Université Paris-Saclay, CentraleSupélec, Inria, 3 Rue Joliot Curie, 91190, Gif-Sur-Yvette, France.
IFP Energies nouvelles, 1 et 4 avenue de Bois-Préau, 92852, Rueil-Malmaison, France.
BMC Bioinformatics. 2022 Oct 17;23(1):429. doi: 10.1186/s12859-022-04955-w.
Gene expression is regulated at different molecular levels, including chromatin accessibility, transcription, RNA maturation, and transport. These regulatory mechanisms have strong connections with cellular metabolism. In order to study the cellular system and its functioning, omics data at each molecular level can be generated and efficiently integrated. Here, we propose BRANENET, a novel multi-omics integration framework for multilayer heterogeneous networks. BRANENET is an expressive, scalable, and versatile method to learn node embeddings, leveraging random walk information within a matrix factorization framework. Our goal is to efficiently integrate multi-omics data to study different regulatory aspects of multilayered processes that occur in organisms. We evaluate our framework using multi-omics data of Saccharomyces cerevisiae, a well-studied yeast model organism.
We test BRANENET on transcriptomics (RNA-seq) and targeted metabolomics (NMR) data for wild-type yeast strain during a heat-shock time course of 0, 20, and 120 min. Our framework learns features for differentially expressed bio-molecules showing heat stress response. We demonstrate the applicability of the learned features for targeted omics inference tasks: transcription factor (TF)-target prediction, integrated omics network (ION) inference, and module identification. The performance of BRANENET is compared to existing network integration methods. Our model outperforms baseline methods by achieving high prediction scores for a variety of downstream tasks.
基因表达在不同的分子水平上受到调控，包括染色质可及性、转录、RNA 成熟和运输。这些调控机制与细胞代谢有很强的联系。为了研究细胞系统及其功能，可以生成和有效地整合每个分子水平的组学数据。在这里，我们提出了 BRANENET，这是一种用于多层异质网络的新型多组学整合框架。BRANENET 是一种表达能力强、可扩展且通用的方法，可利用矩阵分解框架内的随机游走信息学习节点嵌入。我们的目标是有效地整合多组学数据，以研究发生在生物体中的多层过程的不同调控方面。我们使用酿酒酵母（Saccharomyces cerevisiae）的多组学数据来评估我们的框架，酿酒酵母是一种研究得很好的酵母模式生物。
我们在转录组学（RNA-seq）和靶向代谢组学（NMR）数据上测试了 BRANENET，用于野生型酵母菌株在 0、20 和 120 分钟的热激时间过程中。我们的框架学习了表现出热应激反应的差异表达生物分子的特征。我们证明了所学习特征在靶向组学推断任务中的适用性：转录因子（TF）-靶标预测、综合组学网络（ION）推断和模块识别。将 BRANENET 的性能与现有的网络整合方法进行了比较。我们的模型通过实现各种下游任务的高分预测，优于基线方法。
