請用此 Handle URI 來引用此文件：
http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/86621完整後設資料紀錄
| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 莊曜宇(Eric Y. Chuang) | |
| dc.contributor.author | Yuan-Mao Hung | en |
| dc.contributor.author | 洪源懋 | zh_TW |
| dc.date.accessioned | 2023-03-20T00:07:03Z | - |
| dc.date.copyright | 2022-08-12 | |
| dc.date.issued | 2022 | |
| dc.date.submitted | 2022-08-08 | |
| dc.identifier.citation | 1. Barba, M., H. Czosnek, and A. Hadidi, Historical perspective, development and applications of next-generation sequencing in plant virology. Viruses, 2014. 6(1): p. 106-136. 2. Sawicki, M.P., et al., Human genome project. American journal of surgery, 1993. 165(2): p. 258-264. 3. Turnbaugh, P.J., et al., The human microbiome project. Nature, 2007. 449(7164): p. 804-810. 4. Shahi, S.K., S.N. Freedman, and A.K. Mangalam, Gut microbiome in multiple sclerosis: the players involved and the roles they play. Gut microbes, 2017. 8(6): p. 607-615. 5. Yang, B., Y. Wang, and P.-Y. Qian, Sensitivity and correlation of hypervariable regions in 16S rRNA genes in phylogenetic analysis. BMC bioinformatics, 2016. 17(1): p. 1-8. 6. Johnson, J.S., et al., Evaluation of 16S rRNA gene sequencing for species and strain-level microbiome analysis. Nature communications, 2019. 10(1): p. 1-11. 7. Caporaso, J.G., et al., Ultra-high-throughput microbial community analysis on the Illumina HiSeq and MiSeq platforms. The ISME journal, 2012. 6(8): p. 1621-1624. 8. Merriman, B., I.T. D Team, and J.M. Rothberg, Progress in ion torrent semiconductor chip based sequencing. Electrophoresis, 2012. 33(23): p. 3397-3417. 9. Rothberg, J.M. and J.H. Leamon, The development and impact of 454 sequencing. Nature biotechnology, 2008. 26(10): p. 1117-1124. 10. Osman, M.-A., et al., 16S rRNA gene sequencing for deciphering the colorectal cancer gut microbiome: current protocols and workflows. Frontiers in microbiology, 2018. 9: p. 767. 11. de Melo, F., et al., A systematic review of the microbiota composition in various peri-implant conditions: data from 16S rRNA gene sequencing. Archives of oral biology, 2020. 117: p. 104776. 12. Edgar, R.C., Updating the 97% identity threshold for 16S ribosomal RNA OTUs. Bioinformatics, 2018. 34(14): p. 2371-2375. 13. Callahan, B.J., et al., DADA2: High-resolution sample inference from Illumina amplicon data. Nature methods, 2016. 13(7): p. 581-583. 14. Rosen, M.J., et al., Denoising PCR-amplified metagenome data. BMC bioinformatics, 2012. 13(1): p. 1-16. 15. Amir, A., et al., Deblur rapidly resolves single-nucleotide community sequence patterns. MSystems, 2017. 2(2): p. e00191-16. 16. Quiroga, M.V., et al., The ecological assembly of bacterial communities in Antarctic wetlands varies across levels of phylogenetic resolution. Environmental microbiology, 2022. 17. Callahan, B.J., P.J. McMurdie, and S.P. Holmes, Exact sequence variants should replace operational taxonomic units in marker-gene data analysis. The ISME journal, 2017. 11(12): p. 2639-2643. 18. Cole, J.R., et al., Ribosomal Database Project: data and tools for high throughput rRNA analysis. Nucleic acids research, 2014. 42(D1): p. D633-D642. 19. Yilmaz, P., et al., The SILVA and “all-species living tree project (LTP)” taxonomic frameworks. Nucleic acids research, 2014. 42(D1): p. D643-D648. 20. DeSantis, T.Z., et al., Greengenes, a chimera-checked 16S rRNA gene database and workbench compatible with ARB. Applied and environmental microbiology, 2006. 72(7): p. 5069-5072. 21. Willis, A.D., Rarefaction, alpha diversity, and statistics. Frontiers in microbiology, 2019. 