走向分类学共识：从大规模rRNA基因分析推断多样性和丰富性
Towards a Taxonomical Consensus: Diversity and Richness Inference from Large Scale rRNA gene Analysis
摘要：
种群分析一直具有挑战性，但重要的是，导致确定微生物群落成员的多样性和功能预测。在这里，我们详细介绍了我们的生物信息学方法，用于分析大型微生物群落的种群分布和多样性。这是通过以下方式实现的：（i）基于同源性的方法，用于稳健的多态性类型确定，等同于核糖体数据库项目（RDP）分类器的分类准确度，但提供了密切相关序列的改进的关联;（ii）比较不同的聚类方法，用于实现更准确的丰富度估计。我们的方法，我们开发的RDP审查16 S rRNA基因序列集，通过测试它对一个大的16 S rRNA基因数据集的约2300个序列，我们从土壤微生物群落研究。我们的结论是，最好的方法，以获得准确的微生物群落的遗传学概况的基础上，16 S rRNA基因序列信息，是应用优化的爆破分类。这种方法得到了使用完整连锁聚类对密切相关序列进行分组的补充，以计算群落的丰富度和均匀度指数。
英文摘要：
Population analysis is persistently challenging but important, leading to the determination of diversity and function prediction of microbial community members. Here we detail our bioinformatics methods for analyzing population distribution and diversity in large microbial communities. This was achieved via (i) a homology based method for robust phylotype determination, equaling the classification accuracy of the Ribosomal Database Project (RDP) classifier, but providing improved associations of closely related sequences; (ii) a comparison of different clustering methods for achieving more accurate richness estimations. Our methodology, which we developed using the RDP vetted 16S rRNA gene sequence set, was validated by testing it on a large 16S rRNA gene dataset of approximately 2300 sequences, which we obtained from a soil microbial community study. We concluded that the best approach to obtain accurate phylogenetics profile of large microbial communities, based on 16S rRNA gene sequence information, is to apply an optimized blast classifier. This approach is complemented by the grouping of closely related sequences, using complete linkage clustering, in order to calculate richness and evenness indices for the communities.
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