Recent developments in NGS techniques (amplicon based or metagenomics) and associated bioinformatics pipeline tools are allowing the characterization of microbial species, i.e. taxonomic or functional groupings, in terrestrial environments. Unlike the culture-dependant methods, metagenomic and high-throughput amplicon sequencing analysis are providing large volumes of in-depth information (data) about microbial communities. Currently, this information is largely used to decipher the diversity of communities and their response to environmental stresses and stimuli. In spite of the many advantages that these tools provide to the microbial ecology research, they also come with a number of caveats or pitfalls which can result in erroneous data (e.g. incorrect annotations) and/or interpretation. Some of the examples include: sequencing related issues, biased reference data set, complex workflows, annotation issues, ease of avoidance of statistical rigour etc.
One of the examples of the latest usage of omics data in microbial ecology research is related to interactions between species. NGS techniques when combined with network analysis tools could help identify associations or interactions among individual or groups of organisms mainly through co-occurrence data. The effects of biodiversity on ecosystem functioning are mediated by the ecological interactions between species. Therefore, studies about interactions among different species/populations play a critical role in ecological research, in particular, to understand the complexity and stability of communities and their role in broad-scale ecosystem functions, e.g. decomposition, disease suppression, nutrient cycling. Our analysis of 28S LSU sequence based fungal community data, using the Random Matrix Theory-based molecular ecological network analysis tools, showed that the suppressive community network is less condensed with greater modularity whereas the non-suppressive community network is much more centralised.
Overall, although omics-based tools offer a unique ability to interrogate microbial communities in complex ecosystems, a number of challenges related to the quality of NGS data, selection of appropriate/correct bioinformatics tools, validity of various network construction methods etc. remain to be overcome to achieve full benefits from these advanced tools.