Toward efficient and high-fidelity metagenomic data from sub-nanogram DNA: evaluation of library preparation and decontamination methods.

backgroundShotgun metagenomic sequencing has greatly expanded the understanding of microbial communities in various biological niches. However, it is still challenging to efficiently convert sub-nanogram DNA to high-quality metagenomic libraries and obtain high-fidelity data, hindering the exploration of niches with low microbial biomass.

resultsTo cope with this challenge comprehensively, we evaluated the performance of various library preparation methods on 0.5 pg-5 ng synthetic microbial community DNA, characterized contaminants, and further applied different in silico decontamination methods. First, we discovered that whole genome amplification prior to library construction led to worse outcomes than preparing libraries directly. Among different non-WGA-based library preparation methods, we found the endonuclease-based method being generally good for different amounts of template and the tagmentation-based method showing specific advantages with 0.5 pg template, based on evaluation metrics including fidelity, proportion of designated reads, and reproducibility. The load of contaminating DNA introduced by library preparation varied from 0.01 to 15.59 pg for different kits and accounted for 0.05 to 45.97% of total reads. A considerable fraction of the contaminating reads were mapped to human commensal and pathogenic microbes, thus potentially leading to erroneous conclusions in human microbiome studies. Furthermore, the best performing in silico decontamination method in our evaluation, Decontam-either, was capable of recovering the real microbial community from libraries where contaminants accounted for less than 10% of total reads, but not from libraries with heavy and highly varied contaminants.

conclusionsThis study demonstrates that high-quality metagenomic data can be obtained from samples with sub-nanogram microbial DNA by combining appropriate library preparation and in silico decontamination methods and provides a general reference for method selection for samples with varying microbial biomass.