10: p. 2407. 22. Tuomisto, H., A diversity of beta diversities: straightening up a concept gone awry. Part 1. Defining beta diversity as a function of alpha and gamma diversity. Ecography, 2010. 33(1): p. 2-22. 23. Segata, N., et al., Metagenomic biomarker discovery and explanation. Genome biology, 2011. 12(6): p. 1-18. 24. Mandal, S., et al., Analysis of composition of microbiomes: a novel method for studying microbial composition. Microbial ecology in health and disease, 2015. 26(1): p. 27663. 25. Langille, M.G., et al., Predictive functional profiling of microbial communities using 16S rRNA marker gene sequences. Nature biotechnology, 2013. 31(9): p. 814. 26. Douglas, G.M., et al., PICRUSt2 for prediction of metagenome functions. Nature Biotechnology, 2020. 38(6): p. 685-688. 27. Wemheuer, F., et al., Tax4Fun2: prediction of habitat-specific functional profiles and functional redundancy based on 16S rRNA gene sequences. Environmental microbiome, 2020. 15(1): p. 1-12. 28. Caporaso, J.G., et al., QIIME allows analysis of high-throughput community sequencing data. Nature methods, 2010. 7(5): p. 335. 29. Bolyen, E., et al., Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2. Nature biotechnology, 2019. 37(8): p. 852-857. 30. Schloss, P.D., et al., Introducing mothur: open-source, platform-independent, community-supported software for describing and comparing microbial communities. Applied and environmental microbiology, 2009. 75(23): p. 7537-7541. 31. Soni, R., Introduction to nginx web server, in Nginx. 2016, Springer. p. 1-15. 32. Rhoads, A. and K.F. Au, PacBio sequencing and its applications. Genomics, proteomics & bioinformatics, 2015. 13(5): p. 278-289. 33. Lin, B., J. Hui, and H. Mao, Nanopore technology and its applications in gene sequencing. Biosensors, 2021. 11(7): p. 214. 34. Agnihotry, S., A.N. Sarangi, and R. Aggarwal, Construction & assessment of a unified curated reference database for improving the taxonomic classification of bacteria using 16S rRNA sequence data. Indian journal of medical research, 2020. 151(1): p. 93. 35. Abellan-Schneyder, I., et al., Primer, pipelines, parameters: issues in 16S rRNA gene sequencing. Msphere, 2021. 6(1): p. e01202-20. 36. Wenger, A.M., et al., Accurate circular consensus long-read sequencing improves variant detection and assembly of a human genome. Nature biotechnology, 2019. 37(10): p. 1155-1162. 37. Wang, J., et al., Instrumentational implementation for parallelized nanopore electrochemical measurements. Analyst, 2021. 146(13): p. 4111-4120. 38. Weirather, J.L., et al., Comprehensive comparison of Pacific Biosciences and Oxford Nanopore Technologies and their applications to transcriptome analysis. F1000Research, 2017. 6. 39. Wagner, J., et al., Evaluation of PacBio sequencing for full-length bacterial 16S rRNA gene classification. BMC microbiology, 2016. 16(1): p. 1-17. 40. Singer, E., et al., High-resolution phylogenetic microbial community profiling. The ISME journal, 2016. 10(8): p. 2020-2032. 41. Numberger, D., et al., Characterization of bacterial communities in wastewater with enhanced taxonomic resolution by full-length 16S rRNA sequencing. Scientific reports, 2019. 9(1): p. 1-14. 42. Zhang, J., et al., Improved high-throughput sequencing of the human oral microbiome: from Illumina to PacBio. Canadian journal of infectious diseases and medical microbiology, 2020. 2020. 43. Schloss, P.D., et al., Sequencing 16S rRNA gene fragments using the PacBio SMRT DNA sequencing system. PeerJ, 2016. 4: p. e1869. 