Discover the Dehydration Response Genes in Boea hygrometrica Transcriptome Using Bayesian Network Approach
Computational Biology and Bioinformatics
Volume 8, Issue 1, June 2020, Pages: 9-14
Received: Mar. 3, 2020; Accepted: Mar. 18, 2020; Published: Mar. 23, 2020
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Mengmeng Zhang, College of Life Sciences, Capital Normal University, Beijing, China
Lu Wang, College of Life Sciences, Capital Normal University, Beijing, China
Ping Wan, College of Life Sciences, Capital Normal University, Beijing, China
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“Drying without dying” is an amazing feature in land plant evolution. Boea hygrometrica is an important resurrection plant model. The current genome and transcriptome analysis have revealed that some biological processes may contribute to its dehydration tolerance, but genes play pivotal roles in the dehydration response remains unclear. Bayesian network approach is a powerful tool for transcriptome data analysis and biological network reconstruction. In this work, by using the Bayesian network approach, we first reconstruct a gene regulation network with the B. hygrometrica transcriptome data. The network contains 1292 genes. Next, we defined the hub node genes in the network and focus on their functions in order to understand the response B. hygrometrica carried out under the dehydration stress. Finally, by an association analysis, we deduce the function of the unknown gene Bhs126_021 which has a degree of 84 in the network. The data-driven strategy we applied in this work not only finds out the knowledge from the knowledge-driven strategy analysis, but also provides novel findings from the B. hygrometrica transcriptome. Our findings give insight of control genes in land plant under the dehydration stress. The data-driven strategy applied in this work can also efficiently analyze other similar transcriptome data sets.
Dehydration Response Genes, Boea hygrometrica, Bayesian Network, Transcriptome Analysis
To cite this article
Mengmeng Zhang, Lu Wang, Ping Wan, Discover the Dehydration Response Genes in Boea hygrometrica Transcriptome Using Bayesian Network Approach, Computational Biology and Bioinformatics. Vol. 8, No. 1, 2020, pp. 9-14. doi: 10.11648/j.cbb.20200801.12
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This article is an open access article distributed under the Creative Commons Attribution License ( which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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