mixtureS: a novel tool for bacterial strain genome reconstruction from reads
Xin Li, Xin Li, Haiyan Hu, Xiaoman Li, Xiaoman Li
Abstract
MOTIVATION: It is essential to study bacterial strains in environmental samples. Existing methods and tools often depend on known strains or known variations, cannot work on individual samples, not reliable, or not easy to use, etc. It is thus important to develop more user-friendly tools that can identify bacterial strains more accurately. RESULTS: We developed a new tool called mixtureS that can de novo identify bacterial strains from shotgun reads of a clonal or metagenomic sample, without prior knowledge about the strains and their variations. Tested on 243 simulated datasets and 195 experimental datasets, mixtureS reliably identified the strains, their numbers and their abundance. Compared with three tools, mixtureS showed better performance in almost all simulated datasets and the vast majority of experimental datasets. AVAILABILITY AND IMPLEMENTATION: The source code and tool mixtureS is available at http://www.cs.ucf.edu/˜xiaoman/mixtureS/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.