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- Application of computational approaches to analyze metagenomic data
- Ho-Jin Gwak , Seung Jae Lee , Mina Rho
- J. Microbiol. 2021;59(3):233-241. Published online February 10, 2021
- DOI: https://doi.org/10.1007/s12275-021-0632-8
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- 11 Citations
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Abstract
- Microorganisms play a vital role in living systems in numerous
ways. In the soil or ocean environment, microbes are
involved in diverse processes, such as carbon and nitrogen
cycle, nutrient recycling, and energy acquisition. The relation
between microbial dysbiosis and disease developments has
been extensively studied. In particular, microbial communities
in the human gut are associated with the pathophysiology
of several chronic diseases such as inflammatory bowel disease
and diabetes. Therefore, analyzing the distribution of microorganisms
and their associations with the environment
is a key step in understanding nature. With the advent of nextgeneration
sequencing technology, a vast amount of metagenomic
data on unculturable microbes in addition to culturable
microbes has been produced. To reconstruct microbial
genomes, several assembly algorithms have been developed
by incorporating metagenomic features, such as uneven
depth. Since it is difficult to reconstruct complete microbial
genomes from metagenomic reads, contig binning approaches
were suggested to collect contigs that originate from the same
genome. To estimate the microbial composition in the environment,
various methods have been developed to classify
individual reads or contigs and profile bacterial proportions.
Since microbial communities affect their hosts and environments
through metabolites, metabolic profiles from metagenomic
or metatranscriptomic data have been estimated.
Here, we provide a comprehensive review of computational
methods
that can be applied to investigate microbiomes using metagenomic and metatranscriptomic sequencing data. The limitations of metagenomic studies and the key approaches to overcome such problems are discussed.
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