臺灣博碩士論文加值系統

環狀RNA (Circular RNA，簡稱CircRNA)是一種環型的 noncoding RNA，這種特殊的RNA與一般常見的Linear RNA不同，因為其環狀的外型不會受到RNA外切酶影響，使它不易降解，在細胞中也顯得比一般線性RNA來的穩定。由於有前人研究指出有一種RNA binding protein “QKI” 會結合在pre-mRNA的頭尾兩端，使其剪切並形成頭尾相接， 所以在本篇論文中，我們想要利用環狀RNA的junction site資料以及RNA結合蛋白的binding site資料來討論環狀RNA的生成以及功能。這些資料被用來建構出一個觀察RNA結合蛋白是否位於環狀RNA junction site上下游的矩陣，使我們可以利用雙分群演算法的套件：ISA來進行分析。用ISA分析所得到的module會被我們使用Gene Ontology和KEGG pathway 富集分析。在Gene Ontology富集分析方面，我們發現RNA processing 相關的機制對於RNA結合蛋白是有潛在力的，這跟QKI研究相符。然後再KEGG pathway 富集分析方面，我們找到了fatty acid degradation、ABC transporter以及TCA cycle等機制，而阿茲海默症的形成會與脂肪酸的降解相關，且在一篇論文中有提到阿茲海默症的形成會與環狀RNA有關聯性。

Circular RNAs (circRNAs) are types of noncoding RNAs, which are different from linear RNAs. Since the circular structure makes circRNAs are not be influenced by Exoribonucleases, they do not degrade by removing terminal nucleotide from either the 5’ end or 3’ end of the RNA molecules. Previous studies reported that an RNA binding protein, quaking (QKI), can bind on the two sides of pre-mRNA and then generate circRNAs. In this study, we investigated biogenesis of circRNAs using the circRNAs junction sites and protein binding sites in RNAs. These data used to build a binding site matrix, and then we performed bicluster algorithm using ISA package for identifying potential RNA binding proteins (RBPs) which may participate the biogenesis of circRNAs. For each bicluster module, we used circRNAs to perform Gene Ontology and KEGG pathway enrichment analysis. From Gene Ontology enrichment analysis, we discovered that RNA processing associated mechanisms are enriched in potential RNA binding proteins. Furthermore, some mechanisms also found in KEGG enrichment analysis, such as fatty acid degradation, ABC transporters, and TCA cycle. The Alzheimer’s disease formation associated with fatty acid degradation is known mechanism.

中文摘要………………………………………………………………………………i
Abstract……………………………………………………………………………ii
目錄…………………………………………………………………………………iii
表目錄………………………………………………………………………………iv
圖目錄…………………………………………………………………………………v
第一章 前言………………………………………………………………………1
第一節 研究背景………………………………………………………………1
一、CircRNA概述…………………………………………………………1
二、CircRNA資料庫………………………………………………………3
第二章 研究材料與方法……………………………………………………………4
第一節 材料來源………………………………………………………………4
一、CircNet資料庫………………………………………………………4
第二節 分析工具………………………………………………………………6
一、Cluster algorithm分群演算法…………………………………………6
二、Bicluster algorithm 雙分群演算法……………………………………7
三、ISA……………………………………………………………………10
四、Enrichment analysis 富集分析……………………………………….12
第三節 分析方法……………………………………………………………13
一、biogenesis……………………………………………………………13
二、function study…………………………………………………………17
第三章 研究結果………………………………………………………………20
第四章 討論………………………………………………………………22
第五章 結論與未來展望………………………………………………………………23
第六章 附表………………………………………………………………24
第七章 參考文獻………………………………………………………………35

