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題名 以資料分析和機器學習用於HiChIP解析T細胞衰竭機制
Using HiChIP investigate T Cell exhaustion by data analysis and machine learning作者 楊明翰
Yang, Ming-Han貢獻者 張家銘
Chang, Jia-Ming
楊明翰
Yang, Ming-Han關鍵詞 染色體構象捕獲
T細胞衰竭
T cell exhaustion
Hi-C
HiChIP
T cell exhaustion
Hi-C
HiChIP日期 2021 上傳時間 2-Sep-2021 16:56:30 (UTC+8) 參考文獻 Socinski, M., Jotte, R., Cappuzzo, F., Orlandi, F., Stroyakovskiy, D., & Nogami, N. et al. (2018). Atezolizumab for First-Line Treatment of Metastatic Nonsquamous NSCLC. New England Journal Of Medicine, 378(24), 2288-2301. doi: 10.1056/nejmoa1716948\n\nAchinger-Kawecka, J., Stirzaker, C., Chia, K., Portman, N., Campbell, E., & Du, Q. et al. (2021). Epigenetic therapy suppresses endocrine-resistant breast tumour growth by re-wiring ER-mediated 3D chromatin interactions. doi: 10.1101/2021.06.21.449340\n\nTsai, H., Wu, Y., Lin, S., Chen, I., Lee, J., & Cheng, K. et al. (2019). Abstract A221: Epigenetic therapy restores polyfunctionality of malignant pleural effusion T-cells in patients with non-small cell lung cancer without downregulation of PD-1. Regulating T-Cells And Their Response To Cancer. doi: 10.1158/2326-6074.cricimteatiaacr18-a221\n\nWu, Y., Tao, B., Zhang, T., Fan, Y., & Mao, R. (2019). Pan-Cancer Analysis Reveals Disrupted Circadian Clock Associates With T Cell Exhaustion. Frontiers In Immunology, 10. doi: 10.3389/fimmu.2019.02451\n\nKim, Y., Marhon, S., Zhang, Y., Steger, D., Won, K., & Lazar, M. (2018). Rev-erbα dynamically modulates chromatin looping to control circadian gene transcription. Science, 359(6381), 1274-1277. doi: 10.1126/science.aao6891\n\nLiang, C., Huang, S., Zhao, Y., Chen, S., & Li, Y. (2021). TOX as a potential target for immunotherapy in lymphocytic malignancies. Biomarker Research, 9(1). doi: 10.1186/s40364-021-00275-y\n\nLieberman-Aiden, E., van Berkum, N., Williams, L., Imakaev, M., Ragoczy, T., & Telling, A. et al. (2009). Comprehensive Mapping of Long-Range Interactions Reveals Folding Principles of the Human Genome. Science, 326(5950), 289-293. doi: 10.1126/science.1181369\n\nRobertson, G., Hirst, M., Bainbridge, M., Bilenky, M., Zhao, Y., & Zeng, T. et al. (2007). Genome-wide profiles of STAT1 DNA association using chromatin immunoprecipitation and massively parallel sequencing. Nature Methods, 4(8), 651-657. doi: 10.1038/nmeth1068\n\n\nMumbach, M., Rubin, A., Flynn, R., Dai, C., Khavari, P., Greenleaf, W., & Chang, H. (2016). HiChIP: efficient and sensitive analysis of protein-directed genome architecture. Nature Methods, 13(11), 919-922. doi: 10.1038/nmeth.3999\n\nJane B. Reece， Noel Meyers， Lisa A. Urry， Michael L. Cain， Steven A. Wasserman， Peter V. Minorsky ,Campbell Biology Australian and New Zealand Edition, 11th Edition, 367 , Figure 18.8\n\nHaller, F., Bieg, M., Will, R., Körner, C., Weichenhan, D., & Bott, A. et al. (2019). Enhancer hijacking activates oncogenic transcription factor NR4A3 in acinic cell carcinomas of the salivary glands. Nature Communications, 10(1). doi: 10.1038/s41467-018-08069-x\n\nNorthcott, P., Lee, C., Zichner, T., Stütz, A., Erkek, S., & Kawauchi, D. et al. (2014). Enhancer hijacking activates GFI1 family oncogenes in medulloblastoma. Nature, 511(7510), 428-434. doi: 