Detection of low-abundance bacterial strains in metagenomic datasets by eigengenome partitioning
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Author(s)
Huang, Katherine
Gevers, Dirk
Shea, Terrance
Young, Sarah
Cleary, Brian Lowman
Brito, Ilana Lauren
Alm, Eric J
Date Issued
September 2015
Journal
Nature Biotechnology
Publisher
Nature Publishing Group
Citation
Cleary, Brian et al. “Detection of Low-Abundance Bacterial Strains in Metagenomic Datasets by Eigengenome Partitioning.” Nature Biotechnology 33.10 (2015): 1053–1060.
Version
Author's final manuscript
Abstract
Analyses of metagenomic datasets that are sequenced to a depth of billions or trillions of bases can uncover hundreds of microbial genomes, but naive assembly of these data is computationally intensive, requiring hundreds of gigabytes to terabytes of RAM. We present latent strain analysis (LSA), a scalable, de novo pre-assembly method that separates reads into biologically informed partitions and thereby enables assembly of individual genomes. LSA is implemented with a streaming calculation of unobserved variables that we call eigengenomes. Eigengenomes reflect covariance in the abundance of short, fixed-length sequences, or k-mers. As the abundance of each genome in a sample is reflected in the abundance of each k-mer in that genome, eigengenome analysis can be used to partition reads from different genomes. This partitioning can be done in fixed memory using tens of gigabytes of RAM, which makes assembly and downstream analyses of terabytes of data feasible on commodity hardware. Using LSA, we assemble partial and near-complete genomes of bacterial taxa present at relative abundances as low as 0.00001%. We also show that LSA is sensitive enough to separate reads from several strains of the same species.
MIT Department
Massachusetts Institute of Technology. Computational and Systems Biology Program
Massachusetts Institute of Technology. Department of Biological Engineering
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DOI of Published Version
https://doi.org/10.1038/nbt.3329
