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    政大機構典藏 > 理學院 > 資訊科學系 > 學位論文 >  Item 140.119/120261
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    題名: 深度學習‭, ‬卷積神經網路模型‭, ‬預測蛋白質序列質譜儀圖譜
    Predict MS2 spectrum based on protein sequence by Deep Convolutional Neural Networks
    作者: 林洋名
    Lin, Yang-Ming
    貢獻者: 張家銘
    Chang, Jia-Ming
    林洋名
    Lin, Yang-Ming
    關鍵詞: 胜肽
    卷積類神經網路
    質譜儀圖譜
    深度學習
    機器學習
    質譜儀
    Peptide
    Depp convolutional neural network
    Deep learning
    Machine learning
    Mass spectrum
    Tandem mass spectrometry
    日期: 2018
    上傳時間: 2018-10-01 12:11:12 (UTC+8)
    摘要: 生物學家利用質譜儀,對於未知蛋白質樣品進行定量定性。樣品進入質譜儀器內部,經過一連串的激發游離、利用磁場分離不同胜肽或氨基酸、撞擊偵測器的過程,最後會得到一張質譜儀圖譜。質譜儀圖譜包含的訊息為荷質比的訊號強度,每一個氨基酸都會有專屬於自己的荷質比數值,透過各種不同強度的訊號多寡,可以確認各種氨基酸是否存在。因此,如果能夠預測蛋白質質譜儀圖譜上的訊號強度,那會使質譜儀在定性和定量更加有準確度。在這篇論文中,我們提出‭ ‬MS2CNN‭ ‬模型是以深度學習演算法為基礎,透過卷積網路架構學習質譜儀圖譜。我們訓練時採用的質譜圖譜,是來自美國國家標準暨技術研究院公開的資料集,而驗證時會使用另外一組由液相層析串聯式質譜儀所實驗而成,人類的質譜儀圖譜資料集,此份資料會額外獨立出來,不會參與訓練的過程。我們的模型在這組資料集的測試成果分別為:電荷數為2時,餘弦相似度座落在‭ ‬0.57‭ ‬到‭ ‬0.79‭ ‬以及電荷數為3時,餘弦相似度座落在‭ ‬0.59‭ ‬到‭ ‬0.74。交叉驗證的訓練過程,在訓練組、驗證組、測試組分別得到的餘弦相似度和皮爾森相關係數為‭ ‬0.93‭, ‬0.86‭, ‬0.83‭ ‬和‭ ‬0.91‭, ‬0.83‭, ‬0.79。而我們在獨立資料集獲得的餘弦相似度和皮爾森相關係數‭ ‬‭(‬0.69‭ ‬和‭ ‬0.64‭) ‬比起‭ ‬MS2PIP‭ ‬所得到的餘弦相似度和皮爾森相關係數‭ ‬‭(‬0.66‭ ‬和‭ ‬0.61‭) ‬還要好。最後結果顯示,我們的預測結果可以比現行工具‭ ‬MS2PIP‭ ‬預測來的精準,尤其是在胜肽長度小於19的時候。從結果讓我們發現到,只要結合夠多的資料用在深度學習的模型上,我們相信能夠改善在長度較長的胜肽序列的表現結果。

    Mass spectrometry allows biologists to identify and quantify protein samples in the form of digested peptide sequences. Tandem mass spectrometry (MS2) provides a tool to match signal observations with the chemical process. A peak in MS2 spectrum indicates the presence of a peptide fragmented ion with a specific mass and charge. Thus, it is useful to develop the predictor of MS2 signal peak intensity. In this thesis, we proposed a regression model, MS2CNN, based on a deep learning algorithm - deep convolutional neural network. MS2CNN is trained on the National Institute of Standards and Technology MS2 spectrum dataset and evaluated on a publicly available independent test dataset of human HeLa cell lysate from LC-MS experiment. For this dataset, MS2CNN achieved a cosine similarity (COS) in the range of 0.57 and 0.79 for peptides of 2+ and a COS in the range of 0.59 and 0.74 for peptides of 3+. In five-fold cross-validation, the COS and PCC of training, validation and testing is 0.93, 0.86, 0.83 and 0.91, 0.83, 0.79, respectively. In independent set test, our model shows better COS and PCC (0.69 and 0.64) than the ones of MS2PIP (0.66 and 0.61). We showed that MS2CNN performs better than MS2PIP, specially in short peptide (i.e., sequence length less than 19). The results suggest incorporating more data for deep learning model for longer peptides can potentially improve the performance.
