MiCoP: microbial community profiling method for detecting viral and fungal organisms in metagenomic samples
MiCoP: microbial community profiling method for detecting viral and fungal organisms in metagenomic samples
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DOI：
10.1186/s12864-019-5699-9
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发表时间：
2019-06-06
期刊：
影响因子：
4.4
通讯作者：
Eskin, Eleazar
中科院分区：
文献类型：
--
作者：
LaPierre, Nathan;Mangul, Serghei;Eskin, Eleazar
背景：高通量测序促进了宏基因组学的发展，宏基因组学涉及对土壤、海水和人体等各种环境中微生物群落的直接分析。许多基于标记基因或 k 聚体的现有方法的灵敏度有限，或者对许多用户来说计算量太大。此外，宏基因组学的大多数工作都集中在细菌和古细菌上，而忽略了对病毒和真核生物等其他关键微生物的研究。结果：在这里，我们提出了一种方法，MiCoP（微生物组群落分析），该方法使用读段的快速映射来构建病毒和真核生物全基因组的综合参考数据库，以实现最大的读段利用率，并能够分析每个样本中的病毒组和真核组。我们证明宏基因组读数的映射对于较小的病毒和真核参考数据库是可行的。我们证明，我们的方法在模拟和模拟群落数据上是准确的，并且比之前报告的人类微生物组项目真实数据的结果识别出更多的病毒和真菌物种。 结论：MiCoP 是一种基于图谱的方法，在宏基因组样本中病毒和真核生物的丰度分析方面比现有方法更有效。 MiCoP 可用于检测这些社区的全部多样性。
Background: High throughput sequencing has spurred the development of metagenomics, which involves the direct analysis of microbial communities in various environments such as soil, ocean water, and the human body. Many existing methods based on marker genes or k-mers have limited sensitivity or are too computationally demanding for many users. Additionally, most work in metagenomics has focused on bacteria and archaea, neglecting to study other key microbes such as viruses and eukaryotes.Results: Here we present a method, MiCoP (Microbiome Community Profiling), that uses fast-mapping of reads to build a comprehensive reference database of full genomes from viruses and eukaryotes to achieve maximum read usage and enable the analysis of the virome and eukaryome in each sample. We demonstrate that mapping of metagenomic reads is feasible for the smaller viral and eukaryotic reference databases. We show that our method is accurate on simulated and mock community data and identifies many more viral and fungal species than previously-reported results on real data from the Human Microbiome Project.Conclusions: MiCoP is a mapping-based method that proves more effective than existing methods at abundance profiling of viruses and eukaryotes in metagenomic samples. MiCoP can be used to detect the full diversity of these communities.