4.6 Review

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

Journal

MICROORGANISMS
Volume 10, Issue 10, Pages -

Publisher

MDPI
DOI: 10.3390/microorganisms10101961

Keywords

16S rRNA; amplicons; ecology; microbiome; sequence analysis

Categories

Funding

  1. NERC Independent Research Fellowship [NE/L011956/1]
  2. EPSRC [EP/P029329/1, EP/V030515/1]
  3. Higher Education Authority (HEA) of Ireland through: the Programme for Research at Third Level Institutions
  4. Cycle 5 (PRTLI-5) - European Regional Development Fund (ERDF)
  5. Enterprise Ireland Technology Centres Programme [TC/2014/0016]
  6. Science Foundation Ireland [14/IA/2371, 16/RC/3889]
  7. Science Foundation Ireland (SFI) [14/IA/2371] Funding Source: Science Foundation Ireland (SFI)

Ask authors/readers for more resources

Amplifying short read regions, such as 16S rRNA for prokaryotic species or 18S rRNA for eukaryotic species, is a popular and economical method for studying microbial ecology. This review discusses recent advancements in in situ analytical tools that can provide insights into ecological phenomena, metabolic potential, species interactions, and community dynamics. The incorporation of additional information and modalities can enhance our understanding of how microbial communities respond to changes in ecosystems.
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.

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