Looking under the mat: benchmarking computational methods for analysing microbial datasets

Dr Matthew Campnell¹, Dr Mattia Saccò¹, Dr Juliana Mendes Monteiro², Dr Alex Laini³, Professor Morten Allentoft^1,4

¹Trace and Environmental DNA (TrEnD) Laboratory, School of Molecular and Life Sciences, Curtin University, Perth, Australia, ²School of Earth and Environmental Sciences, The University of Queensland, Brisbane, Australia, ³Department of Life Sciences and Systems Biology, University of Torino , Torino, Italy, ⁴Lundbeck Foundation GeoGenetics Centre, GLOBE Institute, University of Copenhagen, Copenhagen, Denmark

Microbial mats are ideal model systems for ecological analysis: they are small-scale, self-sustaining, and comprise the major key element cycles in nature. Given the complexity of these assemblages, their study relies on the optimization and validation of computational methods for analyzing microbial datasets, such as 16S rRNA and shotgun metagenomics. However, while profiling the 16S rRNA gene is a common method to provide an estimate of microbial diversity and abundance, it cannot directly produce functional information. In contrast, shotgun metagenomics reveal functional insights, but their cost can be exorbitant for large scale studies. Recently, tools such as PICRUSt2 and Tax4Fun2 have been developed to predict functional information generated from expected gene counts of 16S rRNA amplicon data. While increasingly popular in microbial ecology studies, only a few benchmarking studies have tested their performance, and none focused thoroughly on microbial mat communities. In this study, we tested prediction tools with shotgun metagenomics on microbial mat communities occurring in hypersaline lakes from Rottnest Island (WA). Shotgun and amplicon sequencing showed comparable functional profiles, although the prediction tools significantly underestimated gene counts for several metabolic pathways. Additionally, the combination of Tax4Fun2 with shotgun-derived profiles showed increased accuracy in functional prediction. Our results indicate that initial shotgun sequencing of the communities remains the best analysis for detailed and small-scale studies, while combination of amplicon and shotgun approaches can provide a valid cost-effective alternative for medium- to long-term biomonitoring studies. Further studies targeting other microbial communities will help untangle the real potential of these tools.

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