10.1038/nature13379\n\nZimmerman, M., Liu, Y., He, S., Durbin, A., Abraham, B., & Easton, J. et al. (2017). MYC Drives a Subset of High-Risk Pediatric Neuroblastomas and Is Activated through Mechanisms Including Enhancer Hijacking and Focal Enhancer Amplification. Cancer Discovery, 8(3), 320-335. doi: 10.1158/2159-8290.cd-17-0993\n\nBhattacharyya, S., Chandra, V., Vijayanand, P., & Ay, F. (2019). Identification of significant chromatin contacts from HiChIP data by FitHiChIP. Nature Communications, 10(1). doi: 10.1038/s41467-019-11950-y\n\nLareau, C., & Aryee, M. (2017). hichipper: A preprocessing pipeline for assessing library quality and DNA loops from HiChIP data. doi: 10.1101/192302\n\nAnders, S., & Huber, W. (2010). Differential expression analysis for sequence count data. Genome Biology, 11(10). doi: 10.1186/gb-2010-11-10-r106\n\nZhi, D. (2019). Gene2vec: distributed representation of genes based on co-expression. BMC Genomics, 20(S1). doi: 10.1186/s12864-018-5370-x\n\n\nZufferey, M., Tavernari, D., Oricchio, E., & Ciriello, G. (2018). Comparison of computational methods for the identification of topologically associating domains. Genome Biology, 19(1). doi: 10.1186/s13059-018-1596-9\n\nLazaris, C., Kelly, S., Ntziachristos, P., Aifantis, I., & Tsirigos, A. (2017). HiC-bench: comprehensive and reproducible Hi-C data analysis designed for parameter exploration and benchmarking. BMC Genomics, 18(1). doi: 10.1186/s12864-016-3387-6\n\nSanders, J., Freeman, T., Xu, Y., Golloshi, R., Stallard, M., & Hill, A. et al. (2020). Radiation-induced DNA damage and repair effects on 3D genome organization. Nature Communications, 11(1). doi: 10.1038/s41467-020-20047-w\n\nDylan Skola (2018).python-genome-browser. Github,from https://github.com/phageghost/python-genome-browser\n\nKnight, P., & Ruiz, D. (2012). A fast algorithm for matrix balancing. IMA Journal Of Numerical Analysis, 33(3), 1029-1047. doi: 10.1093/imanum/drs019\n\nMumbach, M., Satpathy, A., Boyle, E., Dai, C., Gowen, B., & Cho, S. et al. (2017). Enhancer connectome in primary human cells identifies target genes of disease-associated DNA elements. Nature Genetics, 49(11), 1602-1612. doi: 10.1038/ng.3963\n\nQuinlan, A., & Hall, I. (2010). BEDTools: a flexible suite of utilities for comparing genomic features. Bioinformatics, 26(6), 841-842. doi: 10.1093/bioinformatics/btq033\n\nChen, Z., Ji, Z., Ngiow, S., Manne, S., Cai, Z., & Huang, A. et al. (2019). TCF-1-Centered Transcriptional Network Drives an Effector versus Exhausted CD8 T Cell-Fate Decision.\nImmunity, 51(5), 840-855.e5. doi: 10.1016/j.immuni.2019.09.013\n\nIm, S., Hashimoto, M., Gerner, M., Lee, J., Kissick, H., & Burger, M. et al. (2016). Defining CD8+ T cells that provide the proliferative burst after PD-1 therapy. Nature, 537(7620), 417-421. doi: 10.1038/nature19330\n\nSanders, J., Freeman, T., Xu, Y., Golloshi, R., Stallard, M., & Hill, A. et al. (2020). Radiation-induced DNA damage and repair effects on 3D genome organization. Nature Communications, 11(1). doi: 10.1038/s41467-020-20047-w\n\nBlank, C., Haining, W., Held, W., Hogan, P., Kallies, A., & Lugli, E. et al. (2019). Defining ‘T cell exhaustion’. Nature Reviews Immunology, 19(11), 665-674. doi: 10.1038/s41577-019-0221-9 描述 碩士
