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| ???org.dspace.app.webui.jsptag.ItemTag.dcfield??? | Value | Language |
|---|---|---|
| dc.contributor.advisor | 陳虹諺(Hungyen Chen) | |
| dc.contributor.author | Pen-Li Lee | en |
| dc.contributor.author | 李本立 | zh_TW |
| dc.date.accessioned | 2021-06-08T03:43:48Z | - |
| dc.date.copyright | 2019-07-03 | |
| dc.date.issued | 2019 | |
| dc.date.submitted | 2019-06-03 | |
| dc.identifier.citation | Allen, J. D., Xie, Y., Chen, M., Girard, L., & Xiao, G. (2012). Comparing statistical methods for constructing large scale gene networks. PLOS ONE, 7:e29348.
Aubert, D., Chevillard, M., Dorne, A. M., Arlaud, G., & Herzog, M. (1998). Expression patterns of GASA genes in Arabidopsis thaliana: the GASA4 gene is up-regulated by gibberellins in meristematic regions. Plant Molecular Biology, 36:871-883. Bajguz, A. (2011). Suppression of Chlorella vulgaris growth by cadmium, lead, and copper stress and its restoration by endogenous brassinolide. Archives of Environmental Contamination and Toxicology, 60:406-416. Banerjee, A., & Roychoudhury, A. (2017). Abscisic-acid-dependent basic leucine zipper (bZIP) transcription factors in plant abiotic stress. Protoplasma, 254:3-16 Benjamini, Y., & Hochberg, Y. (1995). Controlling the false discovery rate: a practical and powerful approach to multiple testing. Journal of the Royal Statistical Society: Series B (Methodological), 57:289-300. Birecka, H., & Galston, A. W. (1970). Peroxidase ontogeny in a dwarf pea stem as affected by gibberellin and decapitation. Journal of Experimental Botany, 21:735-745. Bolstad, B. M., Irizarry, R. A., Åstrand, M., & Speed, T. P. (2003). A comparison of normalization methods for high density oligonucleotide array data based on variance and bias. Bioinformatics, 19:185-193. Clarke, J. D., Volko, S. M., Ledford, H., Ausubel, F. M., & Dong, X. (2000). Roles of salicylic acid, jasmonic acid, and ethylene in cpr-induced resistance in Arabidopsis. The Plant Cell, 12:2175-2190. Collier, M. D., Fotelli, M. N., Nahm, M., Kopriva, S., Rennenberg, H., Hanke, D. E., & Gessler, A. (2003). Regulation of nitrogen uptake by Fagus sylvatica on a whole plant level–interactions between cytokinins and soluble N compounds. Plant, Cell & Environment, 26:1549-1560. Dill, A., Thomas, S. G., Hu, J., Steber, C. M., & Sun, T. P. (2004). The Arabidopsis F-box protein SLEEPY1 targets gibberellin signaling repressors for gibberellin-induced degradation. The Plant Cell, 16:1392-1405. Espelund, M., Sæbøe‐Larssen, S., Hughes, D. W., Galau, G. A., Larsen, F., & Jakobsen, K. S. (1992). Late embryogenesis‐abundant genes encoding proteins with different numbers of hydrophilic repeats are regulated differentially by abscisic acid and osmotic stress. The Plant Journal, 2:241-252. Feng, S., Martinez, C., Gusmaroli, G., Wang, Y., Zhou, J., Wang, F., ... & Schäfer, E. (2008). Coordinated regulation of Arabidopsis thaliana development by light and gibberellins. Nature, 451:475. Grove, M. D., Spencer, G. F., Rohwedder, W. K., Mandava, N., Worley, J. F., Warthen