A Bayesian data fusion based approach for learning genome-wide transcriptional regulatory networks
AffiliationUniv Arizona Hlth Sci, Dept Med, Ctr Biomed Informat & Biostat
KeywordsGenomic transcriptional networks
Hybrid structure learning algorithm
MetadataShow full item record
CitationSauta, E., Demartini, A., Vitali, F., Riva, A., & Bellazzi, R. (2020). A Bayesian data fusion based approach for learning genome-wide transcriptional regulatory networks. BMC Bioinformatics, 21, 1-28.
Rights© The Author(s). 2020 Open Access. This article is licensed under a Creative Commons Attribution 4.0 International License. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
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AbstractBackground Reverse engineering of transcriptional regulatory networks (TRN) from genomics data has always represented a computational challenge in System Biology. The major issue is modeling the complex crosstalk among transcription factors (TFs) and their target genes, with a method able to handle both the high number of interacting variables and the noise in the available heterogeneous experimental sources of information. Results In this work, we propose a data fusion approach that exploits the integration of complementary omics-data as prior knowledge within a Bayesian framework, in order to learn and model large-scale transcriptional networks. We develop a hybrid structure-learning algorithm able to jointly combine TFs ChIP-Sequencing data and gene expression compendia to reconstruct TRNs in a genome-wide perspective. Applying our method to high-throughput data, we verified its ability to deal with the complexity of a genomic TRN, providing a snapshot of the synergistic TFs regulatory activity. Given the noisy nature of data-driven prior knowledge, which potentially contains incorrect information, we also tested the method's robustness to false priors on a benchmark dataset, comparing the proposed approach to other regulatory network reconstruction algorithms. We demonstrated the effectiveness of our framework by evaluating structural commonalities of our learned genomic network with other existing networks inferred by different DNA binding information-based methods. Conclusions This Bayesian omics-data fusion based methodology allows to gain a genome-wide picture of the transcriptional interplay, helping to unravel key hierarchical transcriptional interactions, which could be subsequently investigated, and it represents a promising learning approach suitable for multi-layered genomic data integration, given its robustness to noisy sources and its tailored framework for handling high dimensional data.
NoteOpen access journal
VersionFinal published version
Except where otherwise noted, this item's license is described as © The Author(s). 2020 Open Access. This article is licensed under a Creative Commons Attribution 4.0 International License. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
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