本文關(guān)鍵詞：基于拓?fù)涠?jí)結(jié)構(gòu)和閱讀框識(shí)別不同細(xì)胞器基因組的非編碼RNA　出處：《內(nèi)蒙古大學(xué)》2016年博士論文　論文類(lèi)型：學(xué)位論文

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【摘要】：隨著功能基因組學(xué)的飛速發(fā)展,對(duì)非編碼轉(zhuǎn)錄產(chǎn)物功能的研究引起越來(lái)越多人的關(guān)注。人類(lèi)基因組中超過(guò)98%的序列為非蛋白質(zhì)編碼DNA,這些非編碼DNA序列大部分會(huì)轉(zhuǎn)錄成RNA,并且直接以RNA的形式發(fā)揮功能,參與基因的轉(zhuǎn)錄調(diào)控、蛋白質(zhì)的翻譯等重要的生物學(xué)功能。而且近年來(lái)人們發(fā)現(xiàn)非編碼RNA往往與疾病、DNA損傷修復(fù)、植物的應(yīng)激反應(yīng)有關(guān)。隨著RNA數(shù)據(jù)的積累,細(xì)胞核基因組編碼的短鏈或長(zhǎng)鏈非編碼RNA(Non-coding RNA,ncRNA)被確認(rèn)對(duì)線(xiàn)粒體的功能以及線(xiàn)粒體動(dòng)力學(xué)有一定的調(diào)節(jié)作用。許多ncRNAs一方面調(diào)控與細(xì)胞器功能相關(guān)的核基因,同時(shí)也與線(xiàn)粒體的形態(tài)、新陳代謝,線(xiàn)粒體白噬以及與線(xiàn)粒體有關(guān)的凋亡有關(guān)。然而,人們對(duì)遺傳信息在不同細(xì)胞器間以ncRNAs形式傳遞的了解長(zhǎng)期以來(lái)仍然有限。因此,理解細(xì)胞器之間的ncRNAs信息傳遞顯得非常重要。隨著ncRNA研究在細(xì)胞器基因組水平的日益深入,發(fā)現(xiàn)對(duì)不同細(xì)胞器基因組轉(zhuǎn)錄的ncRNA的識(shí)別有助于進(jìn)一步了解不同細(xì)胞器基因組ncRNA的功能。本研究在細(xì)胞器基因組水平對(duì)不同細(xì)胞器基因組轉(zhuǎn)錄的ncRNAs的注釋問(wèn)題進(jìn)行了系統(tǒng)的研究,包括構(gòu)建細(xì)胞器基因組水平不同定位信息的非編碼RNAs數(shù)據(jù)集、結(jié)合ncRNA序列和結(jié)構(gòu)特征提取有效的特征參數(shù)并對(duì)參數(shù)進(jìn)行優(yōu)化、預(yù)測(cè)算法的建立以及算法的推廣等。積累的組學(xué)數(shù)據(jù)所呈現(xiàn)的基因水平的復(fù)雜性很難從蛋白質(zhì)編碼基因數(shù)量的角度加以解釋,為此,有人認(rèn)為長(zhǎng)期以來(lái)被認(rèn)為是垃圾的、缺乏蛋白質(zhì)編碼能力的ncRNA的調(diào)節(jié)作用可以解釋這種現(xiàn)象。其中,微小RNAs (microRNAs, miRNAs)和小干涉RNAs (siRNAs)一致被認(rèn)為在生物的調(diào)控功能中扮演重要角色。本論文最后,以microRNA為例,研究非編碼RNA與靶基因?qū)θ橄侔┑陌l(fā)生、發(fā)展的調(diào)控作用�？紤]到多個(gè)microRNA對(duì)靶基因存在協(xié)同調(diào)控作用,我們選取在乳腺癌中起抑癌作用的miR-17-92基因簇及其2個(gè)旁系同源基因簇轉(zhuǎn)錄的1 5種microRNA序列及其共調(diào)控靶基因作為研究對(duì)象,對(duì)基因簇轉(zhuǎn)錄的microRNA序列特征及其共調(diào)控靶基因在乳腺正常組織和乳腺癌組織中的調(diào)控作用進(jìn)行研究。論文主要的研究?jī)?nèi)容如下：一、我們首次從NONCOGING v3.0數(shù)據(jù)庫(kù)中收集并整理出有細(xì)胞器基因組注釋的ncRNA序列,并對(duì)序列長(zhǎng)度分布進(jìn)行分析�？紤]到序列相似性對(duì)預(yù)測(cè)的影響,進(jìn)一步采用Cd-hit軟件構(gòu)建了序列相似性在80%以下的數(shù)據(jù)集ncRNA_361 dataset。從最簡(jiǎn)單的堿基物理化學(xué)特性出發(fā),討論不同細(xì)胞器基因組轉(zhuǎn)錄的ncRNA序列的理化特性。在此基礎(chǔ)上進(jìn)一步考慮閱讀框下的n-mer組分偏好,結(jié)構(gòu)-序列模式下三聯(lián)體組分,以及簡(jiǎn)并密碼子偏好。通過(guò)深入探討無(wú)閱讀框與閱讀框?qū)ψR(shí)別不同細(xì)胞器基因組轉(zhuǎn)錄的ncRNA序列的影響,發(fā)現(xiàn)最優(yōu)閱讀框?yàn)榈谝婚喿x框。二、考慮到ncRNA的結(jié)構(gòu)信息更能反映ncRNA執(zhí)行功能時(shí)的空間構(gòu)象,而保守模體反映了序列在長(zhǎng)期進(jìn)化過(guò)程中的壓力。我們首次提取ncRNA序列的拓?fù)涠?jí)結(jié)構(gòu)特征和保守模體作為在細(xì)胞器基因組水平識(shí)別ncRNA的特征參量。特征的融合不可避免會(huì)帶來(lái)維數(shù)的增加,我們結(jié)合前人的經(jīng)驗(yàn),提出兩種不同的降維方法：一是特征的降維映射,二是基于mRMR的增加特征選擇(IFS)的方法,即選取最優(yōu)特征子集。結(jié)合目前較為流行的離散增量算法(increment of diversity classifier, ID)、K緊鄰算法(K-nearest neighbor classifier, KNN)以及支持向量機(jī)算法(support vector machine, SVM)提出多算法的融合：改進(jìn)的離散量結(jié)合K緊鄰算法(the improved K-minimum increment of diversity classifier, iK-MID)、高效的平均K緊鄰算法(the improved K-nearest neighbor classifier, iKNN)以及離散增量結(jié)合支持向量機(jī)算法(the increment of diversity combining support vector machine,ID-SVM)。最后,通過(guò)不同算法之間的相互比較,探索更加有效的細(xì)胞器基因組ncRNA識(shí)別的理論模型。三、應(yīng)用生物信息學(xué)手段,對(duì)特定miRNA基因簇(hsa-miR-17-92基因簇)及其旁系同源基因簇轉(zhuǎn)錄的miRNA序列特征及其共調(diào)控靶基因在乳腺不同組織的表達(dá)水平進(jìn)行了研究,并利用反饋機(jī)制簡(jiǎn)單解釋該miRNA對(duì)下游基因的調(diào)控機(jī)制,為生物學(xué)實(shí)驗(yàn)提供了有一定研究意義和價(jià)值的線(xiàn)索。
