本文選題：蛋白質(zhì)組學(xué) + 質(zhì)譜　； 參考：《中國人民解放軍軍事醫(yī)學(xué)科學(xué)院》2017年博士論文

【摘要】：蛋白質(zhì)組學(xué)是后基因組時(shí)代生命科學(xué)研究的熱點(diǎn)之一,它研究生物體細(xì)胞、器官乃至組織的蛋白質(zhì)表達(dá)規(guī)律,并闡明其生物學(xué)意義。蛋白質(zhì)組學(xué)研究的重要技術(shù)之一是生物質(zhì)譜技術(shù),對(duì)著生物質(zhì)譜技術(shù)的發(fā)展,促進(jìn)了大規(guī)模蛋白質(zhì)組研究的開展,實(shí)現(xiàn)高通量、高靈敏度和高分辨率的蛋白質(zhì)組學(xué)研究分析平臺(tái)。鳥槍法蛋白質(zhì)組鑒定是蛋白質(zhì)組研究最重要的研究策略:通過實(shí)驗(yàn)產(chǎn)出串聯(lián)質(zhì)譜數(shù)據(jù),通過搜索蛋白質(zhì)序列數(shù)據(jù)庫獲得可靠鑒定肽段結(jié)果,并進(jìn)一步通過蛋白質(zhì)的推導(dǎo)獲得鑒定蛋白質(zhì)結(jié)果。由于質(zhì)譜數(shù)據(jù)的特性,生物樣品多樣、實(shí)驗(yàn)過程復(fù)雜、現(xiàn)有搜索算法和質(zhì)量控制方法局限,盡管數(shù)據(jù)庫搜索策略可以提高生物質(zhì)譜數(shù)據(jù)的解析效率,但仍不能完全解決蛋白質(zhì)鑒定問題。如何保證鑒定結(jié)果的正確性和完整性,是數(shù)據(jù)庫搜索策略的主要問題。隨著質(zhì)譜儀不斷發(fā)展,海量高精度質(zhì)譜數(shù)據(jù)不斷產(chǎn)出,大規(guī)模蛋白質(zhì)組質(zhì)譜數(shù)據(jù)研究的分析方法明顯滯后。質(zhì)譜數(shù)據(jù)分析的瓶頸,已經(jīng)不再是實(shí)驗(yàn)數(shù)據(jù)的產(chǎn)出,而是數(shù)據(jù)的有效分析。因此建立質(zhì)譜數(shù)據(jù)分析平臺(tái),實(shí)現(xiàn)大規(guī)模質(zhì)譜數(shù)據(jù)分析自動(dòng)化實(shí)現(xiàn)十分必要。另一方面,高精度串聯(lián)質(zhì)譜(MS/MS)數(shù)據(jù)所蘊(yùn)含的肽段信息可為基因組解析注入新的思路,從高精度MS/MS數(shù)據(jù)出發(fā),利用基因組數(shù)據(jù)庫搜索,可進(jìn)一步提高質(zhì)譜數(shù)據(jù)解析率。蛋白質(zhì)組基因組學(xué)的研究理念是整合串聯(lián)質(zhì)譜數(shù)據(jù)注釋基因組蛋白質(zhì)編碼基因。本課題致力于基于數(shù)據(jù)庫搜索策略的質(zhì)譜數(shù)據(jù)分析流程的改善、平臺(tái)構(gòu)建及其在人類肝臟蛋白質(zhì)組等大規(guī)模數(shù)據(jù)分析中的應(yīng)用。首先比較譜圖、肽段、蛋白質(zhì)水平質(zhì)量控制方法的嚴(yán)格性,并開發(fā)了針對(duì)Mascot搜索引擎的質(zhì)量控制和蛋白質(zhì)裝配程序ProDistiller;然后探索了常用蛋白質(zhì)序列數(shù)據(jù)庫的區(qū)別及其對(duì)對(duì)鑒定結(jié)果的影響,并依據(jù)我們實(shí)驗(yàn)室長期的數(shù)據(jù)分析經(jīng)驗(yàn),整合質(zhì)譜數(shù)據(jù)分析軟件、構(gòu)建質(zhì)譜數(shù)據(jù)分析平臺(tái)Mass Spectrum Data Processing Pipeline(MSPP)。基于研究發(fā)展的質(zhì)控方法和數(shù)據(jù)分析平臺(tái),我們對(duì)人類染色體蛋白質(zhì)組計(jì)劃產(chǎn)出以及收集的人類肝臟蛋白質(zhì)組的海量數(shù)據(jù)集展開了系統(tǒng)的分析。最后我們建立了基于基因組數(shù)據(jù)庫和預(yù)測蛋白質(zhì)組數(shù)據(jù)庫挖掘新蛋白的數(shù)據(jù)分析流程,實(shí)現(xiàn)了海量人類蛋白質(zhì)組質(zhì)譜數(shù)據(jù)的深度解析。具體內(nèi)容包括:蛋白質(zhì)水平質(zhì)控方法是較譜圖水平、肽段水平質(zhì)控更為嚴(yán)格的質(zhì)量控制方法。尤其對(duì)于復(fù)雜樣本數(shù)據(jù)集,整合實(shí)驗(yàn)數(shù)據(jù)多,蛋白質(zhì)水平累積的假陽性鑒定也多。我們開發(fā)基于PepDistiller結(jié)果進(jìn)行蛋白質(zhì)水平質(zhì)量控制和蛋白質(zhì)裝配的ProDistiller程序,設(shè)置圖譜打分F-value,對(duì)同一個(gè)樣本的圖譜結(jié)果進(jìn)行排序逐個(gè)組裝蛋白,在蛋白水平FDR達(dá)到1%時(shí)停止組裝獲得卡值,蛋白質(zhì)裝配基于簡單原則法。ProDistiller使用Perl語言編寫,可以在多種平臺(tái)下運(yùn)行,結(jié)果中保留肽段鑒定的屬性,如電荷,漏切位點(diǎn)數(shù),母離子和子離子質(zhì)量誤差等。目前常用蛋白質(zhì)組序列數(shù)據(jù)庫有NCBI nr、UniProt、RefSeq、Ensembl等,這幾個(gè)數(shù)據(jù)庫在理論肽段構(gòu)成上基本相似,差別在于存著不同可變剪接形式的蛋白質(zhì)。注釋較好的Uniprot和SwissProt數(shù)據(jù)庫所得到的鑒定結(jié)果要比其它數(shù)據(jù)庫多。另一方面Uniprot和Swiss Prot數(shù)據(jù)庫大小遠(yuǎn)小于Ensembl數(shù)據(jù)庫、RefSeq數(shù)據(jù)庫和NCBI nr數(shù)據(jù)庫,對(duì)計(jì)算所需硬件和時(shí)間需求較小。因此我們建議在常規(guī)的蛋白質(zhì)組質(zhì)譜鑒定的數(shù)據(jù)庫搜索中,數(shù)據(jù)質(zhì)量高、冗余度低的Uniprot和Swiss-Prot數(shù)據(jù)庫是最佳選擇,以基因?yàn)橹行牡难芯靠刹捎肧wiss-Prot為搜索數(shù)據(jù)庫。質(zhì)譜數(shù)據(jù)分析平臺(tái)(MSPP)有效整合并實(shí)現(xiàn)了多種搜索引擎搜索、多水平質(zhì)控和整合、有標(biāo)/無標(biāo)定量等多個(gè)功能模塊,并考慮了多節(jié)點(diǎn)調(diào)度和任務(wù)分配,能夠滿足海量數(shù)據(jù)處理的需求。該平臺(tái)已成功地應(yīng)用于中國人類蛋白質(zhì)組計(jì)劃、人類染色體蛋白質(zhì)組計(jì)劃和人類肝臟蛋白質(zhì)組數(shù)據(jù)集的數(shù)據(jù)分析中,至今已累積處理超過4億張譜圖。隨著蛋白質(zhì)組質(zhì)譜技術(shù)的高速發(fā)展,數(shù)據(jù)規(guī)模逐漸增大,大規(guī)模高通量自動(dòng)化分析,高性能計(jì)算平臺(tái)需要進(jìn)一步優(yōu)化任務(wù)調(diào)度、數(shù)據(jù)分發(fā)和結(jié)果收集,建立高通量、自動(dòng)化的串聯(lián)質(zhì)譜數(shù)據(jù)的新蛋白質(zhì)鑒定平臺(tái)。