Environmental Genomics Group

Our research focuses on the assembly of metagenomic sequence data into microbial genomes, which is of fundamental value to improving our understanding of microbial ecology and metabolism by elucidating the functional potential of hard-to-culture microorganisms. This process enables our users to explore the genetic makeup of microbial communities, shedding light on their roles in various environments and their potential applications. By recovering population genomes from metagenomes, we gain insights into microbial interactions, evolution, and adaptation, ultimately informing strategies for environmental monitoring, bioremediation, and the discovery of novel enzymes and bioactive compounds.

qSIP quantifies incorporation of isotopes from labeled nutrients into biomass. When paired with metagenomics, qSIP can decipher active microbial functions in situ. This methodology builds on the framework of Hungate and colleagues, to quantify heavy isotope incorporation into DNA by active microbial populations (Hungate et al. 2015). With the development of high-throughput SIP (Nuccio et al. 2022) and terabase-scale metagenome combined assemblies (Hofmeyr et al. 2020) we are able to quantify isotope incorporation across a range of environmental conditions through metagenome-assembled genomes and other genomic elements (Vyshenska et al. 2023).

Our research focuses on the implementation of prokaryotic systematics across cultured microbes and recovered population genomes using Average Nucleotide Identity (ANI), which provides a computationally effective method of understanding the microbial makeup of environmental samples. As ANI provides a measure of genomic similarity between two genomes,  we use it to identify and classify bacteria and archaea as well as gain insights into the evolutionary relationships and divergence of these organisms.

Profile HMM search across all IMG metagenomes, capturing sequence diversity for proteins of interest.

In an effort to gain better understanding of complex ecosystems within hard to sequence and assemble samples, such as those from soil and plants, our group researches the use and implementation of machine learning approaches and AI based tools to carry out explorative analysis.

Our efforts focus on harnessing the computational resources available at NERSC and other compute systems to allow the rapid processing and analysis of large amounts of genomic data. In particular we focus on the integration and comparison of thousands of metagenomic datasets, to enable our users to identify patterns and trends across different environments and samples.

• Kellom M, Berg M, Chen IA, Chu K, Clum A, Huntemann M, Ivanova NN, Kyrpides NC, Mukherjee S, Reddy TBK, Roux S, Seshadri R, Szabo G, Varghese NJ, Woyke T, Eloe-Fadrosh EA.0. Tetranucleotide frequencies differentiate genomic boundaries and metabolic strategies across environmental microbiomes. mSystems0:e01744-24. https://doi.org/10.1128/msystems.01744-24
• Oliver, T., Varghese, N., Roux, S. et al. Coassembly and binning of a twenty-year metagenomic time-series from Lake Mendota. Sci Data 11, 966 (2024). https://doi.org/10.1038/s41597-024-03826-8
• Nayfach, S., Roux, S., Seshadri, R. et al. A genomic catalog of Earth’s microbiomes. Nat Biotechnol 39, 499–509 (2021). https://doi.org/10.1038/s41587-020-0718-6
• I-Min A Chen, Ken Chu, Krishnaveni Palaniappan, Anna Ratner, Jinghua Huang, Marcel Huntemann, Patrick Hajek, Stephan J Ritter, Cody Webb, Dongying Wu, Neha J Varghese, T B K Reddy, Supratim Mukherjee, Galina Ovchinnikova, Matt Nolan, Rekha Seshadri, Simon Roux, Axel Visel, Tanja Woyke, Emiley A Eloe-Fadrosh, Nikos C Kyrpides, Natalia N Ivanova, The IMG/M data management and analysis system v.7: content updates and new features, Nucleic Acids Research, Volume 51, Issue D1, 6 January 2023, Pages D723–D732, https://doi.org/10.1093/nar/gkac976
• Roux, S., Krupovic, M., Daly, R.A. et al. Cryptic inoviruses revealed as pervasive in bacteria and archaea across Earth’s biomes. Nat Microbiol 4, 1895–1906 (2019). https://doi.org/10.1038/s41564-019-0510-x