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Review
. 2020 Aug;6(8):mgen000409.
doi: 10.1099/mgen.0.000409. Epub 2020 Jul 24.

Metagenomic approaches in microbial ecology: an update on whole-genome and marker gene sequencing analyses

Ana Elena Pérez-Cobas  1 , Laura Gomez-Valero  1 , Carmen Buchrieser  1
Affiliations
Review

Metagenomic approaches in microbial ecology: an update on whole-genome and marker gene sequencing analyses

Ana Elena Pérez-Cobas et al. Microb Genom. 2020 Aug.

Abstract

Metagenomics and marker gene approaches, coupled with high-throughput sequencing technologies, have revolutionized the field of microbial ecology. Metagenomics is a culture-independent method that allows the identification and characterization of organisms from all kinds of samples. Whole-genome shotgun sequencing analyses the total DNA of a chosen sample to determine the presence of micro-organisms from all domains of life and their genomic content. Importantly, the whole-genome shotgun sequencing approach reveals the genomic diversity present, but can also give insights into the functional potential of the micro-organisms identified. The marker gene approach is based on the sequencing of a specific gene region. It allows one to describe the microbial composition based on the taxonomic groups present in the sample. It is frequently used to analyse the biodiversity of microbial ecosystems. Despite its importance, the analysis of metagenomic sequencing and marker gene data is quite a challenge. Here we review the primary workflows and software used for both approaches and discuss the current challenges in the field.

Keywords: marker genes; metagenomics; sequencing data analyses; whole-genome sequencing.

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Conflict of interest statement

The authors declare there are no conflicts of interest.

Figures

Fig. 1.
Fig. 1.
Schematic representation of the main steps necessary for the analysis of WGS metagenomics derived data. The software related to each step is shown in italics.
Fig. 2.
Fig. 2.
Schematic representation of the main steps necessary for the analysis of marker gene-derived data. The software related to each step is shown in italics.
Fig. 3.
Fig. 3.
Summary of the software related to alpha and beta diversity analyses and the main statistical approaches for marker gene- and WGS-derived data.
See this image and copyright information in PMC

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