進階搜尋


   電子論文尚未授權公開,紙本請查館藏目錄
(※如查詢不到或館藏狀況顯示「閉架不公開」,表示該本論文不在書庫,無法取用。)
系統識別號 U0026-2706201622342100
論文名稱(中文) 建立特定疾病或組織中的微核醣核酸-標靶基因關係的資料庫
論文名稱(英文) Construction of a database which provides disease-specific or tissue-specific miRNA-target relationships
校院名稱 成功大學
系所名稱(中) 電機工程學系
系所名稱(英) Department of Electrical Engineering
學年度 104
學期 2
出版年 105
研究生(中文) 凃博文
研究生(英文) Bo-Wen Tu
學號 N26031681
學位類別 碩士
語文別 中文
論文頁數 49頁
口試委員 指導教授-吳謂勝
口試委員-曾大千
口試委員-張天豪
口試委員-王育民
口試委員-陳建生
中文關鍵字 微核醣核酸  標靶基因  共同調控  篩選器 
英文關鍵字 miRNA  target gene  co-regulation  biological filter 
學科別分類
中文摘要 微核醣核酸(microRNA, miRNA),是一個不會被轉譯成蛋白質,且長度約21至22個核甘酸(Nucleotides),屬於非編碼的RNA(non-coding RNA)片段。微核醣核酸在DNA轉錄到RNA過程中,會和信使核醣核酸(mRNA)的3’非轉譯區(3’ untranslated region)互補結合,抑制轉錄後蛋白質的生成,影響到標靶基因的表現量,影響了細胞生長,甚至和癌症有顯著的關係。因此微核醣核酸在調控基因現象中,扮演很重要的角色。目前線上已經有許多微核醣核酸預測基因的資料庫,如microRNA.org、TargetScan、DIANA-microT、miRDB等等,使用各自的演算法求出標靶基因。因為有太多預測資料庫被發展出來,造成了各自演算法求出的結果不盡相同,以及各自預測分數權重不一樣,容易造成研究學者的不便。因此有一些整合預測資料庫,將許多這些資料庫預測的結果整合在一起,讓使用者方便查看所有的預測結果。如miRSystem、miRwalk、miRecords等等。但是這些整合預測資料庫沒有提供預測結果有表現分佈在特定組織或是疾病,導致整合的預測結果數量仍然相當龐大。為了解決這方面的問題,我們建立了CSmiRTar資料庫。CSmiRTar收集了時常更新預測資料的資料庫,如miRDB microRNA.org, TargetScan及DIANA-microT,以及實驗證實的資料庫mirTarbase。此外也增加微核醣核酸或是基因有表現分佈在組織或是疾病的相關資料的資料庫,如DisGeNET,PhenomiR及Expression Atlas,做為CSmiRTar搜尋時的篩選器。CSmiRTar也提供使用者輸入多個微核醣核酸找出其共同調控的標靶基因,或是輸入多個標靶基因找出其共同被哪些微核醣核酸所調控。我們相信CSmiRTar友善的介面及豐富的資訊,對於研究學者來說,要探討微核醣核酸與標靶基因交互作用是在哪些組織或疾病下,是相當有幫助的。CSmiRTar資料庫目前可在下列網址取得http://cosbi.ee.ncku.edu.tw/CSmiRTar/.
英文摘要 MicroRNAs (miRNAs) are functional RNA molecules which play important roles in post-transcriptional regulation. miRNAs regulate their targets by repressing translation or inducing degradation of target mRNAs. Several databases have been constructed to deposit predicted miRNA-target information by using different algorithms, but these databases usually contains lots of false positives. Besides, the validated databases provides only a few miRNA-target information compared to the predicted databases. To reduce incorrect records and increase the number of reliable records, many other databases integrate these predicted miRNA-target information from the databases mentioned above. However, the expression of the same miRNAs in different tissues are different, the realistic regulatory mechanisms could not be figured out in these databases. Moreover, they cannot return the common targets with multiple input miRNAs. To solve these two problems, we construct a database called CSmiRTar (Condition-Specific miRNA Targets). CSmiRTar collects computationally predicted targets of 2588 miRNAs in Human (or 1945 miRNAs in Mouse) from four existing databases (microRNA.org, TargetScan, DIANA-microT and miRDB), and it provides some biological filters which enabling users to search miRNA targets which are expressed only in a specific tissue or related to a specific disease. Moreover, CSmiRTar allows users to search the common targets of multiple miRNAs under a specific biological condition. We believe that CSmiRTar could be helpful for biologists whom want to study the regulatory mechanisms of miRNAs.
