BHap: a novel approach for bacterial haplotype reconstruction.

MOTIVATION: The bacterial haplotype reconstruction is critical for selecting proper treatments for diseases caused by unknown haplotypes. Existing methods and tools do not work well on this task, because they are usually developed for viral instead of bacterial populations.

RESULTS: In this study, we developed BHap, a novel algorithm based on fuzzy flow networks, for reconstructing bacterial haplotypes from next generation sequencing data. Tested on simulated and experimental datasets, we showed that BHap was capable of reconstructing haplotypes of bacterial populations with an average F1 score of 0.87, an average precision of 0.87, and an average recall of 0.88. We also demonstrated that BHap had a low susceptibility to sequencing errors, was capable of reconstructing haplotypes with low coverage, and could handle a wide range of mutation rates. Compared with existing approaches, BHap outperformed them in terms of higher F1 scores, better precision, better recall, and more accurate estimation of the number of haplotypes.

AVAILABILITY: The BHap tool is available at https://www.cs.ucf.edu/∼xiaoman/BHap/.

SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.