(36, 37)⁠.
After data normalization, traditional amplicon sequencing data analyses include the generation of distance matrices for ordination, clustering, and variance partitioning analyses. Commonly used distance metrics include Bray-Curtis, Jaccard and Unifrac (weighted and unweighted) that – regardless of their value in other fields - also do not take into account the compositional nature of sequencing data. The Aitchison distance - defined as the Euclidian distance on top of a center-log transformed count matrix – is a viable compositional alternative (36)⁠ on top of which ordinations (e.g. PCA biplots) can be performed. Additionally, the Philr transform metric has been introduced as compositional alternative to the weighted Unifrac, that carries phylogenetic information (38)⁠. Most of the above mentioned compositional options are implemented in R packages and include publicly available tutorials.
In addition to the limitations imposed by sequencing technology and the nature of the sequencing data, another aspect that prevents amplicon sequencing data analyses from being fully quantitative is the potential multiple copies of marker genes per organism. The 16S rRNA gene copy number per microbial cell can vary between 1-18 and can additionally show variation within different strains of the same species (39, 40; Lavrinienko et al., 2020). Therefore, relying solely on the number and diversity of 16S rRNA gene sequences can lead to inaccurate estimates of abundance and diversity of microbial communities. Correcting for 16S rRNA gene copy numbers in sequencing surveys remains challenging, particularly for soil. Introducing an internal spike-in can be a useful tool towards more quantitative amplicon data analyses and there are a few studies that applied this technique in soil (41, 42)⁠. There are however, important considerations: i) the choice of spike should neither fall on members of the existing microbial community, nor it should be in concentrations that would shift the sequencing effort towards it; ii) the timing of addition of the spike (before or after nucleic acids extractions) will dictate the kind of retrieved information: while adding the spike after extraction can provide good estimates of sequencing biases, it does not take extraction efficiency into account (42)⁠. A recent study combined amplicon sequencing, a synthetic DNA spike of known concentration on the samples prior extraction, and qPCR quantifications to back calculate the number of copies before extraction after taking into account the extraction yield. The ratio of each OTU against the initial concentration of 16S rRNA genes was used to calculate more accurate abundance levels of each OTU after taking extraction efficiency into account (43)⁠. As a consequence of all the above-mentioned limitations, we recommend a critical evaluation of the different data analyses tools in light of the intrinsic nature of each experimental setup (see section “Ecological interpretations from amplicon sequencing data”).