Jr, J. D., ... & Cook Jr, J. C. (1979). Brassinolide, a plant growth-promoting steroid isolated from Brassica napus pollen. Nature, 281:216. Guo, L., Wang, P., Gu, Z., Jin, X., & Yang, R. (2017). Proteomic analysis of broccoli sprouts by iTRAQ in response to jasmonic acid. Journal of Plant Physiology, 218:16-25. Hakata, M., Muramatsu, M., Nakamura, H., Hara, N., Kishimoto, M., Iida-Okada, K., ... & Yamakawa, H. (2017). Overexpression of TIFY genes promotes plant growth in rice through jasmonate signaling. Bioscience, Biotechnology, and Biochemistry, 81:906-913. Hartigan, J. A., & Wong, M. A. (1979). Algorithm AS 136: A k-means clustering algorithm. Journal of the Royal Statistical Society. Series C (Applied Statistics), 28:100-108. Hirose, N., Makita, N., Kojima, M., Kamada-Nobusada, T., & Sakakibara, H. (2007). Overexpression of a type-A response regulator alters rice morphology and cytokinin metabolism. Plant and Cell Physiology, 48:523-539. Ioio, R. D., Linhares, F. S., Scacchi, E., Casamitjana-Martinez, E., Heidstra, R., Costantino, P., & Sabatini, S. (2007). Cytokinins determine Arabidopsis root-meristem size by controlling cell differentiation. Current Biology, 17:678-682. Ito, Y., & Kurata, N. (2006). Identification and characterization of cytokinin-signalling gene families in rice. Gene, 382:57-65. Johnson, S. C. (1967). Hierarchical clustering schemes. Psychometrika, 32:241-254. Kim, M. J., Shin, R., & Schachtman, D. P. (2009). A nuclear factor regulates abscisic acid responses in Arabidopsis. Plant Physiology, 151:1433-1445. Křeček, P., Skůpa, P., Libus, J., Naramoto, S., Tejos, R., Friml, J., & Zažímalová, E. (2009). The PIN-FORMED (PIN) protein family of auxin transporters. Genome Biology, 10:249. Langfelder, P., & Horvath, S. (2008). WGCNA: an R package for weighted correlation network analysis. BMC Bioinformatics, 9:559. Li, H., Cheng, X., Zhang, L., Hu, J., Zhang, F., Chen, B., ... & Huang, Q. (2018). An integration of genome-wide association study and gene co-expression network analysis identifies candidate genes of stem lodging-related traits in Brassica napus. Frontiers in Plant Science, 9. Liu, L. D., Chen, Q. Y., Yang, Z. W., Cheng, C. C., Wu, B.G., Chang, H. Y., ... & Wu, D. H. (2016). Illustration of GGE biplot analysis on rice regional-trial dataset. Crop, Environment & Bioinformatics 13:167-178. Liu, Z., Li, M., Fang, X., Shen, L., Yao, W., Fang, Z., ... & Lin, C. (2019). Identification of surrogate prognostic biomarkers for allergic asthma in nasal epithelial brushing samples by WGCNA. Journal of Cellular Biochemistry, 120:5137-5150. Liu, Z., Yan, J. P., Li, D. K., Luo, Q., Yan, Q., Liu, Z. B., ... & Yang, Y. (2015). UDP-glucosyltransferase71c5, a major glucosyltransferase, mediates abscisic acid homeostasis in Arabidopsis. Plant Physiology, 167:1659-1670. Lorenz, C., Brandt, S., Borisjuk, L., Rolletschek, H., Heinzel, N., Tohge, T., ... & Hildebrandt, T. M. (2018). The role of persulfide metabolism during Arabidopsis seed development under light and dark conditions. Frontiers in Plant