Bayesian Data Fusion of Gene Expression and Histone Modification Profiles for Inference of Gene Regulatory Network

• Published in: IEEE/ACM transactions on computational biology and bioinformatics 2020-03, Vol.17 (2), p.516-525
• Main Authors: Chen, Haifen, Maduranga, D. A. K., Mundra, Piyushkumar A., Zheng, Jie
• Format: Article
• Language: English
• Subjects: Algorithms; Bayes methods; Bayes Theorem; Bayesian analysis; Biological system modeling; Computational Biology - methods; Conditional probability; data fusion; Data integration; Databases, Genetic; Datasets; DNA; dynamic Bayesian network; Epigenesis, Genetic - genetics; Epigenetics; Gene expression; Gene Expression Regulation - genetics; Gene regulation; Gene regulatory network; Gene Regulatory Networks - genetics; Histone Code - genetics; Histones; Inference; Machine Learning; Performance enhancement; Probability distribution; Regulatory mechanisms (biology); Saccharomyces cerevisiae; Saccharomyces cerevisiae Proteins - genetics; Saccharomyces cerevisiae Proteins - metabolism; Source code; Transcriptome - genetics
• ISSN: 1545-5963; 1557-9964
• DOI: 10.1109/TCBB.2018.2869590

Accurately reconstructing gene regulatory networks (GRNs) from high-throughput gene expression data has been a major challenge in systems biology for decades. Many approaches have been proposed to solve this problem. However, there is still much room for the improvement of GRN inference. Integrating data from different sources is a promising strategy. Epigenetic modifications have a close relationship with gene regulation. Hence, epigenetic data such as histone modification profiles can provide useful information for uncovering regulatory interactions between genes. In this paper, we propose a method to integrate epigenetic data into the inference of GRNs. In particular, a dynamic Bayesian network (DBN) is employed to infer gene regulations from time-series gene expression data. Epigenetic data (histone modification profiles here) are integrated into the prior probability distribution of the Bayesian model. Our method has been validated on both synthetic and real datasets. Experimental results show that the integration of epigenetic data can significantly improve the performance of GRN inference. As more epigenetic datasets become available, our method would be useful for elucidating the gene regulatory mechanisms driving various cellular activities. The source code and testing datasets are available at https://github.com/Zheng-Lab/MetaGRN/tree/master/histonePrior .