請用此 Handle URI 來引用此文件：
http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/59715完整後設資料紀錄
| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 于宏燦(Hon-Tsen Yu) | |
| dc.contributor.author | An-Chi Cheng | en |
| dc.contributor.author | 鄭安琪 | zh_TW |
| dc.date.accessioned | 2021-06-16T09:34:29Z | - |
| dc.date.available | 2020-02-17 | |
| dc.date.copyright | 2017-02-17 | |
| dc.date.issued | 2017 | |
| dc.date.submitted | 2017-02-13 | |
| dc.identifier.citation | 1. Arbogast, B. S. (2007). A Brief History of the New World Flying Squirrels: Phylogeny, Biogeography, and Conservation Genetics. Journal of Mammalogy, 88(4), 840-849.
2. Asshauer, K. P., B. Wemheuer, R. Daniel, & P. Meinicke. (2015). Tax4Fun: predicting functional profiles from metagenomic 16S rRNA data. Bioinformatics, 31(17), 2882-2884. 3. Bailey, M. T., J. C. Walton, S. E. Dowd, Z. M. Weil, & R. J. Nelson. (2010). Photoperiod modulates gut bacteria composition in male Siberian hamsters (Phodopus sungorus). Brain Behav Immun, 24(4), 577-584. 4. Barker, C. J., A. Gillett, A. Polkinghorne, & P. Timms. (2013). Investigation of the koala (Phascolarctos cinereus) hindgut microbiome via 16S pyrosequencing. Vet Microbiol, 167(3-4), 554-564. 5. Byrnes, G., & A. J. Spence. (2011). Ecological and biomechanical insights into the evolution of gliding in mammals. Integr Comp Biol, 51(6), 991-1001. 6. Campbell, J. H., C. M. Foster, T. Vishnivetskaya, A. G. Campbell, Z. K. Yang, A. Wymore, A. V. Palumbo, E. J. Chesler, & M. Podar. (2012). Host genetic and environmental effects on mouse intestinal microbiota. ISME J, 6(11), 2033-2044. 7. Caporaso, J. G., J. Kuczynski, J. Stombaugh, K. Bittinger, F. D. Bushman, E. K. Costello, N. Fierer, A. G. Pena, J. K. Goodrich, J. I. Gordon, G. A. Huttley, S. T. Kelley, D. Knights, J. E. Koenig, R. E. Ley, C. A. Lozupone, D. McDonald, B. D. Muegge, M. Pirrung, J. Reeder, J. R. Sevinsky, P. J. Turnbaugh, W. A. Walters, J. Widmann, T. Yatsunenko, J. Zaneveld, & R. Knight. (2010). QIIME allows analysis of high-throughput community sequencing data. Nat Methods, 7(5), 335-336. 8. Caporaso, J. G., C. L. Lauber, W. A. Walters, D. Berg-Lyons, J. Huntley, N. Fierer, S. M. Owens, J. Betley, L. Fraser, M. Bauer, N. Gormley, J. A. Gilbert, G. Smith, & R. Knight. (2012). Ultra-high-throughput microbial community analysis on the Illumina HiSeq and MiSeq platforms. ISME J, 6(8), 1621-1624. 9. Carey, H. V., & K. N. Duddleston. (2014). Animal-microbial symbioses in changing environments. J Therm Biol, 44, 78-84. 10. Chung, H., S. J. Pamp, J. A. Hill, N. K. Surana, S. M. Edelman, E. B. Troy, N. C. Reading, E. J. Villablanca, S. Wang, J. R. Mora, Y. Umesaki, D. Mathis, C. Benoist, D. A. Relman, & D. L. Kasper. (2012). Gut immune maturation depends on colonization with a host-specific microbiota. Cell, 149(7), 1578-1593. 11. Cole, J. R., Q. Wang, J. A. Fish, B. Chai, D. M. McGarrell, Y. Sun, C. T. Brown, A. Porras-Alfaro, C. R. Kuske, & J. M. Tiedje. (2014). Ribosomal Database Project: data and tools for high throughput rRNA analysis. Nucleic Acids Res, 42(Database issue), D633-642. 12. Cork, S. J., & W. J. Foley. (1991). Digestive and metabolic strategies of arboreal mammalian folivores in relation to chemical defenses in temperate and tropical forests. CRC Press, 133-158. 