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    政大機構典藏 > 商學院 > 統計學系 > 學位論文 >  Item 140.119/30905
    Please use this identifier to cite or link to this item: http://nccur.lib.nccu.edu.tw/handle/140.119/30905


    Title: 蛋白質質譜模擬之研究
    A Simulation Study of Proteomic Mass Spectra
    Authors: 林芳華
    Contributors: 薛慧敏
    林芳華
    Keywords: 質譜儀
    virtual mass spectrometer
    Date: 2005
    Issue Date: 2009-09-14
    Abstract: 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.
    Reference: 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
    Description: 碩士
    國立政治大學
    統計研究所
    93354020
    94
    Source URI: http://thesis.lib.nccu.edu.tw/record/#G0093354020
    Data Type: thesis
    Appears in Collections:[統計學系] 學位論文

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