ArticleBMC microbiology2026
Comparison of library preparation protocols and bioinformatic pipelines in high-throughput 16S rRNA gene sequencing.
Article in BMC microbiology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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8 authors.
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Abstract
background16S rRNA gene sequencing is widely used for bacterial community profiling in both clinical and research contexts. The expanding availability of library preparation protocols and bioinformatic pipelines increases analytical flexibility but may also introduce method-dependent biases that affect inferred microbial composition and relative abundance estimates. The relative impact of library preparation protocol, amplicon region, and bioinformatic pipeline on species-level taxonomic inference and compositional agreement remains insufficiently characterised. We therefore compared the Illumina 16S Metagenomic Sequencing Library Preparation protocol (V3-V4) and the Zymo Quick-16S Plus NGS Library Prep Kit (V1-V2 and V3-V4) in combination with two bioinformatic pipelines, nf-core/ampliseq and TRANA. Performance was assessed using defined microbial community standards and human faecal and colonic biopsy samples.
resultsPipeline choice was the dominant driver of variation in inferred community composition, exceeding the effects of amplicon regions and library preparation protocols. Genus-level profiles were broadly concordant across methods. Species-level resolution and agreement with expected community composition differed systematically between pipelines, with TRANA demonstrating lower Bray-Curtis dissimilarities to expected compositions than nf-core/ampliseq. Amplicon region had a secondary, pipeline-dependent effect, while protocol differences were minor. In clinical samples, inter-individual biological variation exceeded technical variation.
conclusionsBioinformatic processing substantially influenced species-level inference in short-read 16S sequencing, highlighting the importance of pipeline selection for microbiome study design and cross-study comparability.
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