“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.
| Published in | Computational Biology and Bioinformatics (Volume 8, Issue 1) |
| DOI | 10.11648/j.cbb.20200801.12 |
| Page(s) | 9-14 |
| Creative Commons |
This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited. |
| Copyright |
Copyright © The Author(s), 2024. Published by Science Publishing Group |
Dehydration Response Genes, Boea hygrometrica, Bayesian Network, Transcriptome Analysis
| [1] | Wilson CL, Gazette JB: Floral anatomy in Gesneriaceae. I. Cyrtandroideae. 1974, 135:247-256. |
| [2] | Xiao L, Yang G, Zhang L, Yang X, Zhao S, Ji Z, Zhou Q, Hu M, Wang Y, Chen M, et al: The resurrection genome of Boea hygrometrica: A blueprint for survival of dehydration. PNAS 2015, 112:5833–5837. |
| [3] | Pearl J: Probabilistic reasoning in intelligent systems: networks of plausible inference. Elsevier; 2014. |
| [4] | Gevaert O, Smet FD, Timmerman D, Moreau Y, Moor BDJB: Predicting the prognosis of breast cancer by integrating clinical and microarray data with Bayesian networks. 2006, 22: e184-e190. |
| [5] | Friedman N, Linial M, Nachman I, Pe'er DJJocb: Using Bayesian networks to analyze expression data. 2000, 7:601-620. |
| [6] | Bernaola N, Michiels M, Larrañaga P, Bielza C: Learning massive interpretable gene regulatory networks of the human brain by merging Bayesian Networks. BioRxiv 2020. doi: 10.1101/2020.02.05.935007. |
| [7] | Hashimoto RF, Kim S, Shmulevich I, Zhang W, Bittner ML, Dougherty ERJB: Growing genetic regulatory networks from seed genes. 2004, 20:1241-1247. |
| [8] | Auliac C, Frouin V, Gidrol X, d'Alché-Buc FJBb: Evolutionary approaches for the reverse-engineering of gene regulatory networks: A study on a biologically realistic dataset. 2008, 9:91. |
| [9] | Kotiang S, Eslami A: A Probabilistic Graphical Model for System-Wide Analysis of Gene Regulatory Networks. Bioinformatics 2020. Doi:10.1093/bioinformatics/btaa122. |
| [10] | Saint-Antoine MM, Singh A: Network inference in systems biology: recent developments, challenges, and applications. Current Opinion in Biotechnology 2020, 63:89-98. |
| [11] | Spirtes P, Glymour C, Scheines R, Kauffman S, Aimale V, Wimberly F: Constructing Bayesian network models of gene expression networks from microarray data. 2000. |
| [12] | Wan P, Yue Z, Xie Z, Gao Q, Yu M, Yang Z, Huang J: Mechanisms of Radiation Resistance in Deinococcus Radiodurans R1 Revealed by the Reconstruction of Gene Regulatory Network Using Bayesian Network approach. Journal of Proteomics & Bioinformatics 2013: S6: 007. |
| [13] | Smoot ME, Ono K, Ruscheinski J, Wang P-L, Ideker TJB: Cytoscape 2.8: new features for data integration and network visualization. 2011, 27:431-432. |
| [14] | Barabási A-L, Albert RJs: Emergence of scaling in random networks. 1999, 286:509-512. |
| [15] | Jeong H, Tombor B, Albert R, Oltvai ZN, Barabási A-L: The large-scale organization of metabolic networks. Nature 2000, 407:651-654. |
APA Style
Mengmeng Zhang, Lu Wang, Ping Wan. (2020). Discover the Dehydration Response Genes in Boea hygrometrica Transcriptome Using Bayesian Network Approach. Computational Biology and Bioinformatics, 8(1), 9-14. https://doi.org/10.11648/j.cbb.20200801.12
ACS Style
Mengmeng Zhang; Lu Wang; Ping Wan. Discover the Dehydration Response Genes in Boea hygrometrica Transcriptome Using Bayesian Network Approach. Comput. Biol. Bioinform. 2020, 8(1), 9-14. doi: 10.11648/j.cbb.20200801.12
AMA Style
Mengmeng Zhang, Lu Wang, Ping Wan. Discover the Dehydration Response Genes in Boea hygrometrica Transcriptome Using Bayesian Network Approach. Comput Biol Bioinform. 2020;8(1):9-14. doi: 10.11648/j.cbb.20200801.12
Share This Article
Twitter
Linked In
Facebook
Copy
Download@article{10.11648/j.cbb.20200801.12,
author = {Mengmeng Zhang and Lu Wang and Ping Wan},
title = {Discover the Dehydration Response Genes in Boea hygrometrica Transcriptome Using Bayesian Network Approach},
journal = {Computational Biology and Bioinformatics},
volume = {8},
number = {1},
pages = {9-14},
doi = {10.11648/j.cbb.20200801.12},
url = {https://doi.org/10.11648/j.cbb.20200801.12},
eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.cbb.20200801.12},
abstract = {“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.},
year = {2020}
}
TY - JOUR T1 - Discover the Dehydration Response Genes in Boea hygrometrica Transcriptome Using Bayesian Network Approach AU - Mengmeng Zhang AU - Lu Wang AU - Ping Wan Y1 - 2020/03/23 PY - 2020 N1 - https://doi.org/10.11648/j.cbb.20200801.12 DO - 10.11648/j.cbb.20200801.12 T2 - Computational Biology and Bioinformatics JF - Computational Biology and Bioinformatics JO - Computational Biology and Bioinformatics SP - 9 EP - 14 PB - Science Publishing Group SN - 2330-8281 UR - https://doi.org/10.11648/j.cbb.20200801.12 AB - “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. VL - 8 IS - 1 ER -
Information
Email: