Institute of Molecular Life Sciences and Swiss Institute of Bioinformatics, University of Zurich, CH-8057 Zürich, Switzerland.
Institute of Molecular Life Sciences and Swiss Institute of Bioinformatics, University of Zurich, CH-8057 Zürich, Switzerland.
Cell Syst. 2019 Sep 25;9(3):286-296.e8. doi: 10.1016/j.cels.2019.08.002. Epub 2019 Sep 18.
The availability of large-scale metagenomic sequencing data can facilitate the understanding of microbial ecosystems in unprecedented detail. However, current computational methods for predicting ecological interactions are hampered by insufficient statistical resolution and limited computational scalability. They also do not integrate metadata, which can reduce the interpretability of predicted ecological patterns. Here, we present FlashWeave, a computational approach based on a flexible Probabilistic Graphical Model framework that integrates metadata and predicts direct microbial interactions from heterogeneous microbial abundance data sets with hundreds of thousands of samples. FlashWeave outperforms state-of-the-art methods on diverse benchmarking challenges in terms of runtime and accuracy. We use FlashWeave to analyze a cross-study data set of 69,818 publicly available human gut samples and produce, to the best of our knowledge, the largest and most diverse network of predicted, direct gastrointestinal microbial interactions to date. FlashWeave is freely available for download here: https://github.com/meringlab/FlashWeave.jl.
大规模宏基因组测序数据的可用性可以帮助我们以前所未有的细节理解微生物生态系统。然而，目前用于预测生态相互作用的计算方法受到统计分辨率不足和计算可扩展性有限的限制。它们也没有整合元数据，这会降低预测生态模式的可解释性。在这里，我们提出了一种基于灵活概率图模型框架的计算方法 FlashWeave，该方法可以整合元数据，并从具有数十万样本的异构微生物丰度数据集预测直接微生物相互作用。在运行时间和准确性方面，FlashWeave 在各种基准测试挑战上都优于最先进的方法。我们使用 FlashWeave 分析了 69818 个人类肠道样本的跨研究数据集，并生成了迄今为止最大和最多样化的直接胃肠道微生物相互作用预测网络。FlashWeave 可在此处免费下载：https://github.com/meringlab/FlashWeave.jl。
