To circumvent challenges associated with highly compositional relative abundance data , numerous tools for statistical analysis have been introduced. We here suggest such data-driven approaches to address the concerns of normalization, false-discovery rates and the compositional nature of sequencing.
One of the first approaches to analyze amplicon sequencing data is to remove potential sequencing errors. Doing so contributes to the elimination of chimeras and other sequencing artefacts that tend to falsely boost diversity levels  \cite{Edgar2011,Haas2011}. The use of amplicon sequencing variants (ASVs), instead of operational taxonomic units (OTUs) attempts to overcome this issue by assigning a greater probability of a true biological sequence being more abundant than an error-containing sequence (30)⁠. To that end, bioinformatic tools such as DADA2 (31)⁠ and Deblur (32)⁠ attempt to use sequencing error profiles to resolve amplicon sequencing data into ASVs. Furthermore, even though there are some caveats associated with the use of ASVs that might require previous considerations, they have an intrinsic biological meaning as a DNA sequence, as opposed to OTUs which can either be a representation of the most abundant biological sequence or a consensus sequence. Additionally, ASVs make the merging of datasets possible, even when the sequencing primer pairs are different (30)⁠.
Another relevant step when analyzing sequencing data is to account for the different sequencing effort across samples (i.e. different library sizes) that can result in a substantially different number of recovered reads even within sample replicates. Ways to tackle this issue include total library size normalization and rarefaction, although recent literature has advised against the latter (33)⁠. Bioinformatic tools such as DeSeq2 and EdgeR that were originally built for differential gene expression analyses of RNA-seq data, now extend to amplicon-based studies.  These packages provide ways to normalize count tables using the “relative log expression” (RLE) and the “Trimmed Mean of M-values” (TMM) normalization approaches respectively (34, 35)⁠. Both methods are applied on a raw or a low-abundance filtered count table and have performed well in both real and simulated datasets and outperform rarefaction-based approaches (33)⁠. Other alternatives to account for the compositional aspect of sequencing data include center log (CLR), isometric log (ILR) or additive log (ALR) ratios transformations on a count data matrix (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. 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”).

Steps toward reproducible and quantitative sequencing studies

Data-driven approaches to improve reproducibility

In addition to the limitations imposed by sequencing technology and the compositional nature of sequencing data, another aspect that prevents data analyses from being fully quantitative is the potential multiple copies of marker genes per organism. For example, the 16S rRNA gene copy number per bacterial cell can vary between 1-18 and can additionally show variation within different strains of the same species (39, 40; Lavrinienko et al., 2020, \cite{Stoddard_2014}). 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. Several computational tools can  correct amplicon datasets for the number of 16S rRNA gene copies based on existing genome information (e.g. PICRUSt \cite{Douglas_2019} and CopyRighter \cite{Angly_2014}). However, correcting for 16S rRNA gene copy numbers in sequencing surveys remains challenging, particularly for soil, as the gene copy numbers are only known for a subsection of the soil microbes (\cite{Louca_2018,Nunan_2020}. This challenge becomes even more problematic for marker genes of fungi and other eukaryotes such as protists as the copy number here can vary more drastically among taxa \cite{Gong_2013,Gong_2019}. Other housekeeping genes, which occur only once in a genome, such as the recA \cite{Eisen1995} have been proposed in the past, as universal phylogenetic marker genes, but their use remains limited because of lower taxonomic resolution or limited databases.
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 \cite{Tkacz_2018,Hardwick2018}. 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 amplification and/or sequencing biases, it does not take extraction efficiency into account (\cite{Hardwick2018})⁠. 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 \cite{Zemb2020}⁠. 

 Experimental approaches to more quantitative sequencing studies  

Maybe we need a short introduction here why we think that seq data should become more quantitative? For example, if sequencing data shows that species X in sample A increases compared to sample B, it does not necessarily reflect a quantitative increase of species X in sample A. It could well be that the numbers of species X decrease in both sample A and B, with a stronger decrease in sample B.  Also that both increase. etc. Thus, knowing about the absolute numbers may help to "adjust" sequencing data?
An approach towards absolute abundance data from soil communities are direct cell counts obtained through fluorescence microscopy \cite{Bloem1995} or fluorescence activated cell counting \cite{Khalili_2019}. Total counts help to assess the absolute abundance of microbial cells that fall within a certain range of parameters such as cell size and morphology. Counting of cells may help to circumvent overestimation of microbial diversity related to extracellular DNA by counting only intact cells (48, 49)⁠. In addition, it may also be combined with with BioOrthogonal Non-Canonical Amino acid Tagging (BONCAT) to target only the fraction of cells within a soil sample that is translationally active in situ \cite{Couradeau_2019}.
To the best of our knowledge, a direct use of absolute abundances of microbial cells to improve the evaluation of microbial diversity (evenness) of sequencing data has not been reported.
Would it even make sense? Please brainstorm with me. What if:
In contrast to using the total number of all cells, the actual abundances of certain taxa of interest in a soil sample can be obtained by the use of Fluorescence in situ hybridization (FISH) techniques. Recently, Piwosz et al. \cite{Piwosz2020} used CARD-FISH to count the abundances of bacterial taxa in aquatic samples and to compare these to relative abundances generated by amplicon sequencing. The authors concluded that relative abundance data obtained through amplicon sequencing was robust enough for ecological interpretation on a community level.  For specific taxonomic groups, however, the correlation of abundances obtained with both techniques disagreed in large parts, suggesting that care has to be taken when interpreting relative abundance data of single taxa derived from amplicon sequencing. Such technical comparisons for soil samples are rare (e.g. \cite{Ushio_2014}), but given the diversity of soil microbiomes we suggest to use amplicon sequencing data mainly for screenings of soil microbiomes on a community scale (e.g. phylum/class/order level). If the dynamics of certain phylogenetic groups are to be understood on a quantitative basis, we suggest to apply suitable FISH techniques (e.g. \cite{Schmidt_2013}).