Ecosystem-specific microbiota and microbiome databases in the era of big data
|Author(s)||Lobanov Victor1, Gobet Angélique2, Joyce Alyssa1|
|Affiliation(s)||1 : Department of Marine Sciences, University of Gothenburg, Box 461, 405 30, Gothenburg, Sweden
2 : MARBEC, Univ Montpellier, CNRS, Ifremer, IRD, Sète, France
|Source||Environmental Microbiome (2524-6372) (Springer Science and Business Media LLC), 2022-07 , Vol. 17 , N. 1 , P. 37 (17p.)|
|WOS© Times Cited||6|
|Keyword(s)||Community ecology, Meta-omics, Ecosystem-specific database, Data curation, Database management, Microbiota, Microbiome|
The rapid development of sequencing methods over the past decades has accelerated both the potential scope and depth of microbiota and microbiome studies. Recent developments in the field have been marked by an expansion away from purely categorical studies towards a greater investigation of community functionality. As in-depth genomic and environmental coverage is often distributed unequally across major taxa and ecosystems, it can be difficult to identify or substantiate relationships within microbial communities. Generic databases containing datasets from diverse ecosystems have opened a new era of data accessibility despite costs in terms of data quality and heterogeneity. This challenge is readily embodied in the integration of meta-omics data alongside habitat-specific standards which help contextualise datasets both in terms of sample processing and background within the ecosystem. A special case of large genomic repositories, ecosystem-specific databases (ES-DB’s), have emerged to consolidate and better standardise sample processing and analysis protocols around individual ecosystems under study, allowing independent studies to produce comparable datasets. Here, we provide a comprehensive review of this emerging tool for microbial community analysis in relation to current trends in the field. We focus on the factors leading to the formation of ES-DB’s, their comparison to traditional microbial databases, the potential for ES-DB integration with meta-omics platforms, as well as inherent limitations in the applicability of ES-DB’s.