New approach allows for detection of low-abundance bacterial strains in large metagenomic datasets
By Ó³»´«Ã½ Communications
Singling out microbes in the mountains of metagenomic data from complex samples, such as soil or seawater, is computationally intensive. To address this challenge, a team of researchers from the Ó³»´«Ã½, led by institute member and senior author Eric Alm, and graduate student and first author Brian Cleary, created a new method – latent strain analysis (LSA) – that separates sequencing reads into biologically informed partitions and enables assembly of individual genomes, including those of bacteria that are relatively low-abundance. The team also showed that LSA is sensitive enough to separate reads from several strains of the same species. can be found in Nature Biotechnology.