![[feed]](/style/images/feed-icon-14x14.png){kind=icon}
Tools
Tools
Preview |
PDF (Original Article)
- Requires a PDF viewer such as GSview, Xpdf or Adobe Acrobat Reader
2MB |
![[thumbnail of Supporting Information]](https://edoc.mdc-berlin.de/style/images/fileicons/application_msword.png) |
MS Word (Supporting Information)
397kB |
| Item Type: | Article |
|---|---|
| Title: | Systematic quantification and removal of host DNA contamination in 16S rRNA gene sequencing |
| Creators: |
Birkner, Till  ORCID: https://orcid.org/0000-0003-2656-2821, Bartolomaeus, Theda Ulrike Patricia ORCID: https://orcid.org/0000-0002-5156-7184, McParland, Victoria ORCID: https://orcid.org/0000-0003-0731-3272, Forslund-Startceva, Sofia Kirke ORCID: https://orcid.org/0000-0003-4285-6993 and Löber, Ulrike ORCID: https://orcid.org/0000-0001-7468-9531
|
| 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. |
| Keywords: | 16S rRNA Gene Sequencing, Host Contamination, Low‐Biomass Samples, Microbiome Quality Control, Microbiome Standards, Off‐Target Amplification |
| Source: | Quantitative Biology |
| ISSN: | 2095-4689 |
| Publisher: | Wiley |
| Volume: | 14 |
| Number: | 4 |
| Page Range: | e70055 |
| Date: | December 2026 |
| Official Publication: | https://doi.org/10.1002/qub2.70055 |
| Related to: |
Repository Staff Only: item control page
