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研究生: 鄺芷君
Kuong, Chi-Kuan
論文名稱: 透過 Cp190和CTCF的缺失HiC實驗了解建築蛋白在果蠅染色體三結構角色
Loss of Cp190 and CTCF defines a genomic map of architectural elements in Drosophila Genomics of Drosophila architectural elements
指導教授: 張家銘
Chang, Jia-Ming
口試委員: 蘇家玉
Su, Chia-Yu
陳世淯
Chen, Shih-Yu
學位類別: 碩士
Master
系所名稱: 理學院 - 資訊科學系
論文出版年: 2020
畢業學年度: 108
語文別: 英文
論文頁數: 42
中文關鍵詞: 建築蛋白絕緣子
外文關鍵詞: architectural proteins, insulator
DOI URL: http://doi.org/10.6814/NCCU202001731
相關次數: 點閱:91下載:0
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  • 在哺乳類動物中,TAD的邊界上富集了CTCF與cohesin的複合物。然而科學家發現在果蠅身上並未能找到CTCF/cohesin, 而富集在果蠅的TAD邊界上更多的是BEAF-32/CP190 或是 BEAF-32/Chromator。本文透過利用各種資料視覺化方法呈現建築蛋白以及與其結合位點間的相互作用與關係,從而推論昆蟲和哺乳動物是否通過不同的機制來構建TAD,結構蛋白對於昆蟲和哺乳動物基因組的折疊是否同樣重要。
    在這次研究中使用了Chip-Seq,Hi-C和RNA-Seq數據,並且開發了相應的算法以證明在果蠅身上的絕緣子蛋白結合機制中,CP190在招募其他絕緣子並形成絕緣複合體中擔任了關鍵角色。


    CTCF enriched around Mammals’ TAD boundaries, colocalized with cohesin complex, is one of Chromatin architectural proteins. However, instead of the absence of CTCF/cohesin, BEAF-32/CP190 and BEAF-32/Chromator were found at Drosophila’s TAD boundaries. The architectural proteins and their binding sites were used to probe into the relationships between architectural proteins via various visualization data approaches. And extending a provocative question of whether architectural proteins are equally important for proper folding of the insect and vertebrate genomes. Here, we analyzed Chip-Seq, Hi-C, and RNA-Seq data and developed a couple of analysis tools to identify the insulator protein binding mechanism in Drosophila. Cp190 is shown as a critical player in recruiting other insulators and forming an insulation complex.

    1. Introduction 1
    1.1. High-throughput Chromatin Conformation Capture (Hi-C) 1
    1.2. Topologically associating domains (TAD) 2
    1.3. The loop-extrusion model in mammals 2
    1.4. Insulator proteins in Drosophila 3
    2. Dataset and Experiments 5
    2.1. Chip-seq 5
    2.2. Hi-C 5
    2.3. RNA-seq 5
    3. Analysis 6
    3.1. Chip-seq Data Processing 6
    3.1.1. Wild Type 6
    3.1.2. True Insulator Sites 6
    3.2. Hi-C Data Processing 6
    3.2.1. Mapping 6
    3.2.2. Validated Contact Pair 7
    3.2.3. The clustering of Hi-C data 7
    3.2.4. Normalization 8
    3.3. Single insulator site analysis 9
    3.3.1. Virtual 4C 9
    3.3.2. Cis-decay curve 10
    3.3.3. Scaling Factor 12
    3.3.4. Insulation Curve 13
    3.4. Pairwise contact intensity of two insulator sites 15
    3.5. TAD 16
    3.5.1. Define TAD boundary 16
    3.5.2. TAD Overlap 16
    3.6. RNA-Seq processing 17
    4. Results 18
    4.1. Comprehensive insulator classes 18
    4.2. High-quality Hi-C Map 19
    4.3. True insulator site 23
    4.3.1. multiHiCcompare make Hi-C experiments compatible 23
    4.3.2. Insulation Curve of MCIFS increasing in CP190-KO and CTCF-KO data 27
    4.3.3. Select True Insulator Sites 31
    4.3.4. Pairwise Contact Intensity 33
    4.4. Highly correlated RNA-Seq data 35
    4.5. Interactive analysis tools 36
    4.6. Availability of data and materials 38
    5. Discussion and Conclusion 39
    References 40

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