44. Moustafa, A., et al., Microbial metagenome of urinary tract infection. Scientific reports, 2018. 8(1): p. 4333. 45. Chaparro, J.M., et al., Manipulating the soil microbiome to increase soil health and plant fertility. Biology and fertility of soils, 2012. 48(5): p. 489-499. 46. Jha, A.R., et al., Gut microbiome transition across a lifestyle gradient in Himalaya. PLoS biology, 2018. 16(11): p. e2005396. 47. Afgan, E., et al., The Galaxy platform for accessible, reproducible and collaborative biomedical analyses: 2018 update. Nucleic acids research, 2018. 46(W1): p. W537-W544. 48. Rognes, T., et al., VSEARCH: a versatile open source tool for metagenomics. PeerJ, 2016. 4: p. e2584. 49. Kanehisa, M. and S. Goto, KEGG: kyoto encyclopedia of genes and genomes. Nucleic acids research, 2000. 28(1): p. 27-30. 50. Cox, M.J., et al., Longitudinal assessment of sputum microbiome by sequencing of the 16S rRNA gene in non-cystic fibrosis bronchiectasis patients. PLoS one, 2017. 12(2): p. e0170622. 51. Kasai, C., et al., Comparison of the gut microbiota composition between obese and non-obese individuals in a Japanese population, as analyzed by terminal restriction fragment length polymorphism and next-generation sequencing. BMC gastroenterology, 2015. 15(1): p. 100. 52. Rosen, M.J., et al., Denoising PCR-amplified metagenome data. BMC bioinformatics, 2012. 13(1): p. 283. 53. Quast, C., et al., The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucleic acids research, 2012. 41(D1): p. D590-D596. 54. Huse, S.M., et al., VAMPS: a website for visualization and analysis of microbial population structures. BMC bioinformatics, 2014. 15(1): p. 41. 55. DeSantis, T., et al., Greengenes, a chimera-checked 16S rRNA gene database and workbench compatible with ARB. Applied and environmental microbiology, 2006. 72(7): p. 5069-5072. 56. Mohsen, A., et al., Impact of quality trimming on the efficiency of reads joining and diversity analysis of Illumina paired-end reads in the context of QIIME1 and QIIME2 microbiome analysis frameworks. BMC bioinformatics, 2019. 20(1): p. 1-10. 57. Zhou, H., et al., Gut microbiota profile in patients with type 1 diabetes based on 16S rRNA gene sequencing: a systematic review. Disease markers, 2020. 2020: p. 3936247. 58. Peters, B.A., et al., A taxonomic signature of obesity in a large study of American adults. Scientific reports, 2018. 8(1): p. 1-13. 59. Okubo, T., et al., Pyrosequence read length of 16S rRNA gene affects phylogenetic assignment of plant-associated bacteria. Microbes and environments, 2009: p. 1202170353-1202170353. 60. Edgar, R.C., UPARSE: highly accurate OTU sequences from microbial amplicon reads. Nature methods, 2013. 10(10): p. 996-998. 61. Bokulich, N.A., et al., Optimizing taxonomic classification of marker-gene amplicon sequences with QIIME 2’s q2-feature-classifier plugin. Microbiome, 2018. 6(1): p. 1-17. 62. Edgar, R.C., SINTAX: a simple non-Bayesian taxonomy classifier for 16S and ITS sequences. BioRxiv, 2016: p. 074161. 63. Murali, A., A. Bhargava, and E.S. Wright, IDTAXA: a novel approach for accurate taxonomic classification of microbiome sequences. Microbiome, 2018. 6(1): p. 1-14. 64. Allard, G., et al., SPINGO: a rapid species-classifier for microbial amplicon sequences. BMC bioinformatics, 2015. 16(1): p. 1-8. 65. Wang, Q., et al., Naive Bayesian classifier for rapid assignment of rRNA sequences into the new bacterial taxonomy. Applied and environmental microbiology, 2007. 73(16): p. 5261-5267. 66. Lu, J. and S.L. Salzberg, Ultrafast and accurate 16S rRNA microbial community analysis using Kraken 2. Microbiome, 2020. 8(1): p. 1-11. 67. Edgar, R.C., Accuracy of taxonomy prediction for 16S rRNA and fungal ITS sequences. PeerJ, 2018. 6: p. e4652. 68. Wood, D.E., J. Lu, and B. Langmead, Improved metagenomic analysis with Kraken 2. Genome biology, 2019. 20(1): p. 1-13. 69. Meola, M., et al., DAIRYdb: a manually curated reference database for improved taxonomy annotation of 16S rRNA gene sequences from dairy products. BMC genomics, 2019. 20(1): p. 1-16. 70. Allen, H.K., et al., Pipeline for amplifying and analyzing amplicons of the V1–V3 region of the 16S rRNA gene. BMC research notes, 2016. 9(1): p. 1-6. 71. Federhen, S., The NCBI taxonomy database. Nucleic acids research, 2012. 40(D1): p. D136-D143. 72. Schloss, P.D., Reintroducing mothur: 10 years later. Applied and environmental microbiology, 2020. 86(2): p. e02343-19. 73. Maidak, B.L., et al., The RDP (ribosomal database project). Nucleic acids research, 1997. 25(1): p. 109-110. 74. Balvočiūtė, M. and D.H. Huson, SILVA, RDP, Greengenes, NCBI and OTT—how do these taxonomies compare? BMC genomics, 2017. 18(2): p. 1-8. 75. Parks, D.H., et al., GTDB: an ongoing census of bacterial and archaeal diversity through a phylogenetically consistent, rank normalized and complete genome-based taxonomy. Nucleic acids research, 2022. 50(D1): p. D785-D794. 76. Desai, H.P., et al., Comparative study using neural networks for 16S ribosomal gene classification. Journal of computational biology, 2020. 27(2): p. 248-258. 77. Alishum, A., DADA2 formatted 16S rRNA gene sequences for both bacteria & archaea (version 4.2). 2021: Zenodo. 78. Bokulich, N.A., et al., mockrobiota: a public resource for microbiome bioinformatics benchmarking. Msystems, 2016. 1(5): p. e00062-16. 79. Callahan, B.J., et al., High-throughput amplicon sequencing of the full-length 16S rRNA gene with single-nucleotide resolution. Nucleic acids research, 2019. 47(18): p. e103-e103. 80. Hung, Y.-M., et al., To compare the performance of prokaryotic taxonomy classifiers using curated 16S full-length rRNA sequences. Computers in biology and medicine, 2022. 145: p. 105416. 81. Lan, Y., et al., Using the RDP classifier to predict taxonomic novelty and reduce the search space for finding novel organisms. PLoS one, 2012. 7(3): p. e32491. 82. Jeong, J., et al., The effect of taxonomic classification by full-length 16S rRNA sequencing with a synthetic long-read technology. Scientific reports, 2021. 11(1): p. 1-12. 83. Nossa, C.W., et al., Design of 16S rRNA gene primers for 454 pyrosequencing of the human foregut microbiome. World journal of gastroenterology: WJG, 2010. 16(33): p. 4135. 84. Xue, Y., et al., Multi-objective feature selection with missing data in classification. IEEE Transactions on Emerging Topics in Computational Intelligence, 2021. 6(2): p. 355-364. 85. Karagöz, M.A. and O.U. Nalbantoglu, Taxonomic classification of metagenomic sequences from Relative Abundance Index profiles using deep learning. Biomedical Signal Processing and Control, 2021. 67: p. 102539. 86. Edgar, R., Taxonomy annotation and guide tree errors in 16S rRNA databases. PeerJ, 2018. 6: p. e5030. 87. Chiarello, M., et al., Ranking the biases: The choice of OTUs vs. ASVs in 16S rRNA amplicon data analysis has stronger effects on diversity measures than rarefaction and OTU identity threshold. PloS one, 2022. 17(2): p. e0264443. 88. Liland, K.H., H. Vinje, and L. Snipen, microclass: an R-package for 16S taxonomy classification. BMC bioinformatics, 2017. 18(1): p. 1-9. 89. Mahmud, K., et al., Influence of tall fescue Epichloë endophytes on rhizosphere soil microbiome. Microorganisms, 2021. 9(9): p. 1843. 90. Lam, T.Y., et al., Superior resolution characterisation of microbial diversity in anaerobic digesters using full-length 16S rRNA gene amplicon sequencing. Water research, 2020. 178: p. 115815. 91. Tremblay, J. and E. Yergeau, Systematic processing of ribosomal RNA gene amplicon sequencing data. GigaScience, 2019. 8(12): p. giz146. 92. Wade, W. and E. Prosdocimi, Profiling of oral bacterial communities. Journal of dental research, 2020. 99(6): p. 621-629. 93. Hur, M. and S.-J. Park, Identification of microbial profiles in heavy-metal-contaminated soil from full-length 16s rRNA reads sequenced by a pacbio system. Microorganisms, 2019. 7(9): p. 357. 94. Pootakham, W., et al., Taxonomic profiling of Symbiodiniaceae and bacterial communities associated with indo-Pacific corals in the Gulf of Thailand using PacBio sequencing of full-length ITS and 16S rRNA genes. Genomics, 2021. 113(4): p. 2717-2729. 95. Perkel, J.M., Data visualization tools drive interactivity and reproducibility in online publishing. Nature, 2018. 554(7690): p. 133-134. 96. Porcu, V., Matplotlib, in Python for data mining quick syntax reference. 2018, Springer. p. 201-234. 97. Hossain, S., et al. Visualization of bioinformatics data with dash bio. in Proceedings of the 18th Python in science conference. 2019. 98. Chen, S., et al., fastp: an ultra-fast all-in-one FASTQ preprocessor. Bioinformatics, 2018. 34(17): p. i884-i890. 99. Vázquez-Baeza, Y., et al., EMPeror: a tool for visualizing high-throughput microbial community data. . GigaScience, 2013;. 2(1). 100. Lee, S.J. and M. Rho, Multimodal deep learning applied to classify healthy and disease states of human microbiome. Scientific reports, 2022. 12(1): p. 1-11. 101. Chen, X., et al., Human disease prediction from microbiome data by multiple feature fusion and deep learning. Iscience, 2022. 25(4): p. 104081. 102. Zhu, Q., et al. The phylogenetic tree based deep forest for metagenomic data classification. in 2018 IEEE international conference on bioinformatics and biomedicine (BIBM). 2018. IEEE. 103. Lo, C. and R. Marculescu, MetaNN: accurate classification of host phenotypes from metagenomic data using neural networks. Bmc Bioinformatics, 2019. 20(12): p. 1-14. 104. Oh, M. and L. Zhang, DeepMicro: deep representation learning for disease prediction based on microbiome data. Scientific reports, 2020. 10(1): p. 1-9. 105. Vaswani, A., et al., Attention is all you need. Advances in neural information processing systems, 2017. 30. 106. Verleysen, M. and D. François. The curse of dimensionality in data mining and time series prediction. in International work-conference on artificial neural networks. 2005. Springer. | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/86621 | - |
| dc.description.abstract | 近十年由於次世代定序技術的快速進步，使得總體基因體學的相關研究迅速發展，其中有大量文獻探討如何藉由分析原核生物的16S核醣體RNA來獲得一個環境中的菌相組成。因此，許多用來分析次世代定序技術所產出的細菌16S短序列資料的工具軟體與資料庫也應運而生。然而，這些工具大多都是開發在Linux作業系統，以指令集介面為主，對於缺乏資訊背景的研究人員來說，使用時將面臨許多困難，因此如何將這些指令集介面的軟體改良成具有親和力的線上圖形化介面系統，以降低分析工作的複雜度，是此份研究的主題之一。 近年來，越來越多的研究開始利用三代定序技術來研究細菌的16S全長序列，16S全長序列能將菌相分析至「種」的層級。菌種分類在16S核醣體RNA分析流程中是十分重要的一個步驟，但過往的文獻大多集中在探討菌種分類器對於次世代定序所產出的細菌短序列的分類效果，面對三代定序所產出的細菌16S全長片段的菌種分類效果卻鮮少有文獻探討之。因此，各個不同的菌種分類器對於原核生物16S全長序列的分類效能亦是本研究的探討主題之一。此外，這份論文也提出一個以菌種名稱為基礎來整合各個常用的16S資料庫之方法，期使該資料庫能提升「種(species)」層級的分類精確度。該整合資料庫的設計，主要是針對16S全長序列來進行菌種分類。 雖然三代定序所產出的原核生物16S全長序列含有較完整的物種分類資訊，能夠準確地將細菌分類至「種(species)」的層級。然而，截至目前為止，市面上仍然缺乏完整且便利的原核生物16S全長序列分析套件。因此，研發能夠分析原核生物16S全長序列的軟體有其必要性。此份研究亦探討如何開發一個友善便捷的線上原核生物16S全長分析系統，以提高研究人員的工作效率。 過去十年間，總體基因體學的研究人員不僅找出許多生物標記，也發表了大量的菌相資料，然而這些資料至今仍然缺乏具體的應用。此份論文除了探究如何開發總體基因體學的應用軟體，在最後也將提及將大量菌相資料與人工智慧做結合的可能發展方向。 | zh_TW |