The CSmiRTar database is available at http://cosbi.ee.ncku.edu.tw /CSmiRTar/.
論文目次 中文摘要 I
英文摘要 II
誌謝 VI
目錄 VII
表目錄 IX
圖目錄 IXX
中英對照表 .XII
第一章 研究背景 1
1.1 分子生物學的中心法則 1
1.2 miRNA生成與基因調控 2
1.3 miRNA與標靶基因交互作用關係的資料庫 4
1.4 整合miRNA與標靶基因交互作用關係的資料庫 4
1.5 研究動機 6
第二章 資料來源與方法 8
2.1 miRNA與標靶基因交互作用關係的資料庫 8
2.1.1 microRNA.org 8
2.1.2 TargetScan 9
2.1.3 DIANA-microT 9
2.1.4 miRDB 10
2.1.5 miRTarbase 11
2.2 基因或miRNA表現分佈在組織或疾病的資料庫 12
2.2.1 Expression Atlas 12
2.2.2 DisGeNET 13
2.2.3 PhenomiR 14
2.3 資料蒐集處理與分數權重計算 15
第三章 結果與討論 18
3.1 資料庫功能與介面呈現 18
3.1.1 搜尋功能 18
3.1.1.1 輸入單個miRNA找標靶基因 22
3.1.1.2 輸入多個miRNA找標靶基因 25
3.1.1.3 輸入單個基因找miRNA 27
3.1.1.4 輸入多個基因找miRNA 30
3.1.2 瀏覽功能 32
3.2 微核醣核酸與基因調控之案例分析 35
3.2.1 使用篩選器案例分析 35
3.2.2 與其它整合資料庫結果比較分析 38
3.2.3 多輸入功能案例分析 39
第四章 結論與未來展望 41
4.1 結論 41
4.2 未來展望 42
參考文獻 43
參考文獻 [1] V. Agarwal, G. W. Bell, J. W. Nam and D. P. Bartel, “Predicting effective micro-RNA target sites in mammalian mRNAs,” Elife, vol. 4, p. e05005, 2015.
[2] J. Amberger, C. A. Bocchini, A. F. Scott and A. Hamosh, “McKusick’s Online Mendelian Inheritance in Man (OMIM),” Nucleic Acids Res, vol. 37, no. suppl 1, pp. D793-D796, 2009.
[3] K. Azra, G. Dominic, N. Matthew, W. Rachel, R. Lauren, J. E. Eric, M. Philip, d. P. Isabelle, C. G. Kristin, S. Markus, et al., “Combinatorial microRNA target pre-dictions,” Nature Genetics, vol. 37, no. 5, pp. 495-500, 2005.
[4] T. Barrett, D. B. Troup, S. E. Wilhite, P. Ledoux, C. Evangelista, I. F. Kim, M. Tomas-hevsky, K. A. Marshall, K. H. Phillippy, P. M. Sherman, et al., “NCBI GEO: archive for functional genomics data sets--10 years on,” Nucleic Acids Res, vol. 39, no. suppl 1, pp. D1005-D1010, 2011.
[5] D. P. Bartel, “MicroRNAs: genomics, biogenesis, mechanism, and function,” Cell, vol. 116, no. 2, pp. 281-297, 2004.
[6] D. P. Bartel, “MicroRNAs target recognition and regulatory functions," Cell, vol. 136, no. 2, pp. 215-233, 2009.