    參考文獻: 1. Arnold,R.J. et al. (2006) A machine learning approach to predicting peptide fragmentation spectra. Pac. Symp. Biocomput. Pac. Symp. Biocomput., 219–230.
    2. Chollet, F. (2015) keras, GitHub. - References - Scientific Research Publishing.
    3. Cock,P.J.A. et al. (2009) Biopython: freely available Python tools for computational molecular biology and bioinformatics. Bioinforma. Oxf. Engl., 25, 1422–1423.
    4. Degroeve,S. et al. (2015) MS2PIP prediction server: compute and visualize MS2 peak intensity predictions for CID and HCD fragmentation. Nucleic Acids Res., 43, W326–W330.
    5. Degroeve,S. and Martens,L. (2013) MS2PIP: a tool for MS/MS peak intensity prediction. Bioinforma. Oxf. Engl., 29, 3199–3203.
    6. Eidhammer,I. ed. (2007) Computational methods for mass spectrometry proteomics John Wiley & Sons, Chichester, England ; Hoboken, NJ.
    7. Elias,J.E. et al. (2004) Intensity-based protein identification by machine learning from a library of tandem mass spectra. Nat. Biotechnol., 22, 214–219.
    8. Gatto,L. and Christoforou,A. (2014) Using R and Bioconductor for proteomics data analysis. Biochim. Biophys. Acta, 1844, 42–51.
    9. Goloborodko,A.A. et al. (2013) Pyteomics--a Python framework for exploratory data analysis and rapid software prototyping in proteomics. J. Am. Soc. Mass Spectrom., 24, 301–304.
    10. Hultin-Rosenberg,L. et al. (2013) Defining, comparing, and improving iTRAQ quantification in mass spectrometry proteomics data. Mol. Cell. Proteomics MCP, 12, 2021–2031.
    11. Kirik,U. et al. (2018) Improving peptide-spectrum matching by fragmentation prediction using Hidden Markov Models. bioRxiv.
    12. Lawrence,R.T. et al. (2016) Plug-and-play analysis of the human phosphoproteome by targeted high-resolution mass spectrometry. Nat. Methods, 13, 431–434.
    13. LeCun,Y. et al. (2015) Deep learning. Nature, 521, 436.
    14. Lecun,Y. et al. (1998) Gradient-based learning applied to document recognition. Proc. IEEE, 86, 2278–2324.
    15. Li,S. et al. (2011) On the Accuracy and Limits of Peptide Fragmentation Spectrum Prediction. Anal. Chem., 83, 790–796.
    16. Pedregosa,F. et al. (2011) Scikit-learn: Machine Learning in Python. J. Mach. Learn. Res., 12, 2825–2830.
    17. Savojardo,C. et al. (2018) DeepSig: deep learning improves signal peptide detection in proteins. Bioinforma. Oxf. Engl., 34, 1690–1696.
    18. Tsou,C.-C. et al. (2016) Untargeted, spectral library-free analysis of data-independent acquisition proteomics data generated using Orbitrap mass spectrometers. Proteomics, 16, 2257–2271.
    19. Walt,S. van der et al. (2011) The NumPy Array: A Structure for Efficient Numerical Computation. Comput. Sci. Eng., 13, 22–30.
    20. Zhang,Z. (2004) Prediction of low-energy collision-induced dissociation spectra of peptides. Anal. Chem., 76, 3908–3922.
    Zhang,Z. (2005) Prediction of Low-Energy Collision-Induced Dissociation Spectra of Peptides with Three or More Charges. Anal. Chem., 77, 6364–6373.
    描述: 碩士
    國立政治大學
    資訊科學系
    105753032
    資料來源: http://thesis.lib.nccu.edu.tw/record/#G0105753032
    資料類型: thesis
    DOI: 10.6814/THE.NCCU.CS.013.2018.B02
    顯示於類別:[資訊科學系] 學位論文

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