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題名 蛋白質質譜模擬之研究
A Simulation Study of Proteomic Mass Spectra
作者 林芳華
貢獻者 薛慧敏
林芳華
關鍵詞 質譜儀
virtual mass spectrometer
日期 2005
上傳時間 2009-09-14
摘要 進入後基因時代,蛋白質體學成為很多科學家有興趣的主題。蛋白質鑑定成為重要的一環,而質譜儀在縮氨酸分析及蛋白質鑑定中扮演重要的角色。腫瘤、卵巢癌及攝護腺癌等研究亦已成為質譜儀上的應用。Coombes 等人 (2004) 提出了一個線性的數學質譜儀模型,而且建議這個模型可被應用在建立質譜儀的模擬中。本文中,我們利用虛擬的質譜儀產生虛擬質譜資料並加以研究。虛擬的質譜實驗包括了前、後兩部份。樣本資料要放入虛擬質譜儀之前,可能出現的蛋白質其強度(intensity)必須先隨機地被決定,之後強度必須被轉換(calibration)變成離子化的個數(abundance) ;之後將樣本資料丟入虛擬質譜儀中,每一個蛋白值的飛行時間(time of flight, TOF) 將會被紀錄。另外一個轉換(calibration)是將飛行時間(TOF)轉成質量電荷比(mass-to-charge ratio,m/z)。質譜儀和兩個轉換都會在資料中產生誤差。在本文中,一個完整的模擬過程將會一步一步被介紹。同時,兩個轉換的方法所產生的誤差也會被探討。之後,我們將此模擬方法應用於模擬一組攝護腺癌中。
Entering the post genomic era, proteomic has become the topic that scientists are interested in. The authentication of protein has been an important item of the topics. Mass spectrometry (MS) has become an important tool for peptide analysis or proteomic authentication. There are many applications of MS such as oncology, ovarian cancer, and prostate cancer. Coombes et al.(2004) proposed a mathematical model of a virtual spectrometer and suggested that the virtual spectrometer can be applied in conducting a MS simulation. In our study, we focus on designing a simulation study of spectrum data from a virtual MS experiment. The virtual experiment includes two stages: pre- and post-virtual spectrometer. Before the sample data are put into the virtual spectrometer, a virtual population of the intensity of all possible proteins should be determined; a virtual sample is randomly drawn; and the generated sample of intensity should be calibrated to abundance, which is the number of molecules ionized and desorbed from the biological sample. The sample data are then put into the virtual spectrometer and the time of flight (TOF) of each ionized molecule is recorded. Another calibration is employed to transfer a TOF to a mass-to-charge ratio (m/z). The spectrometer and the calibration processes produce variation in MS data. In this study, a complete simulation design of mass spectra will be introduced step by step. Moreover, the calibration effects caused from the two calibration procedures will be investigated. A simulation based on a real data set from a prostate cancer study will be also given as an illustration.
參考文獻 1. Adam, B. L., Qu, Y., Davis, J. W., Ward, M. D., Clements, M. A., Cazares, L. H., Semmes, O. J., Schellhammer, P. F., Yasui, Y., Feng, Z. and Wright, G. L. Jr.(2002) ”Serum protein fingerprinting coupled with a pattern-matching algorithm distinguishes prostate cancer from benign prostate hyperplasia and healthy men.” Cancer Research 62, 3609-3614.
2. Beavis, R. C. and Chait, B. T. (1991) “Velocity distributions of intact high mass polypeptide molecule ions produced by matrix assisted laser desorption.” Chem Phys Lett. 181, 479-484.
3. Bickel, P. J. and Doksum, K. A. (1977) “Mathematical statistics : basic ideas and selected topics.”Holden-Day.
4. Coombes, K. R., Koomen, J. M., Baggerly, K. A., Morris, J. S. and Kobayashi, R. (2004) “Understanding the characteristics of mass spectrometry data through the use of simulation.” Cancer Informatics 1, 41-52.
5. Jeffries, N. (2005) “Algorithms for alignment of mass spectrometry proteomic data.” Bioinfomatics 21, 3066-3073.
6. Morris, J. S., Coombes, K. R., Koomen, J. M., Baggerly, K. A.and Kobayashi, R. (2005) ”Feature extraction and quantification for mass spectrometry in biomedical applications using the mean spectrum” Bioinformatics 21, 1764-1775.
7. Petricoin, E. F., Ardekani, A. M., Hitt, B. A., Levine, P. J., Fusaro, V. A., Steinberg, S. M. and Mills,G. B. (2002) “Use of proteomic patterns in serum to identify ovarian cancer.” Lancet 359 , 572–7.
8. Shiwa, M., Nishimura, Y., Wakatabe, R., Fukawa, A., Arikuni, H., Ota, H., Kato, Y. and Yamori, T. (2003) “Rapid discovery and identification of a tissue-specific tumor biomarker from 39 human cancer cell lines using the SELDI ProteinChip platform.” Biochem Biophys Res Commun 309(1), 18-25.
9. Vorderwülbecke, S., Cleverley, S., Weinberger, S. R. and Wiesner, A. (2005) “Protein quantification by the SELDI-TOF-MS–based ProteinChip® System.” Nature Methods 2, 393-395
描述 碩士
國立政治大學
統計研究所
93354020
94
資料來源 http://thesis.lib.nccu.edu.tw/record/#G0093354020
資料類型 thesis
dc.contributor.advisor 薛慧敏zh_TW
dc.contributor.author (Authors) 林芳華zh_TW
dc.creator (作者) 林芳華zh_TW
dc.date (日期) 2005en_US
dc.date.accessioned 2009-09-14-
dc.date.available 2009-09-14-
dc.date.issued (上傳時間) 2009-09-14-
dc.identifier (Other Identifiers) G0093354020en_US
dc.identifier.uri (URI) https://nccur.lib.nccu.edu.tw/handle/140.119/30905-
dc.description (描述) 碩士zh_TW
dc.description (描述) 國立政治大學zh_TW
dc.description (描述) 統計研究所zh_TW
dc.description (描述) 93354020zh_TW
dc.description (描述) 94zh_TW
dc.description.abstract (摘要) 進入後基因時代,蛋白質體學成為很多科學家有興趣的主題。蛋白質鑑定成為重要的一環,而質譜儀在縮氨酸分析及蛋白質鑑定中扮演重要的角色。腫瘤、卵巢癌及攝護腺癌等研究亦已成為質譜儀上的應用。Coombes 等人 (2004) 提出了一個線性的數學質譜儀模型,而且建議這個模型可被應用在建立質譜儀的模擬中。本文中,我們利用虛擬的質譜儀產生虛擬質譜資料並加以研究。虛擬的質譜實驗包括了前、後兩部份。樣本資料要放入虛擬質譜儀之前,可能出現的蛋白質其強度(intensity)必須先隨機地被決定,之後強度必須被轉換(calibration)變成離子化的個數(abundance) ;之後將樣本資料丟入虛擬質譜儀中,每一個蛋白值的飛行時間(time of flight, TOF) 將會被紀錄。另外一個轉換(calibration)是將飛行時間(TOF)轉成質量電荷比(mass-to-charge ratio,m/z)。質譜儀和兩個轉換都會在資料中產生誤差。在本文中,一個完整的模擬過程將會一步一步被介紹。同時,兩個轉換的方法所產生的誤差也會被探討。之後,我們將此模擬方法應用於模擬一組攝護腺癌中。zh_TW
dc.description.abstract (摘要) Entering the post genomic era, proteomic has become the topic that scientists are interested in. The authentication of protein has been an important item of the topics. Mass spectrometry (MS) has become an important tool for peptide analysis or proteomic authentication. There are many applications of MS such as oncology, ovarian cancer, and prostate cancer. Coombes et al.(2004) proposed a mathematical model of a virtual spectrometer and suggested that the virtual spectrometer can be applied in conducting a MS simulation. In our study, we focus on designing a simulation study of spectrum data from a virtual MS experiment. The virtual experiment includes two stages: pre- and post-virtual spectrometer. Before the sample data are put into the virtual spectrometer, a virtual population of the intensity of all possible proteins should be determined; a virtual sample is randomly drawn; and the generated sample of intensity should be calibrated to abundance, which is the number of molecules ionized and desorbed from the biological sample. The sample data are then put into the virtual spectrometer and the time of flight (TOF) of each ionized molecule is recorded. Another calibration is employed to transfer a TOF to a mass-to-charge ratio (m/z). The spectrometer and the calibration processes produce variation in MS data. In this study, a complete simulation design of mass spectra will be introduced step by step. Moreover, the calibration effects caused from the two calibration procedures will be investigated. A simulation based on a real data set from a prostate cancer study will be also given as an illustration.en_US