Science, 9. Lv, Z., Wang, S., Zhang, F., Chen, L., Hao, X., Pan, Q., ... & Tang, K. (2016). Overexpression of a novel NAC domain-containing transcription factor gene (AaNAC1) enhances the content of artemisinin and increases tolerance to drought and Botrytis cinerea in Artemisia annua. Plant and Cell Physiology, 57:1961-1971. Margolin, A. A., Nemenman, I., Basso, K., Wiggins, C., Stolovitzky, G., Dalla Favera, R., & Califano, A. (2006). ARACNE: an algorithm for the reconstruction of gene regulatory networks in a mammalian cellular context. BMC Bioinformatics, 7. Moons, A., De Keyser, A., & Van Montagu, M. (1997). A group 3 LEA cDNA of rice, responsive to abscisic acid, but not to jasmonic acid, shows variety-specific differences in salt stress response. Gene, 191:197-204. Moubayidin, L., Di Mambro, R., & Sabatini, S. (2009). Cytokinin–auxin crosstalk. Trends in Plant Science, 14:557-562. Ogawa, M., Hanada, A., Yamauchi, Y., Kuwahara, A., Kamiya, Y., & Yamaguchi, S. (2003). Gibberellin biosynthesis and response during Arabidopsis seed germination. The Plant Cell, 15:1591-1604. Park, M. H., Suzuki, Y., Chono, M., Knox, J. P., & Yamaguchi, I. (2003). CsAGP1, a gibberellin-responsive gene from cucumber hypocotyls, encodes a classical arabinogalactan protein and is involved in stem elongation. Plant Physiology, 131:1450-1459. Peng, H., Yang, T., & Jurick II, W. M. (2014). Calmodulin gene expression in response to mechanical wounding and Botrytis cinerea infection in tomato fruit. Plants, 3: 427-441. Peng, J., Wang, P., Zhou, N., & Zhu, J. (2009). Partial correlation estimation by joint sparse regression models. Journal of the American Statistical Association, 104:735-746. Roxrud, I., Lid, S. E., Fletcher, J. C., Schmidt, E. D., & Opsahl-Sorteberg, H. G. (2007). GASA4, one of the 14-member Arabidopsis GASA family of small polypeptides, regulates flowering and seed development. Plant and Cell Physiology, 48:471-483. Schäfer, J., & Strimmer, K. (2004). An empirical Bayes approach to inferring large-scale gene association networks. Bioinformatics, 21:754-764. Schaller, G. E. (2012). Ethylene and the regulation of plant development. BMC Biology, 10:9. Shen, P. C., Hour, A. L., & Liu, L. Y. D. (2017). Microarray meta-analysis to explore abiotic stress-specific gene expression patterns in Arabidopsis. Botanical Studies, 58:22. Smita, S., Katiyar, A., Pandey, D. M., Chinnusamy, V., Archak, S., & Bansal, K. C. (2013). Identification of conserved drought stress responsive gene-network across tissues and developmental stages in rice. Bioinformation, 9:72. Song, S., Qi, T., Huang, H., Ren, Q., Wu, D., Chang, C., ... & Xie, D. (2011). The jasmonate-ZIM domain proteins interact with the R2R3-MYB transcription factors MYB21 and MYB24 to affect jasmonate-regulated stamen development in Arabidopsis. The Plant Cell, 23:1000-1013. Suza, W. P., & Staswick, P. E. (2008). The role of JAR1 in jasmonoyl-L-isoleucine production during Arabidopsis wound response. Planta, 227:1221-1232. Thalmann, M., Pazmino, D., Seung, D., Horrer, D., Nigro, A., Meier, T., ... & Santelia, D. (2016). Regulation