[Abstract]:With the rapid development of functional genomics, research on non encoding transcription function has attracted more and more attention. More than 98% of the sequence in the human genome is a non protein encoding DNA, these non DNA sequences encoding most transcribed into RNA, and directly in the form of RNA function, participate in gene transcription regulation, biological function protein translation and so on. But in recent years it was found that non encoding RNA are often associated with the disease, DNA damage repair, the stress response of plants. With the RNA data, the nuclear genome encoding short or long chain non encoding RNA (Non-coding RNA ncRNA) was identified on mitochondrial function and regulation of mitochondrial dynamics effect of regulation of nuclear gene related to cell function of many ncRNAs on the one hand, and the morphology of mitochondria, mitochondrial autophagy and The new supersedes the old., and line Particles related to apoptosis. However, the people of the genetic information in the form of ncRNAs in different organelles of understanding between a long time is still limited. Therefore, understanding the organelle ncRNAs information transmission is very important. With the increasingly ncRNA research in organelle genome level further, found that the identification of different organelle genome transcription the ncRNA helps to further understand the different organelle genomes ncRNA function. This study makes a systematic research on the organelle genome level annotation on different organelle genome transcription of ncRNAs, encoding non RNAs data including the construction of organelle genomes with different levels of positioning information set, combined with ncRNA sequence and structure feature extraction the effective feature parameters and optimizes the parameters prediction algorithm is established and the algorithm for the promotion. The data of gene water accumulation group Flat complexity is very difficult from the amount of protein encoding genes explain, therefore, some people think that has long been regarded as waste, lack of regulation of protein encoding ability of ncRNA can explain this phenomenon. Among them, the small RNAs (microRNAs, miRNAs) and small interference RNAs (siRNAs) was found to play an important role in the regulation of biological function. At the end of the thesis, taking microRNA as an example, study the non occurrence of encoding RNA and target gene for breast cancer, regulation of development. Considering the multiple microRNA synergistic regulation of target genes, we selected in breast cancer within 15 microRNA sequence of miR-17-92 gene cluster of tumor suppressor role and 2 paralogous gene clusters and co transcriptional regulation of target genes as the research object, the sequence characteristics of microRNA gene clusters and co transcriptional regulation of target genes in normal tissues and breast Regulation of breast cancer tissues were