dc.description.tableofcontents Content Ⅰ
     Figure Content Ⅱ
     Table Content Ⅲ
     Abstract(Chinese) Ⅴ
     Abstract(English) Ⅵ
     
     1. Introduction 1
     2. Pre-Virtual Spectrometer 5
     2.1 Virtual Population 5
     2.2 Virtual Sample 6
     2.3 Calibration from Intensity to Abundance 6
     2.4 An Example 7
     2.5 Calibration from Intensity to Abundance 10
     3. Post-Virtual Spectrometer 14
     3.1 Virtual Mass Spectrometer 14
     3.2 Calibration from TOF to m/z 15
     3.3 Error in Mass 18
     4. Application: A Simulation Study of A Real Example 23
     5. Conclusion 44
     Reference 46
     Appendix 48
zh_TW
dc.language.iso en_US-
dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0093354020en_US
dc.subject (關鍵詞) 質譜儀zh_TW
dc.subject (關鍵詞) virtual mass spectrometeren_US
dc.title (題名) 蛋白質質譜模擬之研究zh_TW
dc.title (題名) A Simulation Study of Proteomic Mass Spectraen_US
dc.type (資料類型) thesisen
dc.relation.reference (參考文獻) 1. Adam, B. L., Qu, Y., Davis, J. W., Ward, M. D., Clements, M. A., Cazares, L. H., Semmes, O. J., Schellhammer, P. F., Yasui, Y., Feng, Z. and Wright, G. L. Jr.(2002) ”Serum protein fingerprinting coupled with a pattern-matching algorithm distinguishes prostate cancer from benign prostate hyperplasia and healthy men.” Cancer Research 62, 3609-3614.zh_TW
dc.relation.reference (參考文獻) 2. Beavis, R. C. and Chait, B. T. (1991) “Velocity distributions of intact high mass polypeptide molecule ions produced by matrix assisted laser desorption.” Chem Phys Lett. 181, 479-484.zh_TW
dc.relation.reference (參考文獻) 3. Bickel, P. J. and Doksum, K. A. (1977) “Mathematical statistics : basic ideas and selected topics.”Holden-Day.zh_TW
dc.relation.reference (參考文獻) 4. Coombes, K. R., Koomen, J. M., Baggerly, K. A., Morris, J. S. and Kobayashi, R. (2004) “Understanding the characteristics of mass spectrometry data through the use of simulation.” Cancer Informatics 1, 41-52.zh_TW
dc.relation.reference (參考文獻) 5. Jeffries, N. (2005) “Algorithms for alignment of mass spectrometry proteomic data.” Bioinfomatics 21, 3066-3073.zh_TW
dc.relation.reference (參考文獻) 6. Morris, J. S., Coombes, K. R., Koomen, J. M., Baggerly, K. A.and Kobayashi, R. (2005) ”Feature extraction and quantification for mass spectrometry in biomedical applications using the mean spectrum” Bioinformatics 21, 1764-1775.zh_TW
dc.relation.reference (參考文獻) 7. Petricoin, E. F., Ardekani, A. M., Hitt, B. A., Levine, P. J., Fusaro, V. A., Steinberg, S. M. and Mills,G. B. (2002) “Use of proteomic patterns in serum to identify ovarian cancer.” Lancet 359 , 572–7.zh_TW
dc.relation.reference (參考文獻) 8. Shiwa, M., Nishimura, Y., Wakatabe, R., Fukawa, A., Arikuni, H., Ota, H., Kato, Y. and Yamori, T. (2003) “Rapid discovery and identification of a tissue-specific tumor biomarker from 39 human cancer cell lines using the SELDI ProteinChip platform.” Biochem Biophys Res Commun 309(1), 18-25.zh_TW
dc.relation.reference (參考文獻) 9. Vorderwülbecke, S., Cleverley, S., Weinberger, S. R. and Wiesner, A. (2005) “Protein quantification by the SELDI-TOF-MS–based ProteinChip® System.” Nature Methods 2, 393-395zh_TW