Auto Tag: profiling metagenomic communities

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.