of leaf starch degradation by abscisic acid is important for osmotic stress tolerance in plants. The Plant Cell, 28:1860-1878. Tirichine, L., Sandal, N., Madsen, L. H., Radutoiu, S., Albrektsen, A. S., Sato, S., ... & Stougaard, J. (2007). A gain-of-function mutation in a cytokinin receptor triggers spontaneous root nodule organogenesis. Science, 315:104-107. Trejo, C. L., & Davies, W. J. (1991). Drought-induced closure of Phaseolus vulgaris L. stomata precedes leaf water deficit and any increase in xylem ABA concentration. Journal of Experimental Botany, 42:1507-1516. Wang, J. L., Zhang, Y., Pan, X. D., Du, J. J., & Guo, X. Y. (2019). Discovery of leaf region and time point related modules and genes in maize (Zea mays L.) leaves by Weighted Gene Co-expression Network analysis (WGCNA) of gene expression profiles of carbon metabolism. Journal of Integrative Agriculture, 18:350-360. Welch, W. J. (1990). Construction of permutation tests. Journal of the American Statistical Association, 85:693-698. Woodward, A. W., & Bartel, B. (2005). Auxin: regulation, action, and interaction. Annals of Botany, 95:707-735. Yan, S. (2018). Integrative analysis of promising molecular biomarkers and pathways for coronary artery disease using WGCNA and MetaDE methods. Molecular Medicine Reports, 18:2789-2797. Zhang, B., & Horvath, S. (2005). A general framework for weighted gene co-expression network analysis. Statistical Applications in Genetics and Molecular Biology, 4. Zuo, Z., Shen, J. X., Pan, Y., Pu, J., Li, Y. G., Shao, X. H., & Wang, W. P. (2018). Weighted gene correlation network analysis (WGCNA) detected loss of MAGI2 promotes chronic kidney disease (CKD) by podocyte damage. Cellular Physiology and Biochemistry, 51:244-261. | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/21717 | - |
| dc.description.abstract | 植物荷爾蒙是能根據其濃度影響植物細胞代謝的有機物，對植物生理調控有相當的重要性。本研究目的在於了解不同植物荷爾蒙對水稻基因表現量的影響，找出易受植物荷爾蒙影響的基因群，並從中選出較為關鍵之基因。研究數據為NCBI資料庫中下載的六種植物荷爾蒙處理之水稻微陣列資料，以變異數分析從中篩選出22991個差異表現基因，並利用加權基因共表現網路(WGCNA)的分析方式將差異表現基因分成基因模塊1至16，再透過熱圖和雙軸圖將根部與莖部的樣本分別進行視覺化。在根的樣本組中，基因模塊3、4、5、7、11、12分別與離層酸、茉莉酸、離層酸或生長素、蕓苔素内酯、細胞分裂素、吉貝素有較高的關聯性；在莖的樣本組中，基因模塊1、2、3、6、7與蕓苔素内酯、茉莉酸、離層酸、吉貝素或細胞分裂素、離層酸或生長素有較高的關聯性。接著利用kIM和kME值挑選在基因模組中與其他基因有較高關聯性且與特徵基因較為相關之基因，稱之為關鍵基因，而其中部分關鍵基因已被證實與特定植物荷爾蒙有所關聯。藉由熱圖和雙軸圖的輔助，有助於解釋不同荷爾蒙處理與基因模塊之間的關係。最終篩選出的關鍵基因則能提供日後植物荷爾蒙和基因功能之相關研究的參考。 | zh_TW |
| dc.description.abstract | Plant hormones are organic substances that control the metabolism of plant cells according to their concentration, and play an important role in physiological regulation of plants. This study aims to understand the effects of different plant hormones on rice gene expression, and identify plant hormone-related clusters of genes and their key genes. In this study, we downloaded the available microarray data sets of rice with six kinds of plant hormone treatments from GEO. 22991 differential expression genes were selected by the analyses of variance (ANOVA), and categorized into gene modules 1 to 16 by weighted gene co-expression network analysis (WGCNA). Heatmaps