[7] D. Betel, M. Wilson, A. Gabow, D. S. Marks and C. Sander, “The microRNA.org resource: targets and expression,” Nucleic Acids Res, vol. 36, no. D1, pp. 149-153, 2008.
[8] J. A. Blake, C. J. Bult, J. T. Eppig, J. A. Kadin and J. E. Richardson, “The Mouse Genome Database: integration of and access to knowledge about the laboratory mouse,” Nucleic Acids Res, vol. 42, no. D1, pp. D810-D817, 2014.
[9] À. Bravo, J. Piñero, N. Queralt-Rosinach, M. Rautschka and L. I. Furlong, “Ex-traction of Relations Between Genes and Diseases From Text and Large-Scale Data Analysis: Implications for Translational Research,” BMC Bioinformatics, vol. 16, no. 1, p. 55, 2015.
[10] N. Bushati and S. M. Cohen, “MicroRNA functions,” Annu Rev Cell Dev Biol, vol. 23, pp. 175-205, 2007.
[11] M. Cesana and G. Q. Daley, “Deciphering the rules of ceRNA networks,” Proc Natl Acad Sci U S A, vol. 110, no. 18, pp. 7112-7113, 2013.
[12] S. Cho, I. Jang, Y. Jun, S. Yoon, M. Ko, Y. Kwon, I. Choi, H. Chang, D. Ryu, B. Lee, et al., “miRGator v3.0: a microRNA portal for deep sequencing, expression profiling and mRNA targeting,” Nucleic Acids Res, vol. 41, no. D1, pp. D252-D257, 2013.
[13] F. Crick, “Central dogma of molecular biology,” Nature, vol. 227, no. 5258, pp. 561-563, 1970.
[14] A. P. Davis, C. G. Murphy, R. Johnson, J. M. Lay, K. Lennon-Hopkins, C. Saraceni-Richards, D. Sciaky, B. L. King, M. C. Rosenstein, T. C. Wiegers, et al., “The comparative toxicogenomics database: update 2013,” Nucleic Acids Res, vol 41, no. D1, pp. D1104-D1114, 2013.
[15] Y. Deng, C. C. Wang, K. W. Choy, Q. Du, J. Chen, Q. Wang, L. Li, T. K. Chung and T. Tang, “Therapeutic potentials of gene silencing by RNA interference: principles, challenges, and new strategies,” Gene, vol. 538, no. 2, pp.217-227, 2014.
[16] H. Dweep, C. Sticht, P. Pandey and N. Gretz, “miRWalk--database: prediction of possible miRNA binding sites by "walking" the genes of three genomes,” J Biomed Inform, vol. 44, no. 5, pp. 839-847, 2011.
[17] W. Filipowicz, L. Jaskiewicz, F. A. Kolb and R. S. Pillai, “Post-transcriptional gene silencing by siRNAs and miRNAs,” Current Opinion in Structural Biology, vol. 15, no. 3, pp. 331-341, 2005.
[18] R. C. Friedman, K. K. Farh, C. B. Burge and D. P. Bartel, “Most mammalian mRNAs are conserved targets of microRNAs,” Genome Research, vol. 19, no. 1, pp. 92-105, 2009.
[19] J. B. Hsu, C. M. Chiu, S. D. Hsu, W. Y. Huang, C. H. Chien, T. Y. Lee and H. D. Huang, “miRTar: an integrated system for identifying miRNA-target interactions in human,” BMC Bioinformatics, vol. 12, pp. 300, 2011.
[20] S. D. Hsu, C. H. Chu, A. P. Tsou, S. J. Chen, H. C. Chen, P. W. Hsu, Y. H. Wong, Y. H. Chen, G. H. Chen and H. D. Huang, “miRNAMap 2.0: genomic maps of microRNAs in metazoan genomes,” Nucleic Acids Res, vol. 36, no. D1, pp. D165-169, 2008.