studied. The main research contents of this paper are as follows: first, we for the first time from the NONCOGING V3.0 database to collect and sort out the ncRNA sequence of organelle genome annotation, and the sequence length distribution were analyzed. Considering the influence of sequence similarity to predict, further uses the Cd-hit software to build the sequence the similarity in the 80% following data sets ncRNA_361 dataset. base starting from the physical and chemical characteristics of the most simple, to discuss the physicochemical properties of different ncRNA sequences of organelle genome transcription. On the basis of further consideration of reading n-mer group box the preference structure sequence mode three CIS components, as well as degenerate codon preference No. Through discussing the reading frame and reading frame influence ncRNA sequence recognition of different organelle genome transcription, find the optimal reading frame for the first reading frame two. And considering the structure information of ncRNA could reflect the spatial conformation of ncRNA when executing the function, and the conserved motif sequence reflects the pressure in the long process of evolution. We first extract two features of ncRNA sequences and topological motifs as organelles in the genome level general characteristic parameters of ncRNA. Don't increase the feature integration will inevitably bring dimension, we combined with previous experience, put forward two kinds of different reduction methods: one is the reduction characteristics of the two is to increase the feature selection based on mRMR (IFS) method, which selects the optimal feature subset. Combined with the discrete incremental algorithm popular (increment of diversity classifier. ID K (K-nearest), close to the neighbor classifier KNN algorithm) and the support vector machine algorithm (support vector machine, SVM) proposed fusion algorithm: discrete combined with improved K close to the count (the improved K-minimum increment of by diversity classifier, iK-MID), the average K efficient algorithm (the improved K-nearest neighbor near classifier, iKNN) and combined with the discrete incremental support vector machine algorithm (the increment of diversity combining support vector machine, ID-SVM). Finally, through the comparison between different methods, explore the theoretical model of cell genomic ncRNA recognition more effectively. Three, application of bioinformatics to specific miRNA gene cluster (hsa-miR-17-92 cluster) miRNA sequence characteristics and paralogous gene cluster and its transcriptional co regulation the expression level of target genes in different tissues of the breast were studied, and the feedback mechanism of the regulation mechanism of miRNA simple explanation on the downstream gene, provides certain research significance and valuable clues for biological experiments.

【學(xué)位授予單位】：內(nèi)蒙古大學(xué)
【學(xué)位級(jí)別】：博士
【學(xué)位授予年份】：2016
【分類(lèi)號(hào)】：Q811.4

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