and biplots were used to visualize the results of the root and shoot samples. In root samples, gene modules 3, 4, 5, 7, 11, and 12 are more related to abscisic acid, jasmonates, abscisic acid or auxin, brassinolides, cytokinin, and gibberellin respectively. In shoot samples, gene modules 1, 2, 3, 6, and 7 are more related to brassinolides, jasmonates, abscisic acid, gibberellin or cytokinin, and abscisic acid or auxin respectively. In each gene module, we set the thresholds of kIM and kME to screen the genes that have high connectivity to the other genes and high correlation to the eigen gene. These genes are called key genes, and some of the key genes have been confirmed that they have relation with specific plant hormones. It is helpful to explain the relationship between different plant hormone treatments and gene modules through the heatmaps and biplots. Finally, the key genes may serve as a reference for future studies about plant hormones and gene function. | en |
| dc.description.provenance | Made available in DSpace on 2021-06-08T03:43:48Z (GMT). No. of bitstreams: 1 ntu-108-R06621206-1.pdf: 1250018 bytes, checksum: a53d0e8591f87a773df8a54253011b8c (MD5) Previous issue date: 2019 | en |
| dc.description.tableofcontents | 目錄
口試委員會審定書 # 誌謝 i 摘要 ii ABSTRACT iii 目錄 iv 圖目錄 vi 表目錄 vii 中英文及縮寫對照表 viii 第一章 前言 1 一、植物荷爾蒙與基因體 1 二、基因網路 2 三、加權基因共表現網路 2 四、研究目的 3 第二章 材料與方法 5 一、樣本蒐集 5 二、分析方法 5 1.資料前處理 5 2.篩選差異表現基因(Differential expression genes, DE genes) 6 3. 建構加權基因共表現網路 7 4.分類基因模塊 8 5.繪製雙軸圖 10 6.基因富集分析 12 7. 篩選基因模塊中的關鍵基因 12 第三章 結果 14 一、差異表現基因 14 二、加權基因共表現網路 14 1.基因模塊 14 2.特徵基因於各荷爾蒙處理下的平均表現量 14 三、雙軸圖 15 四、基因模塊之分析 16 1.基因富集分析 16 2.關鍵基因 16 第四章 討論 18 一、軟閾值的設定 18 二、根部樣本的關鍵基因與各荷爾蒙處理之相關性實證 18 三、莖部樣本的關鍵基因與各荷爾蒙處理之相關性實證 20 四、關鍵基因之探討與應用 22 第五章 結論 24 參考文獻 25 圖目錄 圖一. 加權基因共表現網路之基因分群 34 圖二. 根部樣本之熱圖 35 圖三. 莖部樣本之熱圖 36 圖四. 根部樣本之雙軸圖 37 圖五. 根部樣本之多邊形雙軸圖 38 圖六. 莖部樣本之雙軸圖 39 圖七. 莖部樣本之多邊形雙軸圖 40 附圖目錄 附圖一. 基因網路之分支度次數直方圖 44 附圖二. 根部樣本之kME_i和kIM_i^*散佈圖 45 附圖三. 莖部樣本之kME_i和kIM_i^*散佈圖 47 表目錄 表一. 各基因模塊對應之基因數目 41 表二. 根部樣本之關鍵基因 42 表三. 莖部樣本之關鍵基因 43 附表目錄 附表一. 軟閾值之測試結果 49 附表二. 各基因模塊之基因富集分析結果 50 附錄目錄 附錄一. 本研究分析之R程式碼 55 | |
| dc.language.iso | zh-TW | |
| dc.title | 利用加權基因共表現網路鑑別水稻荷爾蒙相關之基因模塊與關鍵基因 | zh_TW |
| dc.title | Identification of Rice Hormone-Related Gene Modules and Key Genes by Weighted Gene Co-Expression Network Analysis | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 107-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 劉力瑜,張孟基 | |
| dc.subject.keyword | 植物荷爾蒙,加權基因共表現網路,特徵基因,雙軸圖,關鍵基因, | zh_TW |
| dc.subject.keyword | biplot,eigen gene,key gene,plant hormone,weighted gene co-expression network analysis, | en |
| dc.relation.page | 62 | |
| dc.identifier.doi | 10.6342/NTU201900838 | |
| dc.rights.note | 未授權 | |
| dc.date.accepted | 2019-06-04 | |
| dc.contributor.author-college | 生物資源暨農學院 | zh_TW |
| dc.contributor.author-dept | 農藝學研究所 | zh_TW |
| Appears in Collections: | 農藝學系 | |
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| ntu-108-1.pdf Restricted Access | 1.22 MB | Adobe PDF |
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