本文選題：疾病相似性　切入點(diǎn)：疾病網(wǎng)絡(luò)　出處：《華東理工大學(xué)》2015年博士論文

【摘要】：如何從疾病組織和正常組織的表達(dá)譜數(shù)據(jù)中挖掘轉(zhuǎn)錄調(diào)控機(jī)制并比較其間的差異一即差異調(diào)控-成為人們迫切想揭示的問題之一。本文基于差異共表達(dá)分析結(jié)果,對差異共表達(dá)分析結(jié)果進(jìn)行調(diào)控關(guān)系的鑒定,并將差異調(diào)控信息進(jìn)行全方位的圖形化展示,最后,對差異調(diào)控基因進(jìn)行重要性排序,將更有能力捕獲差異共表達(dá)基因或基因?qū)Φ牟町愓{(diào)控基因給予更高的重要性打分,以上方發(fā)被成功實(shí)現(xiàn)為R工具包-DCGLv2(本工作對應(yīng)本文第2章內(nèi)容)。如今,人們開始關(guān)注疾病之間錯綜復(fù)雜的關(guān)系,因?yàn)檫@不僅有助于了解疾病譜的全貌,還提供了全新的視角進(jìn)行疾病病因、發(fā)病機(jī)制的研究,以及藥物的開發(fā)和治療策略的探索。利用上述開發(fā)的用于疾病組織和正常組織間的差異調(diào)控分析工具-DCGLv2,我們在108種疾病中鑒定出1,326對顯著的疾病相關(guān)關(guān)系,并發(fā)現(xiàn)由差異共表達(dá)屬性得到的疾病間相關(guān)關(guān)系比由差異表達(dá)屬性得到的疾病間相關(guān)關(guān)系更符合已知的分子生物學(xué)發(fā)現(xiàn),同時(shí)我們還對來自同一組織的多種疾病和來自不同組織的同一疾病進(jìn)行了分析,發(fā)現(xiàn)疾病間相似性同時(shí)受疾病的種類和發(fā)病組織兩方面影響。另外,我們通過一個子疾病網(wǎng)絡(luò)詳細(xì)示例了如何挖掘疾病關(guān)系對中共有的失調(diào)機(jī)制,且試圖證明共有的失調(diào)機(jī)制是引起疾病的共同原因(本工作對應(yīng)本文第3章內(nèi)容)。為了方便研究者使用自產(chǎn)數(shù)據(jù)挖掘疾病間相關(guān)關(guān)系,我們將上述鑒定疾病間相似性(即疾病相關(guān)關(guān)系)的方法實(shí)現(xiàn)為DSviaDRM工具包。該工具包主要包含了五個鑒定疾病相關(guān)關(guān)系的函數(shù)(DCEA、DCpathway、DS、comDCGL和comDCGLplot),為首個基于基因轉(zhuǎn)錄組數(shù)據(jù)間功能失調(diào)機(jī)制的相似性來鑒定疾病間相關(guān)關(guān)系的工具,為臨床醫(yī)生和生物醫(yī)學(xué)研究者提供了研究疾病的工具(本工作對應(yīng)本文第4章內(nèi)容)。另外,在基因表達(dá)研究領(lǐng)域,位點(diǎn)特異性表達(dá)參與了許多重要的生物學(xué)機(jī)制,也越來越受到人們重視。本文介紹了兩種基于二代測序數(shù)據(jù)鑒定位點(diǎn)特異性表達(dá)的方法(pDNAar和mREF),并比較了兩種方法的優(yōu)劣,發(fā)現(xiàn)在理想條件下mREF更高效,這項(xiàng)工作為今后位點(diǎn)特異性表達(dá)的鑒定提供了方法學(xué)支持(本工作對應(yīng)本文第5章內(nèi)容)�？傮w而言,我們開發(fā)了一種能精確挖掘差異調(diào)控信息的R工具包-DCGLv2,并利用DCGL v2計(jì)算得到大量疾病的差異調(diào)控信息,然后比較疾病間差異調(diào)控信息的相似度來衡量疾病的相似度,并最終將鑒定疾病間相似度的方法實(shí)現(xiàn)為另一個R工具包--DSviaDRM。另一方面,我們比較了兩種鑒定位點(diǎn)特異性表達(dá)的方法,為今后的位點(diǎn)特異性表達(dá)的研究提供了支持。
[Abstract]:How to excavate the transcriptional regulation mechanism from the expression profile data of disease tissues and normal tissues and compare the difference between them, namely differential regulation, has become one of the urgent problems that people want to reveal.Based on the results of differential co-expression analysis, this paper identifies the regulatory relationship of differential co-expression analysis results, and displays the differential regulation information in all directions. Finally, the importance of differentially regulated genes is ranked.The higher importance of differentially regulated genes, which are more capable of capturing differentially expressed genes or pairs of genes, was successfully implemented as R Toolkit -DCGLv2 (this work corresponds to Chapter 2 of this paper).Today, people are beginning to pay attention to the intricate relationship between diseases, because it not only helps to understand the full picture of disease spectrum, but also provides a new perspective to study the etiology and pathogenesis of diseases.And the development of drugs and the exploration of treatment strategies.Using the above developed differential regulation analysis tool DCGLv2 for disease tissues and normal tissues, we identified 1326 pairs of significant disease-related relationships in 108 diseases.It was also found that the disease correlation obtained from differential co-expression attributes was more consistent with known molecular biological findings than the disease correlation obtained from differential expression attributes.At the same time, we also analyzed many diseases from the same tissue and the same disease from different tissues. It was found that the similarity between diseases was affected by both the disease types and the diseased tissues.In addition, we use a sub-disease network to illustrate how to discover the common maladjustment mechanism in disease relationship pairs, and try to prove that common maladjustment mechanism is the common cause of disease. (this work corresponds to the contents of Chapter 3 of this paper.In order to facilitate researchers to use native data to mine the correlation between diseases, the above method of identifying disease similarity (i.e. disease correlation) is implemented as DSviaDRM Toolkit.The toolkit consists of five functions to identify disease-related relationships, DCEAV DCpath way DS.com DCGL and comDCGLplotl. It is the first tool to identify disease-related relationships based on the similarity of dysfunctional mechanisms between gene-transcriptional data.It provides tools for clinicians and biomedical researchers to study diseases (this work corresponds to Chapter 4 of this paper).In addition, in the field of gene expression, locus specific expression has participated in many important biological mechanisms, and has been paid more and more attention.In this paper, two site-specific expression methods based on second-generation sequencing data were introduced, and the advantages and disadvantages of the two methods were compared. It was found that mREF was more efficient under ideal conditions.This work provides methodological support for the identification of site-specific expression in the future (this work corresponds to Chapter 5 of this paper).In general, we developed a R Toolkit-DCGLv2, which can accurately mine differential regulation information, and calculate a large number of disease differential control information by DCGL v2, then compare the similarity of differential regulation information between diseases to measure disease similarity.Finally, the method of identifying the similarity between diseases is realized as another R toolkit, DSvia DRM.On the other hand, we compared two methods of locus specific expression, which provided support for the future research of locus specific expression.
【學(xué)位授予單位】：華東理工大學(xué)
【學(xué)位級別】：博士
【學(xué)位授予年份】：2015
【分類號】：R3416;Q811.4

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