Construction of habitat-specific training sets to achieve species-level assignment in 16S rRNA gene datasets.

ABSTRACT:

Background

The low cost of 16S rRNA gene sequencing facilitates population-scale molecular epidemiological studies. Existing computational algorithms can resolve 16S rRNA gene sequences into high-resolution amplicon sequence variants (ASVs), which represent consistent labels comparable across studies. Assigning these ASVs to species-level taxonomy strengthens the ecological and/or clinical relevance of 16S rRNA gene-based microbiota studies and further facilitates data comparison across studies.

Results

To achieve this, we developed a broadly applicable method for constructing high-resolution training sets based on the phylogenic relationships among microbes found in a habitat of interest. When used with the naïve Bayesian Ribosomal Database Project (RDP) Classifier, this tr

SUBMITTER: F Escapa I 

PROVIDER: S-EPMC7291764 | biostudies-literature | 2020 May

REPOSITORIES: biostudies-literature