June 1, 2021  |  

Accurately surveying uncultured microbial species with SMRT Sequencing

Background: Microbial ecology is reshaping our understanding of the natural world by revealing the large phylogenetic and functional diversity of microbial life. However the vast majority of these microorganisms remain poorly understood, as most cultivated representatives belong to just four phylogenetic groups and more than half of all identified phyla remain uncultivated. Characterization of this microbial ‘dark matter’ will thus greatly benefit from new metagenomic methods for in situ analysis. For example, sensitive high throughput methods for the characterization of community composition and structure from the sequencing of conserved marker genes. Methods: Here we utilize Single Molecule Real-Time (SMRT) sequencing of full-length 16S rRNA amplicons to phylogenetically profile microbial communities to below the genus-level. We test this method on a mock community of known composition, as well as a previously studied microbial community from a lake known to predominantly contain poorly characterized phyla. These results are compared to traditional 16S tag sequencing from short-read technologies and subsets of the full-length data corresponding to the same regions of the 16S gene. Results: We explore the benefits of using full-length amplicons for estimating community structure and diversity. In addition, we investigate the possible effects of context-specific and GC-content biases known to affect short-read sequencing technologies on the predicted community structure. We characterize the potential benefits of profiling metagenomic communities with full-length 16S rRNA genes from SMRT sequencing relative to standard methods.


June 1, 2021  |  

Profiling metagenomic communities using circular consensus and Single Molecule, Real-Time Sequencing.

There are many sequencing-based approaches to understanding complex metagenomic communities spanning targeted amplification to whole-sample shotgun sequencing. While targeted approaches provide valuable data at low sequencing depth, they are limited by primer design and PCR amplification. Whole-sample shotgun experiments generally use short-read, second-generation sequencing, which results in data processing difficulties. For example, reads less than 1 kb in length will likely not cover a complete gene or region of interest, and will require assembly. This not only introduces the possibility of incorrectly combining sequence from different community members, it requires a high depth of coverage. As such, rare community members may not be represented in the resulting assembly. Circular-consensus, single molecule, real-time (SMRT) Sequencing reads in the 1-2 kb range, with >99% accuracy can be efficiently generated for low amounts of input DNA. 10 ng of input DNA sequenced in 4 SMRT Cells would generate >100,000 such reads. While throughput is low compared to second-generation sequencing, the reads are a true random sampling of the underlying community, since SMRT Sequencing has been shown to have no sequence-context bias. Long read lengths mean that that it would be reasonable to expect a high number of the reads to include gene fragments useful for analysis.


June 1, 2021  |  

Profiling metagenomic communities using circular consensus and Single Molecule, Real-Time Sequencing

There are many sequencing-based approaches to understanding complex metagenomic communities, spanning targeted amplification to whole-sample shotgun sequencing. While targeted approaches provide valuable data at low sequencing depth, they are limited by primer design and PCR amplification. Whole-sample shotgun experiments require a high depth of coverage. As such, rare community members may not be represented in the resulting assembly. Circular-consensus, Single Molecule, Real-Time (SMRT) Sequencing reads in the 1-2 kb range, with >99% consensus accuracy, can be efficiently generated for low amounts of input DNA, e.g. as little as 10 ng of input DNA sequenced in 4 SMRT Cells can generate >100,000 such reads. While throughput is low compared to second-generation sequencing, the reads are a true random sampling of the underlying community. Long read lengths translate to a high number of the reads harboring full genes or even full operons for downstream analysis. Here we present the results of circular-consensus sequencing on a mock metagenomic community with an abundance range of multiple orders of magnitude, and compare the results with both 16S and shotgun assembly methods. We show that even with relatively low sequencing depth, the long-read, assembly-free, random sampling allows to elucidate meaningful information from the very low-abundance community members. For example, given the above low-input sequencing approach, a community member at 1/1,000 relative abundance would generate 100 1-2 kb sequence fragments having 99% consensus accuracy, with a high probability of containing a gene fragment useful for taxonomic classification or functional insight.


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