In nature microorganisms live in complex communities, interacting with plants animals, fungi and other microbes. Most of these microbes cannot easily be grown in the laboratory. Metagenomic methods overcome this limitation by extracting DNA or RNA from entire assemblages of microorganisms and sequencing them simultaneously.
The metagenome program provides three major classes of data products through our calls for Scientific Proposals. Community profiling, metagenomes and metatranscriptomes help users understand the community structure, genomic content and function of microbial communities. The program also provides users with advanced online tools for the analysis comparison of their data in the context of environmental metadata.
|Metatranscriptomes||Sequencing RNA from the environment|
|Metagenomes||Sequencing of DNA from the environment|
|Community profiling (iTags)||Sequencing of selected phylogenetic marker genes amplified by PCR.|
|16S||For Bacteria and Archaea. The V4-V5 region of the 16S rRNA gene.|
|18S||For Eukaryotes. The V4 region of the 18S rRNA gene.|
|ITS||For Fungi. The internally transcribed spacer region 2 (Between the 5.8S and the 28S)|
Sample Submission Reference Guides:
- Planning DOE JGI Metagenome Program Sequencing Requests
- DOE JGI Updates to Amplicon (16S rRNA) Sequencing
- iTagger-methods Update
The Metagenome Program major strategic efforts include:
- Terrestrial Carbon Cycling: These include the Great Prairie Grand Challenge Project; the San Francisco Bay/Delta Wetlands Project; Arctic Tundra virome project; and Soil and Peatlands Community Science Program (CSP) Projects.
- Plant-Microbe Interactions: These include the studies of the rhizosphere and endosphere of Arabidopsis, Poplar and Maize, and a project focused on root-enhanced decomposition.
- Technology Development & Data Analysis: Several projects target improved metagenome assembly and genome binning, data interpretation via metagenome visualization, identification of protein and operational taxonomic unit (OTU) clusters, the development of machine learning methods for metagenomics, and viral metagenomics.