【摘要】：隨著基因組測(cè)序的完成和新一代測(cè)序技術(shù)的發(fā)展,人類已經(jīng)掌握了大量生物數(shù)據(jù),而且,基因以及蛋白質(zhì)與蛋白質(zhì)相互作用網(wǎng)絡(luò)方面的數(shù)據(jù)也在不斷更新及豐富。通過這些數(shù)據(jù)來分析肥胖與疾病之間的關(guān)系和發(fā)現(xiàn)影響這種聯(lián)系的關(guān)鍵基因,是研究與肥胖相關(guān)的疾病機(jī)理的重要方法,對(duì)基因組學(xué)和醫(yī)學(xué)也具有現(xiàn)實(shí)意義。眾所周知,肥胖與多種疾病有關(guān),是許多疾病的主要危險(xiǎn)因素,如II型糖尿病、冠心病和心血管疾病等。然而,肥胖在相關(guān)疾病的發(fā)展中起著重要的作用還沒有被很好地理解。而且,目前也缺乏對(duì)于肥胖和相關(guān)疾病之間的全面研究。為了解決這個(gè)問題,我們構(gòu)造三種不同的網(wǎng)絡(luò)分析算法,第一個(gè)名字是OBNet,它主要是基于一個(gè)類似基因集富集分析和一個(gè)隨機(jī)游走過程的算法;第二個(gè)算法叫OBsp,是一種基于最短路徑的算法;最后一個(gè)叫OBoverlap,是基于直接求交集算法。我們分析比較了三種算法,發(fā)現(xiàn)基于擴(kuò)展的模塊化網(wǎng)絡(luò)的OBNet方法是最優(yōu)的,然后我們用這種方法來進(jìn)一步研究肥胖與其相關(guān)疾病之間的分子層次的關(guān)聯(lián)和潛在的功能聯(lián)系,并有助于臨床醫(yī)學(xué)的深入認(rèn)識(shí)。本文主要基于肥胖基因和疾病基因數(shù)據(jù),提出了一種新的研究肥胖和疾病全局關(guān)系的網(wǎng)絡(luò)分析方法,主要完成了以下兩個(gè)方面工作:1)提出三種不同的網(wǎng)絡(luò)分析算法從全局角度來分析肥胖和疾病之間的關(guān)系。通過比較三種不同算法的結(jié)果,選擇OBNet-基于擴(kuò)展的模塊化網(wǎng)絡(luò)可以更好的鑒定肥胖和疾病之間的關(guān)系。根據(jù)OBNet-基于擴(kuò)展的模塊化網(wǎng)絡(luò)方法,我們可以找到與肥胖關(guān)系密切的一些疾病,以及與肥胖相關(guān)的疾病在哪些通路或子網(wǎng)絡(luò)上與肥胖顯著富集關(guān)聯(lián)。最后具體分析了兩個(gè)特定疾病,預(yù)測(cè)了調(diào)節(jié)這兩個(gè)疾病與肥胖間關(guān)系的關(guān)鍵驅(qū)動(dòng)基因。2)基于乳腺癌基因表達(dá)數(shù)據(jù),WGCNA算法可以得到29個(gè)模塊,我們抽取其中與乳腺癌顯著相關(guān)的前10個(gè)模塊;然后根據(jù)OBNet-基于擴(kuò)展的模塊化網(wǎng)絡(luò)方法,我們可以得到的乳腺癌最顯著富集的前10個(gè)子網(wǎng)絡(luò);把這10個(gè)字網(wǎng)絡(luò)與WGCNA的前10個(gè)模塊相比較,發(fā)現(xiàn)了兩者有顯著重疊,這說明我們的OBNet方法在不依賴基因表達(dá)譜的情況下,也可以找到疾病高度相關(guān)的一些驅(qū)動(dòng)基因和模塊。
[Abstract]:With the completion of genome sequencing and the development of new generation of sequencing technology, human beings have mastered a large number of biological data, and the gene and protein-protein interaction network data are constantly updated and enriched. The analysis of the relationship between obesity and disease and the discovery of the key genes affecting the relationship are important methods to study the mechanism of obesity related diseases, and also have practical significance for genomics and medicine. Obesity is known to be a major risk factor for many diseases, such as type II diabetes, coronary heart disease and cardiovascular disease. However, the important role of obesity in the development of related diseases has not been well understood. Moreover, there is a lack of comprehensive research on obesity and related diseases. In order to solve this problem, we construct three different network analysis algorithms. The first is called OBNet, which is mainly based on a similar gene set enrichment analysis and a random walk process algorithm. The second is called OBsp, which is based on the shortest path, and the last is called OBoverlap, which is based on the direct intersection algorithm. We analyzed and compared three algorithms and found that the OBNet method based on extended modular network is optimal. Then we use this method to further study the molecular level association and potential functional association between obesity and its associated diseases. It is helpful for the further understanding of clinical medicine. Based on the data of obesity gene and disease gene, this paper proposes a new network analysis method to study the global relationship between obesity and disease. The main contributions are as follows: 1) three different network analysis algorithms are proposed to analyze the relationship between obesity and disease from a global perspective. By comparing the results of three different algorithms, the extended modular network based on OBNet- can better identify the relationship between obesity and disease. Based on OBNet- 's extended modular network approach, we can find out some diseases closely related to obesity, and which pathways or sub-networks are significantly associated with obesity enrichment for obesity related diseases. Finally, two specific diseases are analyzed, and the key driving genes to regulate the relationship between the two diseases and obesity are predicted. 2) based on the breast cancer gene expression data, the WGCNA algorithm can get 29 modules. We extracted the top 10 modules significantly related to breast cancer; Then, according to OBNet- 's extended modular network method, we can get the first 10 subnetworks that are most significantly enriched in breast cancer. Comparing these 10 word networks with the first 10 modules of WGCNA it is found that there is a significant overlap between them which indicates that our OBNet method can also find some driving genes and modules which are highly related to the disease without relying on gene expression profiles.
【學(xué)位授予單位】：河北科技大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：R589.2

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本文編號(hào)：2414727