Estimation of viral richness from shotgun metagenomes using a frequency count approach
Estimation of viral richness from shotgun metagenomes using a frequency count approach
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DOI：
10.1186/2049-2618-1-5
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发表时间：
2013-01-01
期刊：
影响因子：
15.5
通讯作者：
Stanton, Thaddeus B.
中科院分区：
文献类型：
--
作者：
Allen, Heather K.;Bunge, John;Stanton, Thaddeus B.
背景：病毒是生态系统功能的重要驱动力，但对绝大多数病毒知之甚少。病毒鸟枪宏基因组学使调查广泛的生态问题，在噬菌体社区。一个生态特征是物种丰富度，即群落中不同物种的数量。病毒没有类似于细菌16 S rRNA基因的系统发育标记来估计丰富度，因此重叠群谱被用来测量给定群落中病毒分类群的数量。通过将随机序列读段组装成重叠的序列组（重叠群）并对每个重叠群内分组的序列的数量进行计数，从病毒鸟枪法宏基因组生成重叠群谱。目前的工具，可用于分析重叠群光谱估计噬菌体丰富度是有限的，依赖于秩丰度data.Results：我们提出的统计估计重叠群光谱的病毒丰富度。使用程序CatchAll（http：//www.northeastern.edu/catalog/）根据频率计数数据而不是秩丰度来分析重叠群谱，从而使得能够进行正式的统计分析。此外，潜在的虚假低频计数对丰富度估计的影响最小化的两种方法，经验和统计。结果显示，在几乎所有的环境分析，包括猪粪便和再生淡水比以前的计算更大的估计病毒丰富度。结论：CatchAll产生了一致的估计，从相同或相似的环境中的病毒宏基因组丰富。此外，通过混合重叠群光谱分析来自不同环境的合并病毒宏基因组导致比组分宏基因组更高的丰富度估计。使用CatchAll分析重叠群光谱将通过提供丰富度的统计测量来改善对来自病毒鸟枪宏基因组的丰富度的估计，特别是来自大型数据集的丰富度。
Background: Viruses are important drivers of ecosystem functions, yet little is known about the vast majority of viruses. Viral shotgun metagenomics enables the investigation of broad ecological questions in phage communities. One ecological characteristic is species richness, which is the number of different species in a community. Viruses do not have a phylogenetic marker analogous to the bacterial 16S rRNA gene with which to estimate richness, and so contig spectra are employed to measure the number of virus taxa in a given community. A contig spectrum is generated from a viral shotgun metagenome by assembling the random sequence reads into groups of sequences that overlap (contigs) and counting the number of sequences that group within each contig. Current tools available to analyze contig spectra to estimate phage richness are limited by relying on rank-abundance data.Results: We present statistical estimates of virus richness from contig spectra. The program CatchAll (http://www.northeastern.edu/catchall/) was used to analyze contig spectra in terms of frequency count data rather than rankabundance, thus enabling formal statistical analyses. Also, the influence of potentially spurious low-frequency counts on richness estimates was minimized by two methods, empirical and statistical. The results show greater estimates of viral richness than previous calculations in nearly all environments analyzed, including swine feces and reclaimed fresh water.Conclusions: CatchAll yielded consistent estimates of richness across viral metagenomes from the same or similar environments. Additionally, analysis of pooled viral metagenomes from different environments via mixed contig spectra resulted in greater richness estimates than those of the component metagenomes. Using CatchAll to analyze contig spectra will improve estimations of richness from viral shotgun metagenomes, particularly from large datasets, by providing statistical measures of richness.