| dc.description.abstract | The advancement of next-generation sequencing (NGS) technologies in the past decade has facilitated the development of metagenomics research. A large amount of literature focused on how to elucidate the bacterial composition of an environment by analyzing the prokaryotic 16S ribosomal RNA (rRNA) sequences. Therefore, many analytical tools for analyzing prokaryotic 16S rRNA short reads using NGS data were proposed. However, most of these tools were operated on the Linux system and only provided command line interface. For those who lacked programming background, it was difficult to learn the Linux command line and impeded their analytical tasks. Therefore, one of the topics of this dissertation is to improve the command line-based tools and develop a user-friendly online system with graphical interface to enhance researchers’ working efficiency. Next, in recent years, a growing number of research works used third-generation sequencing technologies to discern the bacterial composition to the species level by sequencing full-length prokaryotic 16S rRNA. Taxonomy assignment is a critical step in analyzing the prokaryotic 16S rRNA. Yet, most previous studies focused only on classifying the short bacterial 16S reads generated by NGS technologies. The classifier’s performance on bacterial 16S full-length sequence data generated by third-generation sequencing (TGS) still remained unclear. Therefore, taxonomy assignment performance of the 16S classifiers on classifying the prokaryotic 16S full-length sequences is another topic of this dissertation. In addition, a taxonomy-based method to integrate the widely-used 16S reference databases to classify 16S full-length sequences and improve the assignment accuracy at the species level was proposed in this dissertation. Although the prokaryotic 16S full-length reads provide comprehensive taxonomy information, there still lacks convenient analytical tools for analyzing prokaryotic 16S full-length sequences. Therefore, developing the analytical tools to support 16S full-length pipeline is in demand. The fourth topic of my dissertation focuses on discussing how to develop a user-friendly online prokaryotic 16S full-length analytical system to enhance the working efficiency of the researchers. Lastly, during the past decade, metagenomics researchers discovered many biomarkers and a large amount of microbial composition data has been released. However, there still lacks practical applications for the published data. Therefore, the possible future opportunities to combine the microbial big data with machine learning algorithms are discussed in the last chapter. | en |
| dc.description.provenance | Made available in DSpace on 2023-03-20T00:07:03Z (GMT). No. of bitstreams: 1 U0001-0608202202004800.pdf: 5912046 bytes, checksum: 8d408f0b6f6ce8952f683245367fc010 (MD5) Previous issue date: 2022 | en |
| dc.description.tableofcontents | 論文口試委員審定書 i 誌謝 ii 中文摘要 iii Abstract v Table of Contents vii List of Figures x List of Tables xii Chapter 1. Introduction 1 1.1 Prokaryotic 16S rRNA metabarcoding analysis 1 1.2 Bioinformatic tools for prokaryotic 16S rRNA data analysis 5 1.3 Prokaryotic 16S sequence classifier 7 1.4 Integration of 16S reference databases 8 1.5 An online analytical platform for 16S full-length analysis 9 1.6 Specific aims 10 1.7 Figures 12 Chapter 2. EasyMAP: A user-friendly online platform for analyzing 16S ribosomal RNA