced residue, metagenomic libraries recover a greater taxonomic diversity of OTUs. In addition, we discover novel species, genera and families in the metagenomic libraries, including OTUs from phyla missed by analysis of PCR sequences. Taken together, these results suggest that PhylOTU enables characterization of part of the biosphere currently hidden from PCR-based surveys of diversity?

SUBMITTER: Sharpton TJ

PROVIDER: S-EPMC3024254 | biostudies-literature | 2011 Jan

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

PhylOTU: a high-throughput procedure quantifies microbial community diversity and resolves novel taxa from metagenomic data.

Sharpton Thomas J TJ Riesenfeld Samantha J SJ Kembel Steven W SW Ladau Joshua J O'Dwyer James P JP Green Jessica L JL Eisen Jonathan A JA Pollard Katherine S KS

PLoS computational biology 20110120 1

Microbial diversity is typically characterized by clustering ribosomal RNA (SSU-rRNA) sequences into operational taxonomic units (OTUs). Targeted sequencing of environmental SSU-rRNA markers via PCR may fail to detect OTUs due to biases in priming and amplification. Analysis of shotgun sequenced environmental DNA, known as metagenomics, avoids amplification bias but generates fragmentary, non-overlapping sequence reads that cannot be clustered by existing OTU-finding methods. To circumvent these ...[more]

PMID: 21283775

Similar Datasets

Project description:Microbial flow cytometry can rapidly characterize the status of microbial communities. Upon measurement, large amounts of quantitative single-cell data are generated, which need to be analyzed appropriately. Cytometric fingerprinting approaches are often used for this purpose. Traditional approaches either require a manual annotation of regions of interest, do not fully consider the multivariate characteristics of the data, or result in many community-describing variables. To address these shortcomings, we propose an automated model-based fingerprinting approach based on Gaussian mixture models, which we call PhenoGMM. The method successfully quantifies changes in microbial community structure based on flow cytometry data, which can be expressed in terms of cytometric diversity. We evaluate the performance of PhenoGMM using data sets from both synthetic and natural ecosystems and compare the method with a generic binning fingerprinting approach. PhenoGMM supports the rapid and quantitative screening of microbial community structure and dynamics.IMPORTANCE Microorganisms are vital components in various ecosystems on Earth. In order to investigate the microbial diversity, researchers have largely relied on the analysis of 16S rRNA gene sequences from DNA. Flow cytometry has been proposed as an alternative technology to characterize microbial community diversity and dynamics. The technology enables a fast measurement of optical properties of individual cells. So-called fingerprinting techniques are needed in order to describe microbial community diversity and dynamics based on flow cytometry data. In this work, we propose a more advanced fingerprinting strategy based on Gaussian mixture models. We evaluated our workflow on data sets from both synthetic and natural ecosystems, illustrating its general applicability for the analysis of microbial flow cytometry data. PhenoGMM supports a rapid and quantitative analysis of microbial community structure using flow cytometry.

Project description:BACKGROUND: As microbial ecologists take advantage of high-throughput sequencing technologies to describe microbial communities across ever-increasing numbers of samples, new analysis tools are required to relate the distribution of microbes among larger numbers of communities, and to use increasingly rich and standards-compliant metadata to understand the biological factors driving these relationships. In particular, the Earth Microbiome Project drives these needs by profiling the genomic content of tens of thousands of samples across multiple environment types. FINDINGS: Features of EMPeror include: ability to visualize gradients and categorical data, visualize different principal coordinates axes, present the data in the form of parallel coordinates, show taxa as well as environmental samples, dynamically adjust the size and transparency of the spheres representing the communities on a per-category basis, dynamically scale the axes according to the fraction of variance each explains, show, hide or recolor points according to arbitrary metadata including that compliant with the MIxS family of standards developed by the Genomic Standards Consortium, display jackknifed-resampled data to assess statistical confidence in clustering, perform coordinate comparisons (useful for procrustes analysis plots), and greatly reduce loading times and overall memory footprint compared with existing approaches. Additionally, ease of sharing, given EMPeror's small output file size, enables agile collaboration by allowing users to embed these visualizations via emails or web pages without the need for extra plugins. CONCLUSIONS: Here we present EMPeror, an open source and web browser enabled tool with a versatile command line interface that allows researchers to perform rapid exploratory investigations of 3D visualizations of microbial community data, such as the widely used principal coordinates plots. EMPeror includes a rich set of controllers to modify features as a function of the metadata. By being specifically tailored to the requirements of microbial ecologists, EMPeror thus increases the speed with which insight can be gained from large microbiome datasets.