[21] S. D. Hsu, Y. T. Tseng, S. Shrestha, Y.L. Lin, A. Khaleel, C. H. Chou, C. F. Chu, H. Y. Huang, C. M. Lin, S. Y. Ho, et al., “miRTarBase update 2014: an information resource for experimentally validated miRNA-target interactions,” Nucleic Acids Res, vol. 42, no. D1, pp. 78-85, 2014.
[22] E. Huntzinger and E. Izaurralde, “Gene silencing by microRNAs: contributions of translational repression and mRNA decay,” Nature Reviews Genetics, vol. 12, no. 9, pp. 99-110, 2011.
[23] Z. Jeyapalan, Z. Deng, T. Shatseva, L. Fang, C. He and B. B. Yang, “Expression of CD44 3'-untranslated region regulates endogenous microRNA functions in tumorigenesis and angiogenesis,” Nucleic Acids Res, vol. 39, no. 8, pp. 3026-3041, 2011.
[24] B. John, A. J. Enright, A. Aravin, T. Tuschl, C. Sander and D. S. Marks, “Human MicroRNA targets,” PLoS Biol, vol. 2, no. 11, p. e363, 2004.
[25] F. A. Karreth, Y. Tay, D. Perna, U. Ala, S. M. Tan, A. G. Rust, G. DeNicola, K. A. Webster, D. Weiss, P. A. Perez-Mancera, et al., “In vivo identification of tumor- suppressive PTEN ceRNAs in an oncogenic BRAF-induced mouse model of melanoma,” Cell, vol. 147, no. 2, pp. 382-95, 2011.
[26] M. Kertesz, N. Iovino, U. Unnerstall, U. Gaul and E. Segal, “The role of site accessibility in microRNA target recognition,” Nature Genetics, vol. 39, no. 10, pp. 1278-1284, 2007.
[27] G. B. Kevin, K. C. Barnes, T. J. Bright and S.A. Wang. “The genetic association database,” Nature Genetics, vol. 36, no. 6, pp. 431-432, 2004.
[28] A. Kowarsch, M. Preusse, C. Marr and F. J. Theis, “miTALOS: analyzing the tissue-specific regulation of signaling pathways by human and mouse microRNAs,” RNA, vol. 17, no. 5 , pp. 809-819, 2011.
[29] A. Kozomara and S. Griffiths-Jones, “miRBase: annotating high confidence microRNAs using deep sequencing data,” Nucleic Acids Res, vol. 42, no. D1, pp. D68-73, 2014.
[30] J. Krutzfeldt, N. Rajewsky, R. Braich, K. G. Rajeev, T. Tuschl, M. Manoharan and M. Stoffel, “Silencing of microRNAs in vivo with ’antagomirs’,” Nature, vol. 438, no. 7068, pp. 685-689, 2005.
[31] S. J. Laulederkind, G. T. Hayman, S. J. Wang, J. R. Smith, T. F. Lowry, R. Nigam, V. Petri, J. de Pons, M. R. Dwinell, M. Shimoyama, et al., “Rat Genome Database 2013—data, tools and users,” Brief Bioinform, vol. 14, no. 4, pp. 520-526, 2013.
[32] R. C. Lee, R. L. Feinbaum and V. Ambros, “The C. elegans heterochronic gene lin-4 encodes small RNAs with antisense complementarity to lin-14,” Cell, vol. 75, no. 5, pp. 843-854, 1993.
[33] D. Y. Lee, Z. Jeyapalan, L. Fang, J. Yang, Y. Zhang, A. Y. Yee, M. Li, W. W. Du, T. Shatseva and B. B. Yang, “Expression of versican 3'-untranslated region modu-lates endogenous microRNA functions,” PLoS One, vol. 5, no. 10, p. e13599, 2010.
[34] B. P. Lewis, C. B. Burge and D. P. Bartel, “Conserved seed pairing, often flanked by adenosines, indicates that thousands of human genes are microRNA targets,” Cell, vol. 120, no. 1, pp. 15-20, 2005.
