An improved statistical model for taxonomic assignment of metagenomics.
An improved statistical model for taxonomic assignment of metagenomics.
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DOI：
10.1186/s12863-018-0680-1
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发表时间：
2018-10-29
期刊：
影响因子：
2.9
通讯作者：
Lee JH
中科院分区：
文献类型：
--
作者：
Yao Y;Jin Z;Lee JH
随着下一代测序技术的进步，研究人员现在可以快速检查人类及其周围环境样本的组成。为了提高宏基因组样本中分类分配的准确性，我们开发了一种方法，该方法允许来自不同基因组的多个错配概率。我们通过开发一种改进的方法扩展了宏基因组序列读数分类分配算法（TAMER），该方法可以为每个基因组设置不同的错配概率，而不是为所有基因组设置单个参数，从而获得更高的准确度。该方法，我们称之为TADIP（基于不同概率的宏基因组分类分配），在模拟和真实的数据集上进行了全面测试。实验结果表明，TADIP算法提高了TAMER算法的性能，尤其是在复杂度较高的大样本数据集上。TADIP是作为一种统计模型开发的，以提高分类分配的估计准确性。基于其可变的失配概率设置和相关方差矩阵设置，与TAMER相比，其性能在高复杂度样本中得到增强。本文的在线版本（10.1186/s12863-018-0680-1）包含补充材料，可供授权用户使用。
With the advances in the next-generation sequencing technologies, researchers can now rapidly examine the composition of samples from humans and their surroundings. To enhance the accuracy of taxonomy assignments in metagenomic samples, we developed a method that allows multiple mismatch probabilities from different genomes. We extended the algorithm of taxonomic assignment of metagenomic sequence reads (TAMER) by developing an improved method that can set a different mismatch probability for each genome rather than imposing a single parameter for all genomes, thereby obtaining a greater degree of accuracy. This method, which we call TADIP (Taxonomic Assignment of metagenomics based on DIfferent Probabilities), was comprehensively tested in simulated and real datasets. The results support that TADIP improved the performance of TAMER especially in large sample size datasets with high complexity. TADIP was developed as a statistical model to improve the estimate accuracy of taxonomy assignments. Based on its varying mismatch probability setting and correlated variance matrix setting, its performance was enhanced for high complexity samples when compared with TAMER. The online version of this article (10.1186/s12863-018-0680-1) contains supplementary material, which is available to authorized users.
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