Comparing Apples and Oranges?: Next Generation Sequencing and Its Impact on Microbiome Analysis.
Comparing Apples and Oranges?: Next Generation Sequencing and Its Impact on Microbiome Analysis.
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DOI：
10.1371/journal.pone.0148028
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发表时间：
2016
期刊：
影响因子：
3.7
通讯作者：
Claesson MJ
中科院分区：
文献类型：
--
作者：
Clooney AG;Fouhy F;Sleator RD;O' Driscoll A;Stanton C;Cotter PD;Claesson MJ
测序技术的快速进步沿着成本的下降，为微生物组研究提供了广泛的机会。这些令人印象深刻的技术发展伴随着方法变量数量的大幅增长，包括采样，存储，DNA提取，引物对，测序技术，化学版本，读长，插入大小和分析管道等。这种变异性的增加有可能损害所进行研究的再现性和可比性。在这里，我们进行了第一次报道的研究比较扩增和鸟枪测序的三个领先的下一代测序技术。使用Illumina HiSeq、MiSeq和Ion PGM鸟枪测序以及跨两个可变16S rRNA基因区域的扩增子测序，将这些应用于六个人粪便样品。值得注意的是，我们发现导致微生物群组成差异最大的因素是所选择的方法，而不是自然的个体间差异，这通常是微生物群研究中最重要的驱动因素之一。扩增子测序在很大程度上受此影响，并且当用MiSeq对16S rRNA V1-V2区域扩增子进行测序时，该问题尤其明显。有些令人惊讶的是，鸟枪序列的分类分箱软件的选择被证明是至关重要的，甚至比测序技术和扩增子的选择更大的区分能力。对于每个样品1000万个读数，获得了HiSeq的最佳N50组装值，而所应用的MiSeq和PGM测序深度证明不足以对粪便样品进行鸟枪测序。另一方面，后一种技术为功能基因分类提供了更好的基础，可能是由于其较长的读取长度。因此，除了强调方法偏差外，本研究还展示了与比较使用不同策略生成的数据相关的风险。我们还建议对某些微生物特别感兴趣的实验室应优化其方案，以使用不同的技术准确检测这些类群。
Rapid advancements in sequencing technologies along with falling costs present widespread opportunities for microbiome studies across a vast and diverse array of environments. These impressive technological developments have been accompanied by a considerable growth in the number of methodological variables, including sampling, storage, DNA extraction, primer pairs, sequencing technology, chemistry version, read length, insert size, and analysis pipelines, amongst others. This increase in variability threatens to compromise both the reproducibility and the comparability of studies conducted. Here we perform the first reported study comparing both amplicon and shotgun sequencing for the three leading next-generation sequencing technologies. These were applied to six human stool samples using Illumina HiSeq, MiSeq and Ion PGM shotgun sequencing, as well as amplicon sequencing across two variable 16S rRNA gene regions. Notably, we found that the factor responsible for the greatest variance in microbiota composition was the chosen methodology rather than the natural inter-individual variance, which is commonly one of the most significant drivers in microbiome studies. Amplicon sequencing suffered from this to a large extent, and this issue was particularly apparent when the 16S rRNA V1-V2 region amplicons were sequenced with MiSeq. Somewhat surprisingly, the choice of taxonomic binning software for shotgun sequences proved to be of crucial importance with even greater discriminatory power than sequencing technology and choice of amplicon. Optimal N50 assembly values for the HiSeq was obtained for 10 million reads per sample, whereas the applied MiSeq and PGM sequencing depths proved less sufficient for shotgun sequencing of stool samples. The latter technologies, on the other hand, provide a better basis for functional gene categorisation, possibly due to their longer read lengths. Hence, in addition to highlighting methodological biases, this study demonstrates the risks associated with comparing data generated using different strategies. We also recommend that laboratories with particular interests in certain microbes should optimise their protocols to accurately detect these taxa using different techniques.