测试
Predictive metabolomic profiling of microbial communities using amplicon or metagenomic sequences
摘要
（1）Microbial community metabolomics, particularly in the human gut, are beginning to provide a new route to identify functions and ecology disrupted in disease. 微生物群落代谢组学，特别是在人类肠道中，开始提供一种新的途径来识别疾病中被破坏的功能和生态。
（2）However, these data can be costly and difficult to obtain at scale, while amplicon or shotgun metagenomic sequencing data are readily available for populations of many thousands. 然而，这些数据可能是昂贵的，并且难以大规模获得，而扩增子或鸟枪法宏基因组测序数据对于成千上万的人群来说是容易获得的。
（3） Here, we describe a computational approach to predict potentially unobserved metabolites in new microbial communities, given a model trained on paired metabolomes and metagenomes from the environment of interest. .在这里，我们描述了一种计算方法来预测新的微生物群落中潜在的未观察到的代谢物，给出了一个对来自感兴趣环境的成对代谢组和元基因组进行训练的模型。
（4）Focusing on two independent human gut microbiome datasets, we demonstrate that our framework successfully recovers community metabolic trends for more than 50% of associated metabolites. 通过关注两个独立的人类肠道微生物组数据集，我们证明了我们的框架成功地恢复了超过50%的相关代谢物的社区代谢趋势。
（5）Similar accuracy is maintained using amplicon profiles of coral-associated, murine gut, and human vaginal microbiomes. We also provide an expected performance score to guide application of the model in new samples. 使用珊瑚相关的、鼠肠道和人类阴道微生物组的扩增子图谱也保持了类似的准确性。我们还提供了一个预期的性能分数，以指导模型在新样本中的应用。
（6）Our results thus demonstrate that this ‘predictive metabolomic’ approach can aid in experimental design and provide useful insights into the thousands of community profiles for which only metagenomes are currently available因此，我们的结果表明，这种“预测代谢组学”方法可以帮助实验设计，并为目前只有宏基因组可用的成千上万的社区概况提供有用的见解。