元宏基因组学提高基于 16S 扩增子的微生物组功能预测准确性。
Meta-Apo improves accuracy of 16S-amplicon-based prediction of microbiome function.
机构信息
Single-Cell Center, CAS Key Lab of Biofuels, Shandong Key Lab of Energy Genetics and Shandong Institute of Energy Research, Qingdao Institute of BioEnergy and Bioprocess Technology, Chinese Academy of Sciences, Qingdao, China.
College of Computer Science and Technology, Qingdao University, Qingdao, China.
出版信息
BMC Genomics. 2021 Jan 6;22(1):9. doi: 10.1186/s12864-020-07307-1.
BACKGROUND
Due to their much lower costs in experiment and computation than metagenomic whole-genome sequencing (WGS), 16S rRNA gene amplicons have been widely used for predicting the functional profiles of microbiome, via software tools such as PICRUSt 2. However, due to the potential PCR bias and gene profile variation among phylogenetically related genomes, functional profiles predicted from 16S amplicons may deviate from WGS-derived ones, resulting in misleading results.
RESULTS
Here we present Meta-Apo, which greatly reduces or even eliminates such deviation, thus deduces much more consistent diversity patterns between the two approaches. Tests of Meta-Apo on > 5000 16S-rRNA amplicon human microbiome samples from 4 body sites showed the deviation between the two strategies is significantly reduced by using only 15 WGS-amplicon training sample pairs. Moreover, Meta-Apo enables cross-platform functional comparison between WGS and amplicon samples, thus greatly improve 16S-based microbiome diagnosis, e.g. accuracy of gingivitis diagnosis via 16S-derived functional profiles was elevated from 65 to 95% by WGS-based classification. Therefore, with the low cost of 16S-amplicon sequencing, Meta-Apo can produce a reliable, high-resolution view of microbiome function equivalent to that offered by shotgun WGS.
CONCLUSIONS
This suggests that large-scale, function-oriented microbiome sequencing projects can probably benefit from the lower cost of 16S-amplicon strategy, without sacrificing the precision in functional reconstruction that otherwise requires WGS. An optimized C++ implementation of Meta-Apo is available on GitHub ( https://github.com/qibebt-bioinfo/meta-apo ) under a GNU GPL license. It takes the functional profiles of a few paired WGS:16S-amplicon samples as training, and outputs the calibrated functional profiles for the much larger number of 16S-amplicon samples.
背景
由于其在实验和计算方面的成本远低于宏基因组全基因组测序 (WGS),16S rRNA 基因扩增子已被广泛用于通过 PICRUSt 2 等软件工具预测微生物组的功能谱。然而,由于潜在的 PCR 偏倚和系统发育相关基因组之间的基因谱变化,从 16S 扩增子预测的功能谱可能与 WGS 衍生的谱不同,从而导致误导性的结果。
结果
我们在这里提出了 Meta-Apo,它大大减少了甚至消除了这种偏差,从而推断出两种方法之间更一致的多样性模式。在来自 4 个身体部位的超过 5000 个人类微生物组 16S-rRNA 扩增子样本中对 Meta-Apo 的测试表明,仅使用 15 个 WGS 扩增子训练样本对,两种策略之间的偏差显著降低。此外,Meta-Apo 能够实现 WGS 和扩增子样本之间的跨平台功能比较,从而极大地提高了基于 16S 的微生物组诊断,例如,通过基于 WGS 的分类,将基于 16S 的功能谱对牙龈炎的诊断准确性从 65%提高到 95%。因此,基于 16S 扩增子测序的低成本,Meta-Apo 可以产生与 shotgun WGS 提供的可靠、高分辨率的微生物组功能视图等效的视图。
结论
这表明,面向功能的大规模微生物组测序项目可能受益于 16S 扩增子策略的低成本,而不会牺牲否则需要 WGS 的功能重建精度。Meta-Apo 的优化 C++实现可在 GitHub(https://github.com/qibebt-bioinfo/meta-apo)上获得,许可证为 GNU GPL。它使用少数配对的 WGS:16S 扩增子样本的功能谱作为训练,并输出大量 16S 扩增子样本的校准功能谱。