Department of Computer Science, College of Medicine, University of Central Florida, Orlando, FL 32816, USA.
Burnett School of Biomedical Science, College of Medicine, University of Central Florida, Orlando, FL 32816, USA.
Bioinformatics. 2019 Nov 1;35(22):4624-4631. doi: 10.1093/bioinformatics/btz280.
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.
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.
The BHap tool is available at http://www.cs.ucf.edu/∼xiaoman/BHap/.
Supplementary data are available at Bioinformatics online.
细菌单倍型重建对于选择未知单倍型引起的疾病的适当治疗方法至关重要。现有的方法和工具在这项任务上效果不佳，因为它们通常是为病毒而不是细菌群体开发的。
在这项研究中，我们开发了一种基于模糊流网络的新算法 BHap，用于从下一代测序数据中重建细菌单倍型。在模拟和实验数据集上的测试表明，BHap 能够以平均 F1 得分为 0.87、平均精度为 0.87 和平均召回率为 0.88 的水平重建细菌群体的单倍型。我们还证明，BHap 对测序错误具有较低的敏感性，能够重建覆盖率较低的单倍型，并能够处理广泛的突变率。与现有方法相比，BHap 在更高的 F1 得分、更好的精度、更好的召回率以及更准确地估计单倍型数量方面表现更为出色。
BHap 工具可在 http://www.cs.ucf.edu/∼xiaoman/BHap/ 获得。
补充数据可在 Bioinformatics 在线获得。
