Metwally Ahmed A, Dai Yang, Finn Patricia W, Perkins David L
Department of Bioengineering, University of Illinois at Chicago, Chicago, IL, United States of America.
Department of Medicine, University of Illinois at Chicago, Chicago, IL, United States of America.
PLoS One. 2016 Sep 28;11(9):e0163527. doi: 10.1371/journal.pone.0163527. eCollection 2016.
Metagenome shotgun sequencing presents opportunities to identify organisms that may prevent or promote disease. The analysis of sample diversity is achieved by taxonomic identification of metagenomic reads followed by generating an abundance profile. Numerous tools have been developed based on different design principles. Tools achieving high precision can lack sensitivity in some applications. Conversely, tools with high sensitivity can suffer from low precision and require long computation time.
In this paper, we present WEVOTE (WEighted VOting Taxonomic idEntification), a method that classifies metagenome shotgun sequencing DNA reads based on an ensemble of existing methods using k-mer-based, marker-based, and naive-similarity based approaches. Our evaluation on fourteen benchmarking datasets shows that WEVOTE improves the classification precision by reducing false positive annotations while preserving a high level of sensitivity.
WEVOTE is an efficient and automated tool that combines multiple individual taxonomic identification methods to produce more precise and sensitive microbial profiles. WEVOTE is developed primarily to identify reads generated by MetaGenome Shotgun sequencing. It is expandable and has the potential to incorporate additional tools to produce a more accurate taxonomic profile. WEVOTE was implemented using C++ and shell scripting and is available at www.github.com/aametwally/WEVOTE.
宏基因组鸟枪法测序为识别可能预防或引发疾病的生物体提供了机会。通过对宏基因组读数进行分类鉴定并生成丰度图谱来实现样本多样性分析。基于不同的设计原则已开发出众多工具。在某些应用中，实现高精度的工具可能缺乏灵敏度。相反，具有高灵敏度的工具可能存在精度低且计算时间长的问题。
在本文中，我们提出了WEVOTE（加权投票分类鉴定法），这是一种基于现有方法的集合，使用基于k-mer、基于标记和基于朴素相似度的方法对宏基因组鸟枪法测序DNA读数进行分类的方法。我们在14个基准数据集上的评估表明，WEVOTE通过减少假阳性注释提高了分类精度，同时保持了较高的灵敏度。
WEVOTE是一种高效的自动化工具，它结合了多种个体分类鉴定方法，以生成更精确和灵敏的微生物图谱。WEVOTE主要用于识别由宏基因组鸟枪法测序产生的读数。它具有可扩展性，有潜力纳入更多工具以生成更准确的分类图谱。WEVOTE使用C++和 shell脚本实现，可在www.github.com/aametwally/WEVOTE获取。