13. Costa, M. C., G. Silva, R. V. Ramos, H. R. Staempfli, L. G. Arroyo, P. Kim, & J. S. Weese. (2015). Characterization and comparison of the bacterial microbiota in different gastrointestinal tract compartments in horses. Vet J, 205(1), 74-80. 14. Daniel, H., A. M. Gholami, D. Berry, C. Desmarchelier, H. Hahne, G. Loh, S. Mondot, P. Lepage, M. Rothballer, A. Walker, C. Bohm, M. Wenning, M. Wagner, M. Blaut, P. Schmitt-Kopplin, B. Kuster, D. Haller, & T. Clavel. (2014). High-fat diet alters gut microbiota physiology in mice. ISME J, 8(2), 295-308. 15. De Filippo, C., D. Cavalieri, M. Di Paola, M. Ramazzotti, J. B. Poullet, S. Massart, S. Collini, G. Pieraccini, & P. Lionetti. (2010). Impact of diet in shaping gut microbiota revealed by a comparative study in children from Europe and rural Africa. Proc Natl Acad Sci U S A, 107(33), 14691-14696. 16. de Oliveira, M. N., K. A. Jewell, F. S. Freitas, L. A. Benjamin, M. R. Totola, A. C. Borges, C. A. Moraes, & G. Suen. (2013). Characterizing the microbiota across the gastrointestinal tract of a Brazilian Nelore steer. Vet Microbiol, 164(3-4), 307-314. 17. Demment, M. W., & P. J. Vansoest. (1985). A Nutritional Explanation for Body-Size Patterns of Ruminant and Nonruminant Herbivores. American Naturalist, 125(5), 641-672. 18. DeSantis, T. Z., P. Hugenholtz, N. Larsen, M. Rojas, E. L. Brodie, K. Keller, T. Huber, D. Dalevi, P. Hu, & G. L. Andersen. (2006). Greengenes, a chimera-checked 16S rRNA gene database and workbench compatible with ARB. Appl Environ Microbiol, 72(7), 5069-5072. 19. Edgar, R. C., B. J. Haas, J. C. Clemente, C. Quince, & R. Knight. (2011). UCHIME improves sensitivity and speed of chimera detection. Bioinformatics, 27(16), 2194-2200. 20. Emerson, D., L. Agulto, H. Liu, & L. Liu. (2008). Identifying and Characterizing Bacteria in an Era of Genomics and Proteomics. BioScience, 58(10), 925. 21. Ewing, B., & P. Green. (1998). Base-Calling of Automated Sequencer Traces UsingPhred.II. Error Probabilities. Genome Research, 8(3), 186-194. 22. Garber, M., M. G. Grabherr, M. Guttman, & C. Trapnell. (2011). Computational methods for transcriptome annotation and quantification using RNA-seq. Nat Methods, 8(6), 469-477. 23. Goodrich, J. K., J. L. Waters, A. C. Poole, J. L. Sutter, O. Koren, R. Blekhman, M. Beaumont, W. Van Treuren, R. Knight, J. T. Bell, T. D. Spector, A. G. Clark, & R. E. Ley. (2014). Human genetics shape the gut microbiome. Cell, 159(4), 789-799. 24. Henry, C. J. K. (2007). Basal metabolic rate studies in humans: measurement and development of new equations. Public Health Nutrition, 8(7a). 25. Human Microbiome Project, C. (2012). Structure, function and diversity of the healthy human microbiome. Nature, 486(7402), 207-214. 26. Kembel, S. W., M. Wu, J. A. Eisen, & J. L. Green. (2012). Incorporating 16S gene copy number information improves estimates of microbial diversity and abundance. PLoS Comput Biol, 8(10), e1002743. 27. Kim, K. A., W. Gu, I. A. Lee, E. H. Joh, & D. H. Kim. (2012). High fat diet-induced gut microbiota exacerbates inflammation and obesity in mice via the TLR4 signaling pathway. PLoS One, 7(10), e47713. 28. Kopylova, E., L. Noe, & H. Touzet. (2012). SortMeRNA: fast and accurate filtering of ribosomal RNAs in metatranscriptomic data. Bioinformatics, 28(24), 3211-3217. 29. Koren, O., J. K. Goodrich, T. C. Cullender, A. Spor, K. Laitinen, H. K. Backhed, A. Gonzalez, J. J. Werner, L. T. Angenent, R. Knight, F. Backhed, E. Isolauri, S. Salminen, & R. E. Ley. (2012). Host remodeling of the gut microbiome and metabolic changes during pregnancy. Cell, 150(3), 470-480. 30. Kozich, J. J., S. L. Westcott, N. T. Baxter, S. K. Highlander, & P. D. Schloss. (2013). Development of a dual-index sequencing strategy and curation pipeline for analyzing amplicon sequence data on the MiSeq Illumina sequencing platform. Appl Environ Microbiol, 79(17), 5112-5120. 31. Langille, M. G., C. J. Meehan, J. E. Koenig, A. S. Dhanani, R. A. Rose, S. E. Howlett, & R. G. Beiko. (2014). Microbial shifts in the aging mouse gut. Microbiome, 2(1), 50. 32. Langille, M. G., J. Zaneveld, J. G. Caporaso, D. McDonald, D. Knights, J. A. Reyes, J. C. Clemente, D. E. Burkepile, R. L. Vega Thurber, R. Knight, R. G. Beiko, & C. Huttenhower. (2013). Predictive functional profiling of microbial communities using 16S rRNA marker gene sequences. Nat Biotechnol, 31(9), 814-821. 33. Langman, V. A., T. J. Roberts, J. Black, G. M. O. Maloiy, N. C. Heglund, J.-M. Weber, R. Kram, & C. R. Taylor. (1995). Moving cheaply: energetics of walking in the african elephant. The Journal of Experimental Biology, 629-632. 34. Lee, P. F. (1998). Body Size Comparison of Two Giant Flying Squirrel Species in Taiwan. Acta Zoologica Taiwanica, 9(1), 51-57. 35. Lee, P. F., Y. S. Lin, & D. R. Progulske. (1993). Reproductive Biology of the Red-Giant Flying Squirrel, Petaurista petaurista, in Taiwan. Journal of Mammalogy, 74(4), 982-989. 36. Ley, R. E., M. Hamady, C. Lozupone, P. J. Turnbaugh, R. R. Ramey, J. S. Bircher, M. L. Schlegel, T. A. Tucker, M. D. Schrenzel, R. Knight, & J. I. Gordon. (2008). Evolution of mammals and their gut microbes. Science, 320(5883), 1647-1651. 37. Lu, H. P., Y. C. Lai, S. W. Huang, H. C. Chen, C. H. Hsieh, & H. T. Yu. (2014). Spatial heterogeneity of gut microbiota reveals multiple bacterial communities with distinct characteristics. Sci Rep, 4, 6185. 38. Mao, S., M. Zhang, J. Liu, & W. Zhu. (2015). Characterising the bacterial microbiota across the gastrointestinal tracts of dairy cattle: membership and potential function. Sci Rep, 5, 16116. 39. Mardis, E. R. (2008). Next-generation DNA sequencing methods. Annu Rev Genomics Hum Genet, 9, 387-402. 40. Martin, M. (2011). Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet journal, 17(1), 10-12. 41. McDonald, J. H. (2009). Handbook of biological statistics (Vol. 2): Sparky House Publishing Baltimore. 42. Middelbos, I. S., B. M. Vester Boler, A. Qu, B. A. White, K. S. Swanson, & G. C. Fahey, Jr. (2010). Phylogenetic characterization of fecal microbial communities of dogs fed diets with or without supplemental dietary fiber using 454 pyrosequencing. PLoS One, 5(3), e9768. 43. Minchin, P. R. (1987). An evaluation of the relative robustness of techniques for ecological ordination. Vegetatio, 69(1), 89-107. 44. Mullis, K. B., & F. A. Faloona. (1987). Specific synthesis of DNA in vitro via a polymerase-catalyzed chain reaction. Methods Enzymol, 155, 335-350. 45. Nagpal, S., M. M. Haque, & S. S. Mande. (2016). Vikodak--A Modular Framework for Inferring Functional Potential of Microbial Communities from 16S Metagenomic Datasets. PLoS One, 11(2), e0148347. 