MSPP成功應(yīng)用于人類染色體蛋白質(zhì)組計(jì)劃中復(fù)雜樣本的數(shù)據(jù)分析。我們對(duì)三組具有不同轉(zhuǎn)移潛能人類肝癌細(xì)胞系樣本Hep3B,HCC97H和HCCLM3進(jìn)行轉(zhuǎn)錄組、翻譯組和蛋白質(zhì)組的深度測序分析,蛋白質(zhì)組學(xué)鑒定9064個(gè)基因,是翻譯組基因總數(shù)的50.2%。其中通過轉(zhuǎn)錄因子富集策略,鑒定到31個(gè)低豐度蛋白質(zhì),證明富集策略對(duì)低豐度蛋白鑒定的有效性。通過樣本特異性數(shù)據(jù)庫搜索,我們發(fā)現(xiàn)SAP只占總鑒定肽段數(shù)目的0.4%,這表明單一氨基酸多態(tài)性對(duì)蛋白質(zhì)鑒定影響很小。為獲得最完整的人類肝臟蛋白質(zhì)組數(shù)據(jù)集,我們系統(tǒng)收集盡可能完整肝臟相關(guān)的質(zhì)譜數(shù)據(jù),記錄樣品狀態(tài),獲得最完整的肝臟質(zhì)譜數(shù)據(jù)第一版。實(shí)驗(yàn)數(shù)據(jù)按照樣本類型分為成人肝、胎肝和肝癌細(xì)胞系三種。使用MSPP用于肝臟質(zhì)譜數(shù)據(jù)重分析,構(gòu)建最新版高可信的人類肝臟蛋白質(zhì)組數(shù)據(jù)集,共鑒定9901個(gè)基因,鑒定結(jié)果遠(yuǎn)遠(yuǎn)高過PeptideAtlas中的現(xiàn)有人類肝臟數(shù)據(jù)集的數(shù)據(jù)量(4,408個(gè)蛋白質(zhì))。與SwissProt和ProteinAtlas中的肝臟組織特異性表達(dá)譜數(shù)據(jù)比較,發(fā)現(xiàn)仍有大量漏檢蛋白質(zhì)。分析其鑒定譜圖的打分情況發(fā)現(xiàn),很多鑒定圖譜并不是打分值低被過濾,而是具有較好打分,導(dǎo)致鑒定結(jié)果存在大量的假陰性。我們建立了基于基因組數(shù)據(jù)庫的數(shù)據(jù)分析流程,初步實(shí)現(xiàn)了海量人類蛋白質(zhì)組質(zhì)譜數(shù)據(jù)的深度解析。使用高精度質(zhì)譜數(shù)據(jù)搜索基因組數(shù)據(jù)庫(理論外顯子連接體數(shù)據(jù)庫)和預(yù)測蛋白質(zhì)AceView數(shù)據(jù)庫,我們發(fā)現(xiàn)了一些圖譜高可信的候選結(jié)果,包括5條可能是新AS的肽段和3條新蛋白肽段。雖然結(jié)果仍需要進(jìn)一步實(shí)驗(yàn)驗(yàn)證,但此次試驗(yàn)證明了基于質(zhì)譜數(shù)據(jù)注釋基因組的可行性,確定了分析方法。
[Abstract]:Proteomics is one of the hotspots in the research of life science in the post genome era. It studies the protein expression of cell, organ and tissue in organism, and clarifies its biological significance. One of the important techniques in the study of proteomics is the bio mass spectrometry technology, which has promoted the development of biomass spectrum technology and promoted the large-scale protein research. A proteomic analysis platform for high flux, high sensitivity and high resolution is carried out. The identification of the proteome of the bird gun method is the most important research strategy in the study of the proteome. Through the experimental production of tandem mass spectrometry data, the results of the peptide segment can be reliably identified by searching the protein sequence database and further through the protein. The results of protein identification are obtained. Because of the characteristics of the mass spectrometry data, the biological samples are diverse, the experimental process is complex, the existing search algorithms and the quality control methods are limited. Although the database search strategy can improve the analytical efficiency of the biological mass spectrometry data, the problem of protein identification can not be completely solved. How to ensure the positive results of the identification is guaranteed. Accuracy and integrity are the main problems of database search strategy. With the continuous development of mass spectrometers, mass high-precision mass spectrometry data are continuously produced, and the analysis method of mass mass spectrometry data analysis is obviously lagging behind. The bottleneck of mass spectrometry data analysis is no longer the output of experimental data, but the effective analysis of data. Therefore, the data analysis is effective. On the other hand, the peptide information contained in the high precision tandem mass spectrometry (MS/MS) data can inject new ideas for genome analysis. From the high precision MS/MS data and the search of the genome database, the analysis rate of mass spectrometry data can be further improved. The idea of