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

Authors :
Tiffany Oliver
Neha Varghese
Simon Roux
Frederik Schulz
Marcel Huntemann
Alicia Clum
Brian Foster
Bryce Foster
Robert Riley
Kurt LaButti
Robert Egan
Patrick Hajek
Supratim Mukherjee
Galina Ovchinnikova
T. B. K. Reddy
Sara Calhoun
Richard D. Hayes
Robin R. Rohwer
Zhichao Zhou
Chris Daum
Alex Copeland
I-Min A. Chen
Natalia N. Ivanova
Nikos C. Kyrpides
Nigel J. Mouncey
Tijana Glavina del Rio
Igor V. Grigoriev
Steven Hofmeyr
Leonid Oliker
Katherine Yelick
Karthik Anantharaman
Katherine D. McMahon
Tanja Woyke
Emiley A. Eloe-Fadrosh
Source :
Scientific Data, Vol 11, Iss 1, Pp 1-7 (2024)
Publication Year :
2024
Publisher :
Nature Portfolio, 2024.

Abstract

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.

Subjects

Subjects :
Science

Details

Language :
English
ISSN :
20524463
Volume :
11
Issue :
1
Database :
Directory of Open Access Journals
Journal :
Scientific Data
Publication Type :
Academic Journal
Accession number :
edsdoj.08c620c9493ebd2d5a49aa266009
Document Type :
article
Full Text :
https://doi.org/10.1038/s41597-024-03826-8