[35] J. H. Li, S. Liu, H. Zhou, L. H. Qu and J. H. Yang, “starBase v2.0: decoding miRNA-ceRNA, miRNA-ncRNA and protein-RNA interaction networks from large-scale CLIP-Seq data,” Nucleic Acids Res, vol. 42, no. D1, pp. D92-97. 2013.
[36] P. Loher and I. Rigoutsos, “Interactive exploration of RNA22 microRNA target predictions,” Bioinformatics, vol. 28, no. 24 , pp. 3322-3323, 2012.
[37] T. P. Lu, C. Y. Lee, M. H. Tsai, Y. C. Chiu, C. K. Hsiao, L. C. Lai and E. Y. Chuang, “miRSystem: an integrated system for characterizing enriched functions and pathways of microRNA targets,” PLoS One, vol. 7, no. 8, p. e42390, 2012.
[38] J. A. Mitchell, A. R. Aronson, J. G. Mork, L. C. Folk, S. M. Humphrey and J. M. Ward, “Gene indexing: characterization and analysis of NLM’s GeneRIFs,” AMIA Annu. Symp. Proc, pp. 460–464, 2003.
[39] M. D. Paraskevopoulou, G. Georgakilas, N. Kostoulas, I. S. Vlachos, T. Vergoulis, M. Reczko, C. Filippidis, T. Dalamagas and A. G. Hatzigeorgiou, “DIANA-microT web server v5.0: service integration into miRNA functional analysis workflows,” Nucleic Acids Res, vol. 41, no. D1, pp. 169-173, 2013.
[40] R. Petryszak, T. Burdett, B. Fiorelli, N. A. Fonseca, M. Gonzalez-Porta, E. Hastings, W. Huber, S. Jupp, M. Keays, N. Kryvych, et al., “Expression Atlas update-a database of gene and transcript expression from microarray- and sequencing-based functional genomics experiments,” Nucleic Acids Res, vol. 42, no. D1, pp. D926-932, 2014.
[41] J. Piñero, N. Queralt-Rosinach, À. Bravo, J. Deu-Pons, A. Bauer-Mehren, M. Baron, F. Sanz and L. I. Furlong, “DisGeNET: a discovery platform for the dynamical exploration of human diseases and their genes,” Database (Oxford), vol. 2015, p. bav028, 2015.
[42] J. Piriyapongsa, C. Bootchai, C. Ngamphiw and S. Tongsima, “microPIR2: a comprehensive database for human-mouse comparative study of microRNA-promoter interactions,” Database (Oxford), vol. 2014, p. bau115, 2014.
[43] L. Poliseno, L. Salmena, J. Zhang, B. Carver, W. J. Haveman and P. P. Pandolfi, “A coding-independent function of gene and pseudogene mRNAs regulates tumour biology,” Nature, vol. 465, no. 7301, pp. 1033-1038, 2010.
[44] M. Reczko, M. Maragkakis, P. Alexiou, I. Grosse and A. G. Hatzigeorgiou, “Fun-ctional microRNA targets in protein coding sequences,” Bioinformatics, vol. 28, no. 6, pp. 771-776, 2012.
[45] M. Rehmsmeier, P. Steffen, M. Hochsmann and R. Giegerich, “Fast and effective prediction of microRNA/target duplexes,” RNA, vol. 10, no. 10, pp. 1507-1517, 2004.
[46] A. Ruepp, A. Kowarsch, D. Schmidl, F. Buggenthin, B. Brauner, I. Dunger, G. Fobo, G. Frishman, C. Montrone and F. J. Theis, “PhenomiR: a knowledgebase for microRNA expression in diseases and biological processes,” Genome Biol, vol. 11, no. 1, p. R6, 2010.
[47] G. Rustici, N. Kolesnikov, M. Brandizi, T. Burdett, M. Dylag, I. Emam, A. Farne, E. Hastings, J. Ison, M. Keays, et al., “ArrayExpress update–trends in database growth and links to data analysis tools,” Nucleic Acids Res, vol. 41, no. D1, pp. D987-D990, 2013.