Project description:A global phenomenon of increasing bark beetle-induced tree mortality has heightened concern regarding ecosystem response and biogeochemical implications. Here we explore microbial dynamics under lodgepole pines through the analysis of bulk (16S rDNA) and potentially active (16S rRNA) communities to understand terrestrial ecosystem responses associated with this form of large-scale tree morality. We found that the relative abundance of bulk and potentially active taxa was correlated across taxonomic levels, but at lower levels cladal differences became more apparent. Despite this correlation, there was a strong differentiation of community composition between bulk and potentially active taxa, with further clustering in association with stages of tree mortality. Surprisingly, community clustering as a function of tree phase had limited correlation to soil water content and total nitrogen concentrations, which were the only two measured edaphic parameters to differ in association with tree phase. Bacterial clustering is more readily explained by the observed decrease in abundance of active, rare microorganisms after tree death in conjunction with stable alpha diversity measurements. This enables the rare fraction of the terrestrial microbial community to maintain metabolic diversity by transitioning between metabolically active and dormant states during this ecosystem disturbance and contributes disproportionately to community dynamics and archived metabolic capabilities. These results suggest that analyzing the bulk and potentially active communities after beetle infestation might be a more sensitive indicator of disruption than measuring local edaphic parameters.ImportanceForests around the world are experiencing unprecedented mortality due to insect infestations fueled in part by a changing climate. While above-ground processes have been explored, changes at the terrestrial interface relevant to microbial biogeochemical cycling remain largely unknown. In this study, we investigated the changing bulk and potentially active microbial communities beneath healthy and beetle-killed trees. We found that even though few edaphic parameters were altered from beetle infestation, the rare microbes were more likely to be active and fluctuate between dormancy and metabolic activity. This indicates rare as opposed to abundant taxa contribute disproportionately to microbial community dynamics and presumably biogeochemical cycling within these types of perturbed ecosystems.

Project description:BackgroundIncreasing studies have demonstrated potential disproportionate functional and ecological contributions of rare taxa in a microbial community. However, the study of the microbial rare biosphere is hampered by their inherent scarcity and the deficiency of currently available techniques. Sample-wise cross contaminations might be introduced by sample index misassignment in the most widely used metabarcoding amplicon sequencing approach. Although downstream bioinformatic quality control and clustering or denoising algorithms could remove sequencing errors and non-biological artifact reads, no algorithm could eliminate high quality reads from sample-wise cross contaminations introduced by index misassignment, making it difficult to distinguish between bona fide rare taxa and potential false positives in metabarcoding studies.ResultsWe thoroughly evaluated the rate of index misassignment of the widely used NovaSeq 6000 and DNBSEQ-G400 sequencing platforms using both commercial and customized mock communities, and observed significant lower (0.08% vs. 5.68%) fraction of potential false positive reads for DNBSEQ-G400 as compared to NovaSeq 6000. Significant batch effects could be caused by stochastically introduced false positive or false negative rare taxa. These false detections could also lead to inflated alpha diversity of relatively simple microbial communities and underestimated that of complex ones. Further test using a set of cow rumen samples reported differential rare taxa by different sequencing platforms. Correlation analysis of the rare taxa detected by each sequencing platform demonstrated that the rare taxa identified by DNBSEQ-G400 platform had a much higher possibility to be correlated with the physiochemical properties of rumen fluid as compared to NovaSeq 6000 platform. Community assembly mechanism and microbial network correlation analysis indicated that false positive or negative rare taxa detection could lead to biased community assembly mechanism and identification of fake keystone species of the community.ConclusionsWe highly suggest proper positive/negative/blank controls, technical replicate settings, and proper sequencing platform selection in future amplicon studies, especially when the microbial rare biosphere would be focused.

Dataset Information

PhylOTU: a high-throughput procedure quantifies microbial community diversity and resolves novel taxa from metagenomic data.

Publications

PhylOTU: a high-throughput procedure quantifies microbial community diversity and resolves novel taxa from metagenomic data.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets