【摘要】：必需基因指的是在優(yōu)化生長(zhǎng)的前提下對(duì)有機(jī)體的生存和生長(zhǎng)不能缺失的基因。研究必需基因有如下重要的意義:(1)必需基因可以作為構(gòu)建最小基因集的基礎(chǔ),通過(guò)對(duì)必需基因的研究可以幫助我們了解生命的起源和進(jìn)化,生產(chǎn)工業(yè)實(shí)用型微生物;(2)必需基因編碼的蛋白質(zhì)通常參與最重要且基礎(chǔ)的代謝過(guò)程,因此,可以作為抗菌藥物的靶標(biāo)。近年來(lái),必需基因的研究已經(jīng)成為生物信息學(xué)研究的熱點(diǎn)之一。本文的研究對(duì)象是由濕實(shí)驗(yàn)方法確定的細(xì)菌必需基因集。其原始的必需基因數(shù)據(jù)來(lái)自于必需基因數(shù)據(jù)庫(kù)DEG(http://tubic.tju.edu.cn/deg/)。受COGs(https://www.ncbi.nlm.nih.gov/COG/)中團(tuán)簇的啟發(fā),我們提出了細(xì)菌必需基因團(tuán)簇模型的概念,就是將具有相同或者相近功能的必需基因以團(tuán)簇的形式進(jìn)行存儲(chǔ),這也是與當(dāng)前大多數(shù)存儲(chǔ)基因的數(shù)據(jù)庫(kù)的最大不同,團(tuán)簇的大小反映了該類基因的保守性強(qiáng)弱。到目前為止,細(xì)菌必需基因數(shù)據(jù)進(jìn)一步豐富,例如DEG的最新版本(截止到2017年3月)收錄了46套細(xì)菌必需基因數(shù)據(jù)集和16套真核生物細(xì)菌的必需基因數(shù)據(jù)集,為相關(guān)研究奠定了基礎(chǔ)。基于必需基因團(tuán)簇模型和最新的數(shù)據(jù),我們構(gòu)建更新了細(xì)菌必需基因團(tuán)簇?cái)?shù)據(jù)庫(kù)(CEG,Cluster of Essential Genes,http://cefg.cn/ceg/),其版本稱之為CEG 2.0。在該數(shù)據(jù)庫(kù)中,以團(tuán)簇的形式存放必需基因,并進(jìn)一步增加并豐富了和必需基因相關(guān)的很多信息,如:增加了基因編碼蛋白質(zhì)結(jié)構(gòu)、基因毒力因子、基因參加的代謝通路以及與基因相關(guān)的藥物等重要信息。另外,我們將細(xì)菌必需基因與人類基因序列作比對(duì),提供用戶兩者的同源性信息。這些信息在新的藥物靶標(biāo)發(fā)掘過(guò)程中,具有極大的借鑒意義。團(tuán)簇的大小也具有重要的生物學(xué)意義,團(tuán)簇越大,其中包含的基因就越保守,用戶通過(guò)觀察團(tuán)簇的大小,就可以直接看出具有該功能的基因的是在多物種中普遍存在的,還是個(gè)別物種所具有的。根據(jù)構(gòu)建的CEG數(shù)據(jù)庫(kù),我們提出了一種新的基于存儲(chǔ)的團(tuán)簇大小來(lái)預(yù)測(cè)細(xì)菌基因必需性的算法—K-value。K-value算法的主要原理是依據(jù)團(tuán)簇的大小進(jìn)行必需基因的預(yù)測(cè),在預(yù)測(cè)的時(shí)候,只需要用戶提供基因的基因名即可對(duì)基因完成預(yù)測(cè)。最后,我們編程實(shí)現(xiàn)了此算法,稱之為CEG_Match。在CEG 2.0中的CEG_Match,我們?cè)黾恿诵碌墓δ?用戶不僅可以根據(jù)基因功能進(jìn)行預(yù)測(cè),還可以根據(jù)基因序列信息進(jìn)行預(yù)測(cè)。該預(yù)測(cè)工具與傳統(tǒng)的必需基因識(shí)別方式比較,在保證不低的準(zhǔn)確率的基礎(chǔ)上,對(duì)非必需基因的識(shí)別率更高,而且執(zhí)行效率更快。這解決了CEG 1.0中的預(yù)測(cè)算法只能根據(jù)基因名進(jìn)行預(yù)測(cè)的缺陷。最后,對(duì)本文構(gòu)建的數(shù)據(jù)庫(kù)信息進(jìn)行統(tǒng)計(jì),包括物種、團(tuán)簇以及基因功能等,并對(duì)以后可開展的工作進(jìn)行了展望。
[Abstract]:Essential genes refer to genes that cannot be deleted from the survival and growth of organisms on the premise of optimal growth. The study of essential genes is of great significance as follows: (1) essential genes can be used as the basis for the construction of the minimum gene set, and the study of essential genes can help us understand the origin and evolution of life and produce industrial practical microorganisms. (2) the proteins encoded by essential genes are usually involved in the most important and basic metabolic process, so they can be used as targets for antibiotics. In recent years, the study of essential genes has become one of the hotspots in bioinformatics research. The research object of this paper is the necessary gene set of bacteria determined by wet experiment. Its original essential gene data comes from the essential gene database DEG (http://tubic.tju.edu.cn/deg/).) Inspired by COGs (cluster in https://www.ncbi.nlm.nih.gov/COG/), we put forward the concept of bacterial essential gene cluster model, that is, to store the necessary genes with the same or similar functions in the form of clusters, which is also the biggest difference from most of the current databases of stored genes, and the size of the clusters reflects the conservatism of this kind of genes. So far, the data of bacterial essential genes have been further enriched, such as the latest version of DEG (up to March 2017), which contains 46 sets of bacterial essential gene data sets and 16 sets of essential gene data sets of eukaryotic bacteria, which lays a foundation for related research. Based on the essential gene cluster model and the latest data, we constructed and updated the bacterial essential gene cluster database (CEG,Cluster of Essential Genes, http://cefg.cn/ceg/),), which is called CEG 2.0. In this database, essential genes are stored in clusters, and a lot of information related to essential genes is further increased and enriched, such as gene coding protein structure, gene virulence factors, metabolic pathways in which genes participate, and gene-related drugs. In addition, we compare the bacterial essential genes with human gene sequences to provide users with homology information. This information has great reference significance in the process of discovering new drug targets. The size of the cluster also has important biological significance, the larger the cluster, the more conservative the genes contained in it. Through the size of the observation cluster, users can directly see whether the gene with this function is common in many species or has it in individual species. According to the constructed CEG database, we propose a new algorithm based on storage cluster size to predict the necessary gene requirements of bacteria. The main principle of K-value.K-value algorithm is to predict the necessary genes according to the size of the cluster. in the prediction, only the gene name of the gene can be provided by the user to complete the prediction of the gene. Finally, we program and implement this algorithm, called CEG_Match.. We add new functions to CEG_Match, in CEG 2.0. Users can predict not only according to gene function, but also according to gene sequence information. Compared with the traditional essential gene recognition method, the prediction tool has higher recognition rate and faster execution efficiency on the basis of ensuring the accuracy of non-essential genes. This solves the defect that the prediction algorithm in CEG 1.0 can only predict according to the gene name. Finally, the database information constructed in this paper is counted, including species, clusters and gene function, and the future work is prospected.
【學(xué)位授予單位】：電子科技大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：Q933

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相關(guān)期刊論文 前2條

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本文編號(hào)：2500958