Systematic quantification and removal of host DNA contamination in 16S rRNA gene sequencing
- 1Host‐Microbiome Factors in Cardiovascular Disease Lab Max Delbrück Center for Molecular Medicine in the Helmholtz Association Berlin Germany
Abstract
16S ribosomal RNA (rRNA) gene sequencing is a standard tool for microbial community analysis. Challenges can occur, particularly in low‐biomass samples, when host DNA triggers off‐target amplification. Low‐biomass microbiome studies are particularly vulnerable to contamination from host DNA, which can obscure microbial signals and bias interpretation. This contamination presents a significant barrier to accurately characterizing microbial communities, especially in clinical or environmental samples with limited bacterial DNA. To systematically quantify and mitigate host DNA interference, we constructed a bacterial mock community dilution series spiked with controlled proportions of human DNA. Using 16S rRNA gene sequencing, we assessed how increasing host DNA affects microbial community profiles and evaluated several computational approaches for removing host‐derived sequences, including pre‐clustering filtering, post‐clustering operational taxonomic unit (OTU) filtering, and the R package Decontam. We found that off‐target amplification was more prevalent when the bacterial content was less than 10% relative to host DNA. Total DNA concentration induced minimal bias. Post‐clustering OTU filtering and reference genome mapping effectively reduced host contamination. Among the tested correction strategies, post‐clustering OTU filtering proved most effective and computationally sustainable, achieving nearly complete removal of host‐derived reads with minimal effect on microbial diversity estimates. Although 16S rRNA gene sequencing remains a cost‐effective and high‐throughput technology, it requires rigorous methodological controls in low‐biomass contexts. Our study offers a systematic evaluation of off‐target amplification effects and practical mitigation strategies to improve the accuracy of microbial community analysis. The presented framework provides a robust and scalable approach for identifying and removing host contamination from low‐biomass 16S rRNA sequencing data.
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BibTeX
@article{birkner2026systematic,
title = {{Systematic quantification and removal of host DNA contamination in 16S rRNA gene sequencing}},
author = {Till Birkner and Theda Ulrike Patricia Bartolomaeus and Victoria McParland and Sofia Kirke Forslund‐Startceva and Ulrike Löber},
journal = {Quantitative Biology},
year = {2026},
month = nov,
doi = {10.1002/qub2.70055},
url = {https://doi.org/10.1002/qub2.70055},
note = {CC BY},
}
RIS
TY - JOUR
TI - Systematic quantification and removal of host DNA contamination in 16S rRNA gene sequencing
AU - Till Birkner
AU - Theda Ulrike Patricia Bartolomaeus
AU - Victoria McParland
AU - Sofia Kirke Forslund‐Startceva
AU - Ulrike Löber
PY - 2026
DA - 2026/11/07
JO - Quantitative Biology
DO - 10.1002/qub2.70055
UR - https://doi.org/10.1002/qub2.70055
AB - 16S ribosomal RNA (rRNA) gene sequencing is a standard tool for microbial community analysis. Challenges can occur, particularly in low‐biomass samples, when host DNA triggers off‐target amplification. Low‐biomass microbiome studies are particularly vulnerable to contamination from host DNA, which can obscure microbial signals and bias interpretation. This contamination presents a significant barrier to accurately characterizing microbial communities, especially in clinical or environmental samples with limited bacterial DNA. To systematically quantify and mitigate host DNA interference, we constructed a bacterial mock community dilution series spiked with controlled proportions of human DNA. Using 16S rRNA gene sequencing, we assessed how increasing host DNA affects microbial community profiles and evaluated several computational approaches for removing host‐derived sequences, including pre‐clustering filtering, post‐clustering operational taxonomic unit (OTU) filtering, and the R package Decontam. We found that off‐target amplification was more prevalent when the bacterial content was less than 10% relative to host DNA. Total DNA concentration induced minimal bias. Post‐clustering OTU filtering and reference genome mapping effectively reduced host contamination. Among the tested correction strategies, post‐clustering OTU filtering proved most effective and computationally sustainable, achieving nearly complete removal of host‐derived reads with minimal effect on microbial diversity estimates. Although 16S rRNA gene sequencing remains a cost‐effective and high‐throughput technology, it requires rigorous methodological controls in low‐biomass contexts. Our study offers a systematic evaluation of off‐target amplification effects and practical mitigation strategies to improve the accuracy of microbial community analysis. The presented framework provides a robust and scalable approach for identifying and removing host contamination from low‐biomass 16S rRNA sequencing data.
SN - 2095-4697
SN - 2095-4689
ER -