Method to Harmonize Data from Two Microbiome Sequencing Techniques Described

Scientists at the University of California San Diego have successfully integrated two leading microbiome sequencing techniques, 16S ribosomal RNA gene amplicon (16S) and shotgun metagenomics sequencing, thanks to their new reference database called Greengenes2. Their reference tree, published in Nature Biotechnology, allows researchers to compare and combine microbiome data derived from both techniques, rescuing over a decade's worth of 16S data that might have otherwise become obsolete in the age of shotgun sequencing.

The original Greengenes database had been widely used in the microbiome field for many years, but it was limited by relying on the sequence of a single gene, 16S, which lacked species-level specificity crucial for clinical work. Modern microbiome studies have moved to shotgun sequencing, offering more detailed insights into microbial function and species identification. The discrepancies between the two techniques were previously attributed to differences in sample preparation, but the new study reveals that incompatibilities arise from computation differences. Greengenes2 addresses this issue, allowing the reuse of data from millions of samples in older studies and enhancing the reproducibility of microbiome research.

To develop Greengenes2, the researchers expanded the Web of Life whole genome database and used computational tools to integrate 16S sequences into the whole-genome phylogeny. They created an extensive reference database that both 16S and shotgun sequencing data can be mapped onto. Confirming its effectiveness, the researchers analyzed data from both techniques against the Greengenes2 phylogeny, showing highly correlated diversity assessments, taxonomic profiles, and effect sizes.

Greengenes2 represents a major step forward in improving the reproducibility of microbiome studies and empowering scientists to draw clinical conclusions from microbiome data. The database allows a vast repository of 16S data to be combined with modern shotgun data, facilitating new meta-analyses and standardizing results across the two sequencing methods.