proteomic genomics is to integrate tandem mass spectrometry data to annotate genome protein coding genes. This topic is devoted to the improvement of mass spectrometry data analysis process based on database search strategy, platform construction and its application in large-scale data analysis of human liver proteome. First, the comparison of spectrum, peptide, protein The quality control method of quality control and the quality control of Mascot search engine and the protein assembly program ProDistiller are developed. Then the difference of the common protein sequence database and its influence on the identification results are explored, and the mass spectrometry data analysis software is integrated according to our laboratory data analysis experience for a long time. The mass data analysis platform Mass Spectrum Data Processing Pipeline (MSPP). Based on the research and development of the quality control method and data analysis platform, we carried out a series analysis of the mass data set of the human chromosome proteome production and the collection of human liver proteome. Finally, we established the number of genome based on the genome number. According to the data analysis process of mining and predicting proteome database, the depth analysis of mass human proteome mass spectrometry data is realized. The specific contents include: protein level quality control method is more than spectral level, peptide level quality control is more stringent quality control method. Especially for complex sample data set, integration experiment There are more data and more false positive identification of protein accumulation. We develop a ProDistiller program based on PepDistiller results for protein level quality control and protein assembly, set the map to score F-value, sort the results of the same sample by assembling the protein one by one, and stop the assembly when the protein level FDR reaches 1%. The protein assembly is based on the simple principle.ProDistiller, which is written in the Perl language, and can be run on a variety of platforms. In the result, the properties of the peptide segment identification, such as the charge, the number of missing bits, the mass error of the mother ion and the subions, are retained. The commonly used proteome sequence databases are NCBI NR, UniProt, RefSeq, Ensembl and so on. The database is basically similar in the composition of the theoretical peptide segments, and the difference lies in the proteins stored in different alterable splicing forms. The better Uniprot and SwissProt databases have more identification results than other databases. On the other hand, the Uniprot and Swiss Prot databases are much smaller than the Ensembl database, the RefSeq database and the NCBI NR data. Libraries are less required for hardware and time for computing. So we recommend that in a database search for conventional proteome mass spectrometry identification, data quality is high, Uniprot and Swiss-Prot databases with low redundancy are the best choice. Swiss-Prot is the search database based on the center research. The mass spectrometry data analysis platform (MSPP) is available. It has achieved many search engine search, multi level quality control and integration, multi function modules, such as standard / scale-free, and multi node scheduling and task