The emergence of metagenomics-based approaches in biology has overcome historical culture-based biases in microbiological studies. This has also enabled a more comprehensive assessment of the microbial ecology of environmental samples. The subsequent development of next-generation sequencing technologies, able to produce hundreds of millions of sequences at improved cost and speed, necessitated a computational shift from user-supervised alignment and analysis pipelines, that were used previously for vector-based metagenomic studies that relied on Sanger sequencing. Current computational advances have expanded the scope of microbial biogeography studies and offered novel insights into microbial responses to environmental variation and anthropogenic inputs into ecosystems. However, new biostatistical and computational approaches are required to handle the large volume and complexity of these new multivariate datasets. While this has allowed more complete characterization of taxonomic, phylogenetic and functional microbial diversity, these tools are still limited by methodological biases, incomplete databases, and the high cost of fully characterizing environmental biodiversity. This review addresses the evolution of methods to monitor surface waters and characterize environmental samples through the recent computational advances in metagenomics, with an emphasis on the study of surface waters. These new methods have provided an abundance of opportunities to expand our understanding of the interaction between microbial communities and public health. Specifically, they have allowed for comprehensive monitoring of bacterial communities in surface waters for changes in community structure associated with faecal contamination and the presence of human pathogens, rather than relying on only a few indicator bacteria to direct public health concerns.
|Original language||English (US)|
|Number of pages||9|
|Journal||Journal of the Marine Biological Association of the United Kingdom|
|State||Published - Jan 1 2016|
- 16S rDNA
- environmental samples
- next-generation sequencing