Studying soil microbiomes is challenging because soil contains thousands of microbial species at vastly different abundances. The choice of sequencing method has a strong effect on which of these species are detected and how the community is described. Three approaches now dominate soil microbiome research: short-read 16S rRNA amplicon sequencing on Illumina platforms, full-length 16S sequencing on Oxford Nanopore Technologies (ONT) platforms (particularly the R10.4.1 flow cell), and long-read shotgun metagenomics. Each has distinct biases that shape the recovered community, yet researchers routinely select a method based on cost, understanding, or local expertise rather than on a clear knowledge of what each approach methodically over- or under-represents. Here, we review head-to-head benchmarking studies that have applied two or more of these methods to the same soil or directly comparable samples. We show that while long-read and short-read 16S approaches generally converge on dominant taxa and on between-sample differences, they disagree substantially on alpha diversity estimates, rare taxon detection, and the relative abundances of entire phyla. The R10.4.1 flow cell chemistry has narrowed but not eliminated the accuracy gap with Illumina, and shotgun metagenomics reveals systematic biases in both short and long-read assembly that depend on population diversity within the sample. We synthesise this evidence into an evidence-based decision framework tied to specific research questions and recognise the gaps in soil-specific benchmarking that limit current methods. Rather than asking which platform is “best,” we argue that method choice should be framed as an important part of study design, with the biases of the chosen method acknowledged and, where possible, controlled for.
Albastaki et al. (2026) studied this question.