New approach allows for detection of low-abundance bacterial strains in large metagenomic datasets
By Broad Communications
September 24, 2015
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 Broad Institute, 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. Their paper can be found in Nature Biotechnology.