sequencing data 16 2.1 Introduction 16 2.2 Materials and methods 17 2.2.1 System implementation 17 2.2.2 Default parameters 18 2.2.3 Plug-ins version 21 2.3 Results 22 2.3.1 System description 22 2.3.2 Further integrated tools 23 2.3.3 Evaluation 24 2.4 Discussion 26 2.5 Figures 29 2.6 Tables 36 Chapter 3. Performance comparison of prokaryotic taxonomy classifiers using curated 16S full-length rRNA sequences 38 3.1 Introduction 38 3.2 Materials and methods 40 3.2.1 Classifier settings 40 3.2.2 Validation datasets 43 3.2.3 Validation methods 44 3.3 Results 46 3.4 Discussion 50 3.5 Figures 56 3.6 Tables 63 Chapter 4. 16S-ITGDB: An integrated database for improving species classification of prokaryotic 16S ribosomal RNA sequences 66 4.1 Introduction 66 4.2 Materials and methods 68 4.2.1 Sequence-based integration 69 4.2.2 Taxonomy-based integration 70 4.2.3 Validation 71 4.3 Results 77 4.4 Discussion 80 4.5 Figures 89 4.6 Tables 96 Chapter 5. OATEN: an online platform for prokaryotic 16S rRNA full-length sequence analysis 102 5.1 Introduction 102 5.2 Materials and methods 103 5.2.1 Initial quality plot 103 5.2.2 Quality control and OTU table construction 104 5.2.3 Taxonomy assignment 104 5.2.4 Alpha diversity 106 5.2.5 Beta diversity 106 5.2.6 Taxonomy differential abundance analysis 107 5.2.7 Gene function prediction 107 5.3 Results 108 5.4 Discussion 109 5.5 Figures 111 Chapter 6. Perspectives 115 6.1 Research discussion 115 6.2 Future directions 118 6.3 Conclusion 121 References 123 | |
| dc.language.iso | en | |
| dc.subject | 菌種分類器 | zh_TW |
| dc.subject | 總體基因體學 | zh_TW |
| dc.subject | 線上分析平台 | zh_TW |
| dc.subject | 16S資料庫整合 | zh_TW |
| dc.subject | 16S核醣體RNA | zh_TW |
| dc.subject | 16S核醣體RNA | zh_TW |
| dc.subject | 總體基因體學 | zh_TW |
| dc.subject | 三代定序 | zh_TW |
| dc.subject | 16S資料庫整合 | zh_TW |
| dc.subject | 線上分析平台 | zh_TW |
| dc.subject | 三代定序 | zh_TW |
| dc.subject | 菌種分類器 | zh_TW |
| dc.subject | 16S database integration | en |
| dc.subject | Metagenomics | en |
| dc.subject | 16S rRNA | en |
| dc.subject | Prokaryotic sequence classifier | en |
| dc.subject | Third-generation sequencing | en |
| dc.subject | Online analysis platform | en |
| dc.subject | 16S database integration | en |
| dc.subject | Metagenomics | en |
| dc.subject | 16S rRNA | en |
| dc.subject | Prokaryotic sequence classifier | en |
| dc.subject | Third-generation sequencing | en |
| dc.subject | Online analysis platform | en |
| dc.title | 建立適用於生醫大數據分析及分類的生物資訊學工具及資料庫 | zh_TW |
| dc.title | Developing bioinformatics tools and databases for biomedical big data analysis and classification | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 110-2 | |
| dc.description.degree | 博士 | |
| dc.contributor.oralexamcommittee | 李心予(Hsin-Yu Lee),吳俊穎(Chun-Ying Wu),邱瀚模(Han-Mo Chiu),曾新穆(Vincent S. Tseng),曹伯年(Po-Nien Tsao),張瑞峰(Ruey-Feng Chang) | |
| dc.subject.keyword | 總體基因體學,16S核醣體RNA,菌種分類器,三代定序,線上分析平台,16S資料庫整合, | zh_TW |
| dc.subject.keyword | Metagenomics,16S rRNA,Prokaryotic sequence classifier,Third-generation sequencing,Online analysis platform,16S database integration, | en |
| dc.relation.page | 129 | |
| dc.identifier.doi | 10.6342/NTU202202108 | |
| dc.rights.note | 同意授權(全球公開) | |
| dc.date.accepted | 2022-08-08 | |
| dc.contributor.author-college | 電機資訊學院 | zh_TW |
| dc.contributor.author-dept | 生醫電子與資訊學研究所 | zh_TW |
| dc.date.embargo-lift | 2022-08-12 | - |
| 顯示於系所單位： | 生醫電子與資訊學研究所 | |
文件中的檔案：
| 檔案 | 大小 | 格式 | |
|---|---|---|---|
| U0001-0608202202004800.pdf | 5.77 MB | Adobe PDF | 檢視/開啟 |
系統中的文件，除了特別指名其著作權條款之外，均受到著作權保護，並且保留所有的權利。