The impact of sequencing depth on the inferred taxonomic composition and AMR gene content of metagenomic samples.
The impact of sequencing depth on the inferred taxonomic composition and AMR gene content of metagenomic samples.
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DOI：
10.1186/s40793-019-0347-1
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发表时间：
2019-10-24
影响因子：
7.9
通讯作者：
REHAB consortium
中科院分区：
文献类型：
--
作者：
Gweon HS;Shaw LP;Swann J;De Maio N;AbuOun M;Niehus R;Hubbard ATM;Bowes MJ;Bailey MJ;Peto TEA;Hoosdally SJ;Walker AS;Sebra RP;Crook DW;Anjum MF;Read DS;Stoesser N;REHAB consortium
鸟枪法宏基因组学越来越多地用于表征微生物群落，特别是用于研究不同动物和环境背景下的抗菌素耐药性 (AMR)。有许多不同的方法可以从鸟枪法宏基因组数据推断复杂群落样本的分类组成和 AMR 基因含量，但为这些样本建立最佳测序深度、数据处理和分析方法的工作却很少。在这项研究中，我们使用鸟枪法宏基因组学和对来自同一样本的培养分离株进行测序来解决这些问题。我们对三个潜在的环境 AMR 基因库（猪盲肠、河流沉积物、污水）进行了采样，并使用鸟枪法宏基因组学对样本进行了高深度测序（每个样本约 2 亿个读数）。除此之外，我们从相同的样品中培养了肠杆菌科细菌的单菌落分离株，并使用混合测序（短读长和长读长）来创建高质量的组装件，以便与宏基因组数据进行比较。为了自动化数据处理，我们开发了一个开源软件管道“ResPipe”。分类学分析对测序深度的影响比 AMR 基因内容稳定得多。每个样本 100 万次读取足以实现与完整分类组成的 < 1% 差异。然而，每个样品至少需要 8000 万次读取才能恢复样品中存在的不同 AMR 基因家族的全部丰富性，并且在每个样品 2 亿次读取的流出物中仍然发现了额外的 AMR 基因等位基因多样性。使用基因长度和嗜热栖热菌 DNA 的外源尖峰对映射到 AMR 基因的读数进行标准化，极大地改变了估计的基因丰度分布。虽然使用鸟枪法宏基因组学可以从废水中培养的分离株中恢复大部分基因组含量，但猪盲肠或河流沉积物的情况并非如此。测序深度和分析方法可以严重影响鸟枪法宏基因组学对多种微生物动物和环境样本的分析。培养分离株的测序和鸟枪法宏基因组学都可以恢复使用其他方法无法识别的实质性多样性。通过将宏基因组读数映射到数据库来推断 AMR 基因含量或存在时，需要特别考虑。 ResPipe 是我们开发的开源软件管道，可免费使用 (https://gitlab.com/hsgweon/ResPipe)。
Shotgun metagenomics is increasingly used to characterise microbial communities, particularly for the investigation of antimicrobial resistance (AMR) in different animal and environmental contexts. There are many different approaches for inferring the taxonomic composition and AMR gene content of complex community samples from shotgun metagenomic data, but there has been little work establishing the optimum sequencing depth, data processing and analysis methods for these samples. In this study we used shotgun metagenomics and sequencing of cultured isolates from the same samples to address these issues. We sampled three potential environmental AMR gene reservoirs (pig caeca, river sediment, effluent) and sequenced samples with shotgun metagenomics at high depth (~ 200 million reads per sample). Alongside this, we cultured single-colony isolates of Enterobacteriaceae from the same samples and used hybrid sequencing (short- and long-reads) to create high-quality assemblies for comparison to the metagenomic data. To automate data processing, we developed an open-source software pipeline, ‘ResPipe’. Taxonomic profiling was much more stable to sequencing depth than AMR gene content. 1 million reads per sample was sufficient to achieve < 1% dissimilarity to the full taxonomic composition. However, at least 80 million reads per sample were required to recover the full richness of different AMR gene families present in the sample, and additional allelic diversity of AMR genes was still being discovered in effluent at 200 million reads per sample. Normalising the number of reads mapping to AMR genes using gene length and an exogenous spike of Thermus thermophilus DNA substantially changed the estimated gene abundance distributions. While the majority of genomic content from cultured isolates from effluent was recoverable using shotgun metagenomics, this was not the case for pig caeca or river sediment. Sequencing depth and profiling method can critically affect the profiling of polymicrobial animal and environmental samples with shotgun metagenomics. Both sequencing of cultured isolates and shotgun metagenomics can recover substantial diversity that is not identified using the other methods. Particular consideration is required when inferring AMR gene content or presence by mapping metagenomic reads to a database. ResPipe, the open-source software pipeline we have developed, is freely available (https://gitlab.com/hsgweon/ResPipe).
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开放式细菌种群基因组学：BIGSDB软件，pubmlst.org网站及其应用。
影响因子：
--
作者：
Jolley KA;Bray JE;Maiden MCJ
通讯作者：
Maiden MCJ
影响因子：
4.3
作者：
Kaminski J;Gibson MK;Franzosa EA;Segata N;Dantas G;Huttenhower C
通讯作者：
Huttenhower C
影响因子：
15.5
作者：
Pal C;Bengtsson-Palme J;Kristiansson E;Larsson DG
通讯作者：
Larsson DG
影响因子：
3.2
作者：
Logan, Latania K.;Braykov, Nikolay P.;Laxminarayan, Ramanan
通讯作者：
Laxminarayan, Ramanan
影响因子：
11
作者：
Li, Bing;Yang, Ying;Zhang, Tong
通讯作者：
Zhang, Tong