New paper of interest on “Phylotags” – High-resolution phylogenetic microbial community profiling

This seems like it could be of interest to microbial community researchers: The ISME Journal – High-resolution phylogenetic microbial community profiling

Abstract (with bolding by me)

Over the past decade, high-throughput short-read 16S rRNA gene amplicon sequencing has eclipsed clone-dependent long-read Sanger sequencing for microbial community profiling. The transition to new technologies has provided more quantitative information at the expense of taxonomic resolution with implications for inferring metabolic traits in various ecosystems. We applied single-molecule real-time sequencing for microbial community profiling, generating full-length 16S rRNA gene sequences at high throughput, which we propose to name PhyloTags. We benchmarked and validated this approach using a defined microbial community. When further applied to samples from the water column of meromictic Sakinaw Lake, we show that while community structures at the phylum level are comparable between PhyloTags and Illumina V4 16S rRNA gene sequences (iTags), variance increases with community complexity at greater water depths. PhyloTags moreover allowed less ambiguous classification. Last, a platform-independent comparison of PhyloTags and in silico generated partial 16S rRNA gene sequences demonstrated significant differences in community structure and phylogenetic resolution across multiple taxonomic levels, including a severe underestimation in the abundance of specific microbial genera involved in nitrogen and methane cycling across the Lake’s water column. Thus, PhyloTags provide a reliable adjunct or alternative to cost-effective iTags, enabling more accurate phylogenetic resolution of microbial communities and predictions on their metabolic potential.

Basically, they use PacBio sequencing and a novel informatics workflow to generate full length 16S rRNA sequences from communities of microbes.

Workflow of the PhyloTag sequence generation and cluster analysis pipeline with results from one of the five mock community replicate data sets (more details in Supplementary Figure 1). A simulated FL 16S rRNA gene read data set generated from the 23 genomes of the selected bacterial species was used to optimize the clustering steps in the pipeline (see Materials and methods section). Detailed processing steps for iTag and shotgun sequences are illustrated in Supplementary Figure 1.

And they show this approach can be a powerful tool in characterizing communities, and perhaps most importantly allows one to more accurately phylogenetically analyze members of the community than short “iTag” sequencing.  Now, this approach is more expensive and not everyone has access to a PacBio system, but it could be of use to others out there.

They conclude

Using PhyloTags to assess microbial community diversity in environmental samples allows us to fill important gaps in the tree of life while improving classification and microbial community profiling accuracy with important implications for inferred metabolic potential and biogeochemical roles of uncultivated microorganisms in natural and human engineered ecosystems


One issue that hopefully will get addressed:

The paper reports “Raw and processed sequence data is publically available on the JGI Genome portal page (” but that link is dead.

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Jonathan Eisen

I am an evolutionary biologist and a Professor at U. C. Davis. My lab is in the UC Davis Genome Center and I hold appointments in the Department of Medical Microbiology and Immunology in the School of Medicine and the Department of Evolution and Ecology in the College of Biological Sciences. My research focuses on the origin of novelty (how new processes and functions originate). To study this I focus on sequencing and analyzing genomes of organisms, especially microbes and using phylogenomic analysis (see my lab site here which has more information on lab activities).  In addition to research, I am heavily involved in the Open Access publishing and Open Science movements.