How does metagenomic sequencing differ from whole-genome sequencing (WGS)?

Metagenomic Sequencing

Metagenomic sequencing and whole-genome sequencing (WGS) are distinct techniques, but they can generate overlapping types of data depending on the experimental setup. Here’s a breakdown of the differences and similarities:

Metagenomics is ideal for studying microbial communities in situ, where multiple organisms interact for example in gut samples, wastewater, soil or water.

• Definition of Metagenomic Sequencing:

• Metagenomics refers to the sequencing of genetic material from a mixed community of organisms (e.g., microorganisms in soil, water, wastewater or gut samples). It provides information on the collective genomes present in the sample.

• Purpose of Metagenomic Sequencing:

• Metagenomic Sequencing identifies the diversity of organisms (microbial community composition) in a sample.

• It analyzes functional genes and pathways in the community.

• It also detects unculturable organisms in environmental samples.

• Data Output of Metagenomic Sequencing:

• Taxonomic profiles: Identifies organisms in the community (e.g., species, genus).

• Functional profiles: Detects genes or metabolic pathways shared across the community.

• Applications of Metagenomic Sequencing:

• Metagenomic Sequencing is used for studying microbial communities in complex environments.

• It used for monitoring microbiome changes due to antibiotics or environmental changes.

• Metagenomic Sequencing is used for identifying novel genes or pathways.

• Challenges of Metagenomic Sequencing:

• Data is often fragmented, as sequencing reads may come from multiple organisms.

• Reconstructing complete genomes (metagenome-assembled genomes, or MAGs) can be computationally intensive and incomplete.

Whole-Genome Sequencing (WGS)

WGS is more suitable when the goal is to study the genetic makeup of a single organism in detail for example to investigate antimicrobial resistance genes (ARGs) or a mutation in the genome.

• Definition of WGS:

• WGS focuses on sequencing the entire genome of a single organism (cultured isolate) to generate a complete genome sequence.

• Purpose of WGS:

• WGS provides a detailed map of a specific organism’s genome.

• It allows for analysis of mutations, gene content, and regulatory elements.

• Data Output of WGS:

• A single, complete genome sequence (or nearly complete with advanced assembly tools).

• It enables detailed genomic comparisons between strains or species.

• Applications of WGS:

• WGS identifies genetic variants, virulence factors, or antimicrobial resistance genes (ARGs).

• It is applied in phylogenetic studies.

• WGS is applied in genome editing or synthetic biology.

• Challenges of WGS:

• It requires pure cultures of organisms for sequencing.

• It may not be feasible for unculturable species.

Comparison of Results from metagenomic sequencing and WGS

• Overlap:

• Both methods can identify antimicrobial resistance genes (ARGs), virulence factors, and metabolic pathways, though metagenomics does so across a community and WGS for a single organism.

• If metagenomic sequencing resolves the genome of a dominant species, the results may resemble a WGS dataset for that organism.

• Differences between metagenomic sequencing and WGS:

• Resolution: Metagenomics covers community-level data, while WGS focuses on individual genomes.

• Complexity: Metagenomics can provide insights into interactions among community members, which WGS cannot.

• Completeness: WGS generates a complete genome; metagenomics may provide fragmented sequences unless the data is deeply analyzed.