Beyond Basic Diversity Estimates—Analytical Tools for Mechanistic Interpretations of Amplicon Sequencing Data

Abstract

Understanding microbial ecology through amplifying short read regions, typically 16S rRNA for prokaryotic species or 18S rRNA for eukaryotic species, remains a popular, economical choice. These methods provide relative abundances of key microbial taxa, which, depending on the experimental design, can be used to infer mechanistic ecological underpinnings. In this review, we discuss recent advancements in in situ analytical tools that have the power to elucidate ecological phenomena, unveil the metabolic potential of microbial communities, identify complex multidimensional interactions between species, and compare stability and complexity under different conditions. Additionally, we highlight methods that incorporate various modalities and additional information, which in combination with abundance data, can help us understand how microbial communities respond to change in a typical ecosystem. Whilst the field of microbial informatics continues to progress substantially, our emphasis is on popular methods that are applicable to a broad range of study designs. The application of these methods can increase our mechanistic understanding of the ongoing dynamics of complex microbial communities.

Host genotype and microbiome associations in co‐occurring clonal and non‐clonal kelp, Ecklonia radiata

“…For example, functional predictions can be highly variable compared to data obtained through direct functional assessments such as shotgun metagenomic sequencing and transcriptomics (Toole et al, 2021) and may be poorly represented in non‐human environments (Sun et al, 2020). Nonetheless, the available genomes from which the present functional profiles are being predicted are in constant development and enough resolution at this stage exists to be able to do a general metabolic profiling of the bacterial communities (Douglas et al, 2020; Trego et al, 2022). Thus, the results here can provide a rough understanding of the functional capacity of the kelp's associated bacterial community, but further research should be done to assess specific functions and real‐time intraspecific genetic regulation that could vary between the two morphs.…”

Molecular ecology of microbiomes in the wild: Common pitfalls, methodological advances and future directions

“…Modelling advances in community ecology offer exciting opportunities to understand the complex patterns in microbial diversity and complement robust sampling designs (Grantham et al., 2020; Trego et al., 2022). In addition, novel methods for analysing amplicon sequencing data are continuously emerging, primarily focused on the human gut microbiome but adaptable to other microbial ecology fields with suitable study designs and datasets (Trego et al., 2022).…”

“…Modelling advances in community ecology offer exciting opportunities to understand the complex patterns in microbial diversity and complement robust sampling designs (Grantham et al., 2020; Trego et al., 2022). In addition, novel methods for analysing amplicon sequencing data are continuously emerging, primarily focused on the human gut microbiome but adaptable to other microbial ecology fields with suitable study designs and datasets (Trego et al., 2022). These tools tackle a broad range of ecological and evolutionary questions from quantifying community assembly processes, mapping occurrence networks, capturing spatial/temporal dynamics, integrating multi‐omics, identifying differentially abundant taxa, finding species‐environment associations and predicting functional patterns (Trego et al., 2022).…”

Data-Driven Mathematical Approach for Removing Rare Features in Zero-Inflated Datasets

“…However, the process of feature removal is usually accomplished by setting an arbitrary threshold of percent presence among samples (i.e., only retain features present in at 10% of samples) and there appears to be no rule or consistent approach across studies for identifying that threshold. Thresholds for feature removal have been set at 10% [9], ≥1% in at least one sample [10], 85% [11,12], or not reported or ignored [13][14][15][16]. While setting a percentage threshold for feature retention seems appealing, such a process does not consider the sparsity of individual datasets.…”