Scalable microbial strain inference in metagenomic data using strainfacts

HIGHLIGHTS

• who: Xiangpeng Li and colleagues from the The Gladstone Institute of Data Science and Biotechnology, San Francisco, CA, United States, Department of Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, CA, United States, Department of Bioengineering and have published the research work: Scalable Microbial Strain Inference in Metagenomic Data Using StrainFacts, in the Journal: (JOURNAL)
• what: The authors show that the resulting tool, StrainFacts, can scale to tens of thousands of samples, hundreds of strains, and thousands of SNPs, opening the door to strain inference in large metagenome collections. The authors show below . . .
  

   

  

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