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Coassembly and binning of a twenty-year metagenomic time-series from Lake Mendota

Published in:

Scientific Data 11(1) , 966 ( 2024)

Author(s):

Oliver, Tiffany, Varghese, Neha, Roux, Simon, Schulz, Frederik, Huntemann, Marcel, Clum, Alicia, Foster, Brian, Foster, Bryce, Riley, Robert, LaButti, Kurt, Egan, Robert, Hajek, Patrick, Mukherjee, Supratim, Ovchinnikova, Galina, Reddy, T. B. K., Calhoun, Sara, Hayes, Richard D., Rohwer, Robin R., Zhou, Zhichao, Daum, Chris, Copeland, Alex, Chen, I-Min A., Ivanova, Natalia N., Kyrpides, Nikos C., Mouncey, Nigel J., del Rio, Tijana Glavina, Grigoriev, Igor V., Hofmeyr, Steven, Oliker, Leonid, Yelick, Katherine, Anantharaman, Karthik, McMahon, Katherine D., Woyke, Tanja, Eloe-Fadrosh, Emiley A.

DOI:

10.1038/s41597-024-03826-8

Abstract:

The North Temperate Lakes Long-Term Ecological Research (NTL-LTER) program has been extensively used to improve understanding of how aquatic ecosystems respond to environmental stressors, climate fluctuations, and human activities. Here, we report on the metagenomes of samples collected between 2000 and 2019 from Lake Mendota, a freshwater eutrophic lake within the NTL-LTER site. We utilized the distributed metagenome assembler MetaHipMer to coassemble over 10 terabases (Tbp) of data from 471 individual Illumina-sequenced metagenomes. A total of 95,523,664 contigs were assembled and binned to generate 1,894 non-redundant metagenome-assembled genomes (MAGs) with ≥50% completeness and ≤10% contamination. Phylogenomic analysis revealed that the MAGs were nearly exclusively bacterial, dominated by Pseudomonadota (Proteobacteria, N = 623) and Bacteroidota (N = 321). Nine eukaryotic MAGs were identified by eukCC with six assigned to the phylum Chlorophyta. Additionally, 6,350 high-quality viral sequences were identified by geNomad with the majority classified in the phylum Uroviricota. This expansive coassembled metagenomic dataset provides an unprecedented foundation to advance understanding of microbial communities in freshwater ecosystems and explore temporal ecosystem dynamics.

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