國立政治大學
資訊科學系
108753203資料來源 http://thesis.lib.nccu.edu.tw/record/#G0108753203 資料類型 thesis dc.contributor.advisor 張家銘 zh_TW dc.contributor.advisor Chang, Jia-Ming en_US dc.contributor.author (Authors) 楊明翰 zh_TW dc.contributor.author (Authors) Yang, Ming-Han en_US dc.creator (作者) 楊明翰 zh_TW dc.creator (作者) Yang, Ming-Han en_US dc.date (日期) 2021 en_US dc.date.accessioned 2-Sep-2021 16:56:30 (UTC+8) - dc.date.available 2-Sep-2021 16:56:30 (UTC+8) - dc.date.issued (上傳時間) 2-Sep-2021 16:56:30 (UTC+8) - dc.identifier (Other Identifiers) G0108753203 en_US dc.identifier.uri (URI) https://ah.lib.nccu.edu.tw/item?item_id=156136 - dc.description (描述) 碩士 zh_TW dc.description (描述) 國立政治大學 zh_TW dc.description (描述) 資訊科學系 zh_TW dc.description (描述) 108753203 zh_TW dc.description.tableofcontents Introduction 12\nImmunotherapy and T cell exhaustion 12\nCandidate Mechanism of T cell exhaustion 13\nNext-Generation Sequencing Technology 15\nHi-C method 15\nChIP-seq method 17\nHiChIP method 17\nDifferent scales of 3D chromosome 19\nEnhancer Hijacking and T cell exhaustion 19\nMethods 21\nData Analysis Workflow Design 21\nMain Procedure (P) Illustration 23\nHi-C Pro (P1) 23\nhichipper (P2) 24\nHiChIP Loop Scanner (P3) and HiChIP Loop Counter (P4) 25\nDESeq2 (P5) 29\nDESeq2 Normalization Re-implementation (P6) 29\nHiChIP Loop Intensity Regulation Imputation Model with Machine Learning 30\nArtificial Neural Network 30\nHiChIP Loop Intensity Neural Network 31\nHiChIP Loop Intensity Gene Set Enrichment (GSEA) Analysis 32\nInsulation Score Analysis and TAD Calling 32\nA / B Compartment Analysis 34\nQuality Control (Q) 35\nLibrary QC (Q1,Q2) 35\nMapping Coverage QC (Q3) 35\nHi-C Contact Map Correlation QC (Internal Consistency QC) (Q4) 35\nhichipper QC (Q5) 35\nData Visualization 35\nConvert bed file to longrange 35\nExperimental Results 36\nSummary of Data 36\nPilot Run Results (lab00) 37\nExperimental Results (lab01 & lab02) 39\nExperimental Data QC - HiC Contact Map Corr (Q-4) 39\nChromatin Loop analysis of T Cell exhaustion 41\nHiChIP Loop Heatmap Visualization Comparison V-4 41\nExperimental Data QC - DESeq2 Scatter QC (Q-6) 43\nExperimental Data QC - DESeq2 PCA QC (Q-7) 44\nDESeq2 Result (V-5) 45\nChromatin Topologically Associating Domain (TAD) analysis of T Cell exhaustion 47\nHiChP Contact Map Enhancement 47\nHiChP Insulation Score analysis 48\nComparison of Chromosome Organization from Small to Middle Scale of T Cell exhaustion 49\nStudy of T cell exhaustion Subtype with HiChIP Loop Intensity Regulation Trend 52\nHiChIP Loop Intensity Neural Network 54\nGene2Vec PCA analysis 54\nNeural Network Training Result 55\nHiChIP Loop Intensity Neural Network prediction in real data 56\nGSEA result 57\nDiscussion and Conclusion 60\nHiChIP Loop Associate With Transcription factors of Genes 60\nRNA Seq & HiChIP Loop Intensity Correlation 66\nHiChIP Full Comparison Plot 68\nConclusion 70\nReferences 71 zh_TW dc.format.extent 11018202 bytes - dc.format.mimetype application/pdf - dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0108753203 en_US dc.subject (關鍵詞) 染色體構象捕獲 zh_TW dc.subject (關鍵詞) T細胞衰竭 zh_TW dc.subject (關鍵詞) T cell exhaustion zh_TW dc.subject (關鍵詞) Hi-C zh_TW dc.subject (關鍵詞) HiChIP zh_TW dc.subject (關鍵詞) T cell exhaustion en_US dc.subject (關鍵詞) Hi-C en_US dc.subject (關鍵詞) HiChIP en_US dc.title (題名) 以資料分析和機器學習用於HiChIP解析T細胞衰竭機制 zh_TW dc.title (題名) Using HiChIP investigate T Cell exhaustion by data analysis and machine learning en_US dc.type (資料類型) thesis en_US dc.relation.reference (參考文獻) Socinski, M., Jotte, R., Cappuzzo, F., Orlandi, F., Stroyakovskiy, D., & Nogami, N. et al. (2018). Atezolizumab for First-Line Treatment of Metastatic Nonsquamous NSCLC. New England Journal Of Medicine, 378(24), 2288-2301. doi: 10.1056/nejmoa1716948\n\nAchinger-Kawecka, J., Stirzaker, C., Chia, K., Portman, N., Campbell, E., & Du, Q. et al. (2021). Epigenetic therapy suppresses endocrine-resistant breast tumour growth by re-wiring ER-mediated 3D chromatin interactions. doi: 10.1101/2021.06.21.449340\n\nTsai, H., Wu, Y., Lin, S., Chen, I., Lee, J., & Cheng, K. et al. (2019). Abstract A221: Epigenetic therapy restores polyfunctionality of malignant pleural effusion T-cells in patients with non-small cell lung cancer without downregulation of PD-1. Regulating T-Cells And Their Response To Cancer. doi: 10.1158/2326-6074.cricimteatiaacr18-a221\n\nWu, Y., Tao, B., Zhang, T., Fan, Y., & Mao, R. (2019). Pan-Cancer Analysis Reveals Disrupted Circadian Clock Associates With T Cell Exhaustion. Frontiers In Immunology, 10. doi: 10.3389/fimmu.2019.02451\n\nKim, Y., Marhon, S., Zhang, Y., Steger, D., Won, K., & Lazar, M. (2018). Rev-erbα dynamically modulates chromatin looping to control circadian gene transcription. Science, 359(6381), 1274-1277. doi: 10.1126/science.aao6891\n\nLiang, C., Huang, S., Zhao, Y., Chen, S., & Li, Y. (2021). TOX as a potential target for immunotherapy in lymphocytic malignancies. Biomarker Research, 9(1). doi: 10.1186/s40364-021-00275-y\n\nLieberman-Aiden, E., van Berkum, N., Williams, L., Imakaev, M., Ragoczy, T., & Telling, A. et al. (2009). Comprehensive Mapping of Long-Range Interactions Reveals Folding Principles of the Human Genome. Science, 326(5950), 289-293. doi: 10.1126/science.1181369\n\nRobertson, G., Hirst, M., Bainbridge, M., Bilenky, M., Zhao, Y., & Zeng, T. et al. (2007). Genome-wide profiles of STAT1 DNA association using chromatin immunoprecipitation and massively parallel sequencing. Nature Methods, 4(8), 651-657. doi: 10.1038/nmeth1068\n\n\nMumbach, M., Rubin, A., Flynn, R., Dai, C., Khavari, P., Greenleaf, W., & Chang, H. (2016). HiChIP: efficient and sensitive analysis of protein-directed genome architecture. Nature Methods, 13(11), 919-922. doi: 10.1038/nmeth.3999\n\nJane B. Reece， Noel Meyers， Lisa A. Urry， Michael L. Cain， Steven A. Wasserman， Peter V. Minorsky ,Campbell Biology Australian and New Zealand Edition, 11th Edition, 367 , Figure 18.8\n\nHaller, F., Bieg, M., Will, R., Körner, C., Weichenhan, D., & Bott, A. et al. (2019). Enhancer hijacking activates oncogenic transcription factor NR4A3 in acinic cell carcinomas of the salivary glands. Nature Communications, 10(1). doi: 10.1038/s41467-018-08069-x\n\nNorthcott, P., Lee, C., Zichner, T., Stütz, A., Erkek, S., & Kawauchi, D. et al. (2014). Enhancer hijacking activates GFI1 family oncogenes in medulloblastoma. Nature, 511(7510), 428-434. doi: 10.1038/nature13379\n\nZimmerman, M., Liu, Y., He, S., Durbin, A., Abraham, B., & Easton, J. et