Sequential co-assembly reduces computational resources and errors in metagenome-assembled genomes
Year: 2025
Type: article
Abstract: Generating metagenome-assembled genomes from DNA shotgun sequencing datasets can demand considerable computational resources. Here, we describe a sequential co-assembly method that reduces the assembly of duplicate reads through successive application of single-node computing tools for read assembly and mapping. Using a simulated mouse microbiome DNA shotgun sequencing dataset, we demonstrated that this approach shortens assembly time, uses less memory than traditional co-assembly, and produces significantly fewer assembly errors. Applying sequential co-assembly to shotgun sequencing reads from (1) a longitudinal study of gut microbiomes from undernourished Bangladeshi children and (2) a 2.3-terabyte dataset generated from gnotobiotic mice colonized with pooled microbiomes from these children that was too large to be handled by a traditional co-assembly approach also demonstrated significant reductions in assembly time and memory requirements. These results suggest that this approach should be useful in resource-constrained settings, including in low- and middle-income countries.
Source: Cell Reports Methods
Authors: Hannah M. Lynn, Jeffrey I. Gordon
Institution: Washington University in St. Louis
Language: English
Subfield: Molecular Biology
Domain: Life Sciences
Open Access status: gold
Estimated APC paid: $4,824
Funders: Bill and Melinda Gates Foundation, Washington University in St. Louis, University of Washington, National Institutes of Health, Washington University School of Medicine in St. Louis, National Institute of General Medical Sciences, National Institute of Diabetes and Digestive and Kidney Diseases, Office of Extramural Research, National Institutes of Health
OpenAlex ID: W4408510501