Skip to Content

Metagenomics Defined

Genomics will help explain the microbial world.
July 1, 2007

This spring, the National Research Council released a report titled “The New Science of Metagenomics: Revealing the Secrets of Our Microbial Planet.” To many, the term “metagenomics” might seem abstract–after all, it does sound like “metaphysics.” So what is microbial metagenomics, and what is its relevance to the future of biology, biological engineering, and biotechnology?

Conventional genomic research on microörganisms determines the DNA sequences of individual microbes by examining cultivated strains. In metagenomics, DNA sequence information is extracted from entire microbial communities in situ. Metagenomic approaches use this bulk data to infer underlying properties of both individual microbes and microbial communities as a whole.

Metagenomics advances the understanding of complex microbial systems in several ways. Microbe cultivation efforts have failed to recover many of the microörganisms that predominate in a variety of natural and man-made settings. The ­majority of extant microbial species and their behaviors therefore represent a vast biological terra incognita. Meta­genomic approaches, which sidestep the need to purify and cultivate individual microbial strains, make it easier to retrieve genome sequence information from elusive microbial species. A second, and perhaps more important, point is that microbial species do not generally occur as single strains or pure cultures. Rather, any given microbial assemblage can consist of hundreds of different species, each one displaying significant genetic variability. The biological meaning and functional consequences of this tremendous within- and between-species biodiversity remain obscure. Metagenomic approaches enable direct assessment of community diversity and provide data sets relevant to both measuring and modeling biological processes.

Microbial communities in humans will no doubt be intensively studied using metagenomic approaches. Already, the complex interplay between human genotype and phenotype, and the associated micro­biome composition and response, is becoming clearer (see “Our Microbial Menagerie”). But other uses of metagenomics will also be important. Energy applications, including microbially produced biofuels and new processes for biomass conversion, are a good example. The study of anthropogenic effects on microbial processes that regulate the mass balance of planetary carbon and nitrogen cycles will also benefit from metagenomics.

Like the human genome sequence, the results of metagenomic analysis represent a type of “parts list” that does not fully capture the functional properties, interrelationships, and dynamics of living microbial communities. They do, however, begin to extend our analytical reach beyond the single organism. Population genomics, “community metabolism,” and genomic comparisons of different microbial communities are all now possible. We are not so far away from a systems biology that will provide a more holistic and accurate picture of the whole hierarchy of biological systems–from molecular, subcellular, and intercellular interactions to populations, communities, and ecosystems.

Ed DeLong is a professor in the Biological Engineering Division and the Department of Civil and Environmental Engineering at MIT.

Keep Reading

Most Popular

Large language models can do jaw-dropping things. But nobody knows exactly why.

And that's a problem. Figuring it out is one of the biggest scientific puzzles of our time and a crucial step towards controlling more powerful future models.

OpenAI teases an amazing new generative video model called Sora

The firm is sharing Sora with a small group of safety testers but the rest of us will have to wait to learn more.

The problem with plug-in hybrids? Their drivers.

Plug-in hybrids are often sold as a transition to EVs, but new data from Europe shows we’re still underestimating the emissions they produce.

Google DeepMind’s new generative model makes Super Mario–like games from scratch

Genie learns how to control games by watching hours and hours of video. It could help train next-gen robots too.

Stay connected

Illustration by Rose Wong

Get the latest updates from
MIT Technology Review

Discover special offers, top stories, upcoming events, and more.

Thank you for submitting your email!

Explore more newsletters

It looks like something went wrong.

We’re having trouble saving your preferences. Try refreshing this page and updating them one more time. If you continue to get this message, reach out to us at with a list of newsletters you’d like to receive.