ArticleScientific reports2022
Higher off-target amplicon detection rate in MiSeq v3 compared to v2 reagent kits in the context of 16S-rRNA-sequencing.
Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Nasopharyngeal microbiota is influenced by agricultural air pollution in individuals with and without COPD.Scientific reports · 2025Article
- Microbiome profiling and Actinomycetes isolation from tropical marine sponges.AIMS microbiology · 2025Article
- Differential microbial composition in parasitic vs. questing ticks based on 16S next-generation sequencing.Frontiers in microbiology · 2023Article
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Authors and funding
8 authors.
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Abstract
One of the most widely used techniques in microbiota research is 16S-rRNA-sequencing. Several laboratory processes have been shown to impact sequencing results, especially in low biomass samples. Low biomass samples are prone to off-target amplification, where instead of bacterial DNA, host DNA is erroneously amplified. Knowledge on the laboratory processes influencing off-target amplification and detection is however scarce. We here expand on previous findings by demonstrating that off-target amplification is not limited to invasive biopsy samples, but is also an issue in low bacterial biomass respiratory (mucosal) samples, especially when below 0.3 pg/μL. We show that off-target amplification can partly be mitigated by using gel-based library purification methods. Importantly, we report a higher off-target amplicon detection rate when using MiSeq reagent kit v3 compared to v2 (mean 13.3% vs 0.1% off-target reads/sample, respectively), possibly as a result of differences in reagents or sequencing recipes. However, since after bioinformatic removal of off-target reads, MiSeq reagent kit v3 still results in a twofold higher number of reads when compared to v2, v3 is still preferred over v2. Together, these results add to the growing knowledge base on off-target amplification and detection, allowing researchers to anticipate this problem in 16S-rRNA-based microbiome studies involving low biomass samples.
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