al. (2017). MYC Drives a Subset of High-Risk Pediatric Neuroblastomas and Is Activated through Mechanisms Including Enhancer Hijacking and Focal Enhancer Amplification. Cancer Discovery, 8(3), 320-335. doi: 10.1158/2159-8290.cd-17-0993\n\nBhattacharyya, S., Chandra, V., Vijayanand, P., & Ay, F. (2019). Identification of significant chromatin contacts from HiChIP data by FitHiChIP. Nature Communications, 10(1). doi: 10.1038/s41467-019-11950-y\n\nLareau, C., & Aryee, M. (2017). hichipper: A preprocessing pipeline for assessing library quality and DNA loops from HiChIP data. doi: 10.1101/192302\n\nAnders, S., & Huber, W. (2010). Differential expression analysis for sequence count data. Genome Biology, 11(10). doi: 10.1186/gb-2010-11-10-r106\n\nZhi, D. (2019). Gene2vec: distributed representation of genes based on co-expression. BMC Genomics, 20(S1). doi: 10.1186/s12864-018-5370-x\n\n\nZufferey, M., Tavernari, D., Oricchio, E., & Ciriello, G. (2018). Comparison of computational methods for the identification of topologically associating domains. Genome Biology, 19(1). doi: 10.1186/s13059-018-1596-9\n\nLazaris, C., Kelly, S., Ntziachristos, P., Aifantis, I., & Tsirigos, A. (2017). HiC-bench: comprehensive and reproducible Hi-C data analysis designed for parameter exploration and benchmarking. BMC Genomics, 18(1). doi: 10.1186/s12864-016-3387-6\n\nSanders, J., Freeman, T., Xu, Y., Golloshi, R., Stallard, M., & Hill, A. et al. (2020). Radiation-induced DNA damage and repair effects on 3D genome organization. Nature Communications, 11(1). doi: 10.1038/s41467-020-20047-w\n\nDylan Skola (2018).python-genome-browser. Github,from https://github.com/phageghost/python-genome-browser\n\nKnight, P., & Ruiz, D. (2012). A fast algorithm for matrix balancing. IMA Journal Of Numerical Analysis, 33(3), 1029-1047. doi: 10.1093/imanum/drs019\n\nMumbach, M., Satpathy, A., Boyle, E., Dai, C., Gowen, B., & Cho, S. et al. (2017). Enhancer connectome in primary human cells identifies target genes of disease-associated DNA elements. Nature Genetics, 49(11), 1602-1612. doi: 10.1038/ng.3963\n\nQuinlan, A., & Hall, I. (2010). BEDTools: a flexible suite of utilities for comparing genomic features. Bioinformatics, 26(6), 841-842. doi: 10.1093/bioinformatics/btq033\n\nChen, Z., Ji, Z., Ngiow, S., Manne, S., Cai, Z., & Huang, A. et al. (2019). TCF-1-Centered Transcriptional Network Drives an Effector versus Exhausted CD8 T Cell-Fate Decision.\nImmunity, 51(5), 840-855.e5. doi: 10.1016/j.immuni.2019.09.013\n\nIm, S., Hashimoto, M., Gerner, M., Lee, J., Kissick, H., & Burger, M. et al. (2016). Defining CD8+ T cells that provide the proliferative burst after PD-1 therapy. Nature, 537(7620), 417-421. doi: 10.1038/nature19330\n\nSanders, J., Freeman, T., Xu, Y., Golloshi, R., Stallard, M., & Hill, A. et al. (2020). Radiation-induced DNA damage and repair effects on 3D genome organization. Nature Communications, 11(1). doi: 10.1038/s41467-020-20047-w\n\nBlank, C., Haining, W., Held, W., Hogan, P., Kallies, A., & Lugli, E. et al. (2019). Defining ‘T cell exhaustion’. Nature Reviews Immunology, 19(11), 665-674. doi: 10.1038/s41577-019-0221-9 zh_TW dc.identifier.doi (DOI) 10.6814/NCCU202101394 en_US