1.Rybak-Wolf A, Stottmeister C, Glazar P, Jens M, Pino N, Giusti S, Hanan M, Behm M, Bartok O, Ashwal-Fluss R et al: Circular RNAs in the Mammalian Brain Are Highly Abundant, Conserved, and Dynamically Expressed. Molecular cell 2015, 58(5):870-885.
2.Suzuki H, Zuo Y, Wang J, Zhang MQ, Malhotra A, Mayeda A: Characterization of RNase R-digested cellular RNA source that consists of lariat and circular RNAs from pre-mRNA splicing. Nucleic acids research 2006, 34(8):e63.
3.Ashwal-Fluss R, Meyer M, Pamudurti NR, Ivanov A, Bartok O, Hanan M, Evantal N, Memczak S, Rajewsky N, Kadener S: circRNA biogenesis competes with pre-mRNA splicing. Molecular cell 2014, 56(1):55-66.
4.Starke S, Jost I, Rossbach O, Schneider T, Schreiner S, Hung LH, Bindereif A: Exon circularization requires canonical splice signals. Cell reports 2015, 10(1):103-111.
5.Wang Y, Wang Z: Efficient backsplicing produces translatable circular mRNAs. Rna 2015, 21(2):172-179.
6.Zhang XO, Wang HB, Zhang Y, Lu X, Chen LL, Yang L: Complementary sequence-mediated exon circularization. Cell 2014, 159(1):134-147.
7.Liang D, Wilusz JE: Short intronic repeat sequences facilitate circular RNA production. Genes & development 2014, 28(20):2233-2247.
8.Kramer MC, Liang D, Tatomer DC, Gold B, March ZM, Cherry S, Wilusz JE: Combinatorial control of Drosophila circular RNA expression by intronic repeats, hnRNPs, and SR proteins. Genes & development 2015, 29(20):2168-2182.
9.Conn SJ, Pillman KA, Toubia J, Conn VM, Salmanidis M, Phillips CA, Roslan S, Schreiber AW, Gregory PA, Goodall GJ: The RNA binding protein quaking regulates formation of circRNAs. Cell 2015, 160(6):1125-1134.
10.Ivanov A, Memczak S, Wyler E, Torti F, Porath HT, Orejuela MR, Piechotta M, Levanon EY, Landthaler M, Dieterich C et al: Analysis of intron sequences reveals hallmarks of circular RNA biogenesis in animals. Cell reports 2015, 10(2):170-177.
11.Chen LL: The biogenesis and emerging roles of circular RNAs. Nature reviews Molecular cell biology 2016, 17(4):205-211.
12.Kos A, Dijkema R, Arnberg AC, van der Meide PH, Schellekens H: The hepatitis delta (delta) virus possesses a circular RNA. Nature 1986, 323(6088):558-560.
13.Sanger HL, Klotz G, Riesner D, Gross HJ, Kleinschmidt AK: Viroids are single-stranded covalently closed circular RNA molecules existing as highly base-paired rod-like structures. Proceedings of the National Academy of Sciences of the United States of America 1976, 73(11):3852-3856.
14.Danan M, Schwartz S, Edelheit S, Sorek R: Transcriptome-wide discovery of circular RNAs in Archaea. Nucleic acids research 2012, 40(7):3131-3142.
15.Soma A, Onodera A, Sugahara J, Kanai A, Yachie N, Tomita M, Kawamura F, Sekine Y: Permuted tRNA genes expressed via a circular RNA intermediate in Cyanidioschyzon merolae. Science 2007, 318(5849):450-453.
16.Li-Pook-Than J, Bonen L: Multiple physical forms of excised group II intron RNAs in wheat mitochondria. Nucleic acids research 2006, 34(9):2782-2790.
17.Molina-Sanchez MD, Martinez-Abarca F, Toro N: Excision of the Sinorhizobium meliloti group II intron RmInt1 as circles in vivo. The Journal of biological chemistry 2006, 281(39):28737-28744.
18.Li JH, Liu S, Zhou H, Qu LH, Yang JH: starBase v2.0: decoding miRNA-ceRNA, miRNA-ncRNA and protein-RNA interaction networks from large-scale CLIP-Seq data. Nucleic acids research 2014, 42(Database issue):D92-97.
19.Memczak S, Jens M, Elefsinioti A, Torti F, Krueger J, Rybak A, Maier L, Mackowiak SD, Gregersen LH, Munschauer M et al: Circular RNAs are a large class of animal RNAs with regulatory potency. Nature 2013, 495(7441):333-338.
20.Ghosal S, Das S, Sen R, Basak P, Chakrabarti J: Circ2Traits: a comprehensive database for circular RNA potentially associated with disease and traits. Frontiers in genetics 2013, 4:283.
21.Liu YC, Li JR, Sun CH, Andrews E, Chao RF, Lin FM, Weng SL, Hsu SD, Huang CC, Cheng C et al: CircNet: a database of circular RNAs derived from transcriptome sequencing data. Nucleic acids research 2016, 44(D1):D209-215.
22.Azuaje F: A cluster validity framework for genome expression data. Bioinformatics 2002, 18(2):319-320.
23.Bergmann S, Ihmels J, Barkai N: Iterative signature algorithm for the analysis of large-scale gene expression data. Physical review E, Statistical, nonlinear, and soft matter physics 2003, 67(3 Pt 1):031902.
24.Datta S, Datta S: Comparisons and validation of statistical clustering techniques for microarray gene expression data. Bioinformatics 2003, 19(4):459-466.
25.Frith MC, Li MC, Weng Z: Cluster-Buster: Finding dense clusters of motifs in DNA sequences. Nucleic acids research 2003, 31(13):3666-3668.
26.Chen B, He J, Pellicer S, Pan Y: Using hybrid hierarchical K-means (HHK) clustering algorithm for protein sequence motif super-rule-tree (SRT) structure construction. International journal of data mining and bioinformatics 2010, 4(3):316-330.
27.Prelic A, Bleuler S, Zimmermann P, Wille A, Buhlmann P, Gruissem W, Hennig L, Thiele L, Zitzler E: A systematic comparison and evaluation of biclustering methods for gene expression data. Bioinformatics 2006, 22(9):1122-1129.
28.Cheng Y, Church GM: Biclustering of expression data. Proceedings / International Conference on Intelligent Systems for Molecular Biology ; ISMB International Conference on Intelligent Systems for Molecular Biology 2000, 8:93-103.
29.Van Mechelen I, Bock HH, De Boeck P: Two-mode clustering methods: a structured overview. Statistical methods in medical research 2004, 13(5):363-394.
30.Ramaswamy S, Tamayo P, Rifkin R, Mukherjee S, Yeang CH, Angelo M, Ladd C, Reich M, Latulippe E, Mesirov JP et al: Multiclass cancer diagnosis using tumor gene expression signatures. Proceedings of the National Academy of Sciences of the United States of America 2001, 98(26):15149-15154.