Li Xin, Hu Haiyan, Li Xiaoman
Department of Computer Science.
Burnett School of Biomedical Science, College of Medicine, University of Central Florida, Orlando, FL 32816, USA.
Bioinformatics. 2021 May 1;37(4):575-577. doi: 10.1093/bioinformatics/btaa728.
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.
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.
The source code and tool mixtureS is available at http://www.cs.ucf.edu/˜xiaoman/mixtureS/.
Supplementary data are available at Bioinformatics online.
研究环境样本中的细菌菌株至关重要。现有方法和工具通常依赖已知菌株或已知变异，无法处理单个样本，不可靠或不易使用等。因此，开发更用户友好的工具以更准确地识别细菌菌株非常重要。
我们开发了一种名为mixtureS的新工具，它可以从克隆或宏基因组样本的鸟枪法测序读段中从头识别细菌菌株，而无需事先了解菌株及其变异情况。在243个模拟数据集和195个实验数据集上进行测试，mixtureS能够可靠地识别菌株、它们的数量及其丰度。与三种工具相比，mixtureS在几乎所有模拟数据集和绝大多数实验数据集中都表现出更好的性能。
mixtureS的源代码和工具可在http://www.cs.ucf.edu/˜xiaoman/mixtureS/获取。
补充数据可在《生物信息学》在线获取。