allocation, which can meet the needs of mass data processing. The platform has been successfully applied to the Chinese human proteome plan, human chromosome proteome plan and human being. In the analysis of the data set of the liver proteome data, more than 400 million spectra have been processed so far. With the rapid development of the protein mass spectrometry technology, the scale of the data is increasing, the large-scale high throughput automation analysis. The high performance computing platform needs to further optimize the task scheduling, the data distribution and the result collection, the establishment of high flux and automation. .MSPP, a new protein identification platform for tandem mass spectrometry, has been successfully applied to data analysis of complex samples in the human chromosome proteome program. Three groups of human hepatoma cell lines with different metastatic potential, Hep3B, HCC97H and HCCLM3, were transcribed, the deep sequencing analysis and proteomics of the translation group and proteome 9064 genes were identified, which were 50.2%. of the total number of genes in the translation group. 31 low abundance proteins were identified by the transcription factor enrichment strategy. It was proved that the enrichment strategy was effective for the identification of low abundance proteins. We found that only 0.4% of the total number of peptide segments were identified by the sample specific database search. This indicates that the single amino acid polymorphism is found to be 0.4%. In order to obtain the most complete data set of the human liver proteome, we systematically collect the complete liver related mass spectrum data, record the state of the sample, and obtain the first version of the most complete liver mass spectrometry data. The experimental data are divided into three types, adult liver, fetal liver and liver cancer cell line according to the sample type. Using MSPP For the liver mass spectrometry data reanalysis, the latest and highly trusted human liver proteome data set was constructed, and 9901 genes were identified. The results were much higher than the amount of data (4408 proteins) of the existing human liver data set in PeptideAtlas. Compared with the specific expression profiles of liver tissue in SwissProt and ProteinAtlas, the results were compared. There are still a lot of leakage of protein. Analysis of the score of its identification spectrum, it is found that many identification atlas are not low score and filtered, but have good scores, resulting in a large number of false negative results. We have established the data analysis flow based on the genome database, and initially realized mass human proteome mass spectrometry. Depth analysis of data. Using high-precision mass spectrometry data to search the genome database (the exon connector database) and the predictive protein AceView database, we found some highly credible candidate results, including 5 new AS peptides and 3 new protein peptides, although the results still need further experimental verification, but This experiment proved the feasibility of annotation genome based on mass spectrometry data and identified the analysis method.
【學(xué)位授予單位】：中國人民解放軍軍事醫(yī)學(xué)科學(xué)院
【學(xué)位級(jí)別】：博士
【學(xué)位授予年份】：2017
【分類號(hào)】：Q51;Q811.4

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