Performance of Microbiome Sequence Inference Methods in Environments with Varying Biomass

MSystems
Vincent CarusoLisa Karstens

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 baseline performance. We then benchmarked the methods on a dilution series made from a single mock community-samples that varied only in biomass. ASVs/OTUs inferred by each method were classified as representing expected community, technical noise, or contamination. With the high-biomass data, we found that the ASV methods had good sensitivity and precision, whereas the other methods suffered in one area or in both. Inferred contamination was pre...Continue Reading

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Citations

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Datasets Mentioned

BETA
ERR777695
ERR777739
SRP155048

Methods Mentioned

BETA
PCR
in silico methods
electrophoresis

Software Mentioned

USEARCH
QIIME
DADA2
Deblur
MED
BLAST
UCHIME
R
R script
UCLUST

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