Performance of Microbiome Sequence Inference Methods in Environments with Varying Biomass.

ABSTRACT: Microbiome community composition plays an important role in human health, and while most research to date has focused on high-microbial-biomass communities, low-biomass communities are also important. However, contamination and technical noise make determining the true community signal difficult when biomass levels are low, and the influence of varying biomass on sequence processing methods has received little attention. Here, we benchmarked six methods that infer community composition from 16S rRNA sequence reads, using samples of varying biomass. We included two operational taxonomic unit (OTU) clustering algorithms, one entropy-based method, and three more-recent amplicon sequence variant (ASV) methods. We first compared inference results from high-biomass mock communities to assess bas

SUBMITTER: Caruso V 

PROVIDER: S-EPMC6381225 | biostudies-literature | 2019 Jan-Feb

REPOSITORIES: biostudies-literature