[48] L. Salmena, L. Poliseno, Y. Tay, L. Kats and P. P. Pandolfi, “A ceRNA hypothesis: the rosetta stone of a hidden RNA language? ,” Cell, vol. 146, no. 3, pp. 353-358, 2011.
[49] A. L. Sarver and S. Subramanian, “Competing endogenous RNA database,” Bioinformation, vol. 8, no. 15, pp. 731-733, 2012.
[50] R. Sen, S. Ghosal, S. Das, S. Balti and J. Chakrabarti, “Competing endogenous RNA: the key to posttranscriptional regulation,” ScientificWorldJournal, vol. 2014, no. 2014, p. 896206, 2014.
[51] E. A. Shirdel, W. Xie, T. W. Mak and I. Jurisica, “NAViGaTing the micronome--using multiple microRNA prediction databases to identify signalling pathway-associated microRNAs,” PLoS One, vol. 6, no. 2 , p. e17429, 2011.
[52] E. J. Sontheimer and R. W. Carthew, “Silence from within: endogenous siRNAs and miRNAs,” Cell, vol. 122, no. 1, pp. 9-12, 2005.
[53] G. Stefani and F. J. Slack, “Small non-coding RNAs in animal development,” Nat Rev Mol Cell Biol, vol. 9, no. 3, pp. 219-230, 2008.
[54] Y. Tay, L. Kats, L. Salmena, D. Weiss, S. M. Tan, U. Ala, F. Karreth, L. Poliseno, P. Provero, F. Di Cunto, et al., “Coding-independent regulation of the tumor suppressor PTEN by competing endogenous mRNAs,” Cell, vol. 147, no. 2, pp. 344-357, 2011.
[55] Y. Tay, J. Rinn and P. P. Pandolfi, “The multilayered complexity of ceRNA crosstalk and competition,” Nature, vol. 505, no. 7483, pp. 344-352, 2014.
[56] The UniProt Consortium, “The UniProt Consortium: Activities at the universal protein resource (UniProt),” Nucleic Acids Res, vol. 42, no. D1, pp. D191-D198, 2014.
[57] X. Wang and I. M. El Naqa, “Prediction of both conserved and nonconserved microRNA targets in animals,” Bioinformatics, vol. 24, no. 3, pp. 325-332, 2008.
[58] D. Wang, J. Gu, T. Wang and Z. Ding, “OncomiRDB: a database for the experi-mentally verified oncogenic and tumor-suppressive microRNAs,” Bioinformatics, vol. 30, no. 15, pp. 2237-2238, 2014.
[59] P. Wang, H. Zhi, Y. Zhang, Y. Liu, J. Zhang, Y. Gao, M. Guo, S. Ning and X. Li, “miRSponge: a manually curated database for experimentally supported miRNA sponges and ceRNAs.,” Database (Oxford), vol. 2015, p.bav098, 2015.
[60] B. Wightman, I. Ha and G. Ruvkun, “Posttranscriptional regulation of the heterochronic gene lin-14 by lin-4 mediates temporal pattern formation in C. elegans,” Cell, vol. 75, no. 5, pp. 855-862, 1993.
[61] N. Wong and X. Wang, “miRDB: an online resource for microRNA target prediction and functional annotations,” Nucleic Acids Res, vol. 43, no. D1, pp. D146-152, 2015.
[62] F. Xiao, Z. Zuo, G. Cai, S. Kang, X. Gao and T. Li, “miRecords: an integrated resource for microRNA-target interactions,” Nucleic Acids Res, vol. 37, no. 8, pp. D105-110, 2009.
論文全文使用權限
  • 同意授權校內瀏覽/列印電子全文服務,於2018-06-30起公開。


  • 如您有疑問,請聯絡圖書館
    聯絡電話:(06)2757575#65773
    聯絡E-mail:etds@email.ncku.edu.tw