46. Ohdachi, S. D., Y. Ishibashi, M. A. Iwasa, & T. Saitoh. (2009). The wild mammals of Japan: Shoukadoh Book Sellers Kyoto. 47. Orth, J. D., I. Thiele, & B. O. Palsson. (2010). What is flux balance analysis? Nat Biotechnol, 28(3), 245-248. 48. Oshida, T., L.-K. Lin, S.-W. Chang, Y.-J. Chen, & J.-K. Lin. (2011). Phylogeography of two sympatric giant flying squirrel subspecies, Petaurista alborufus lena and P. philippensis grandis (Rodentia: Sciuridae), in Taiwan. Biological Journal of the Linnean Society, 102(2), 404-419. 49. Oshlack, A., M. D. Robinson, & M. D. Young. (2010). From RNA-seq reads to differential expression results. Genome Biol, 11(12), 220. 50. Quast, C., E. Pruesse, P. Yilmaz, J. Gerken, T. Schweer, P. Yarza, J. Peplies, & F. O. Glockner. (2013). The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucleic Acids Res, 41(Database issue), D590-596. 51. Seedorf, H., N. W. Griffin, V. K. Ridaura, A. Reyes, J. Cheng, F. E. Rey, M. I. Smith, G. M. Simon, R. H. Scheffrahn, D. Woebken, A. M. Spormann, W. Van Treuren, L. K. Ursell, M. Pirrung, A. Robbins-Pianka, B. L. Cantarel, V. Lombard, B. Henrissat, R. Knight, & J. I. Gordon. (2014). Bacteria from diverse habitats colonize and compete in the mouse gut. Cell, 159(2), 253-266. 52. Segata, N., J. Izard, L. Waldron, D. Gevers, L. Miropolsky, W. S. Garrett, & C. Huttenhower. (2011). Metagenomic biomarker discovery and explanation. Genome Biol, 12(6), R60. 53. Speakman, J. (1997). Factors influencing the daily energy expenditure of small mammals. Proc Nutr Soc, 56(3), 1119-1136. 54. Speakman, J. R. (2005). Body size, energy metabolism and lifespan. J Exp Biol, 208(Pt 9), 1717-1730. 55. Stearns, J. C., M. D. Lynch, D. B. Senadheera, H. C. Tenenbaum, M. B. Goldberg, D. G. Cvitkovitch, K. Croitoru, G. Moreno-Hagelsieb, & J. D. Neufeld. (2011). Bacterial biogeography of the human digestive tract. Sci Rep, 1, 170. 56. Stevenson, T. J., K. N. Duddleston, & C. L. Buck. (2014). Effects of season and host physiological state on the diversity, density, and activity of the arctic ground squirrel cecal microbiota. Appl Environ Microbiol, 80(18), 5611-5622. 57. Suzuki, T. A., & M. W. Nachman. (2016). Spatial Heterogeneity of Gut Microbial Composition along the Gastrointestinal Tract in Natural Populations of House Mice. PLoS One, 11(9), e0163720. 58. Thomas, F., J. H. Hehemann, E. Rebuffet, M. Czjzek, & G. Michel. (2011). Environmental and gut bacteroidetes: the food connection. Front Microbiol, 2, 93. 59. Thorington Jr, R. W., J. L. Koprowski, M. A. Steele, & J. F. Whatton. (2012). Squirrels of the world: JHU Press. 60. Vetrovsky, T., & P. Baldrian. (2013). The variability of the 16S rRNA gene in bacterial genomes and its consequences for bacterial community analyses. PLoS One, 8(2), e57923. 61. Wei, F., X. Wang, & Q. Wu. (2015). The giant panda gut microbiome. Trends Microbiol, 23(8), 450-452. 62. White, C. R., & R. S. Seymour. (2003). Mammalian basal metabolic rate is proportional to body mass2/3. Proc Natl Acad Sci U S A, 100(7), 4046-4049. 63. Woese, C. R., & G. E. Fox. (1977). Phylogenetic structure of the prokaryotic domain: the primary kingdoms. Proc Natl Acad Sci U S A, 74(11), 5088-5090. 64. Woese, C. R., O. Kandler, & M. L. Wheelis. (1990). Towards a natural system of organisms: proposal for the domains Archaea, Bacteria, and Eucarya. Proc Natl Acad Sci U S A, 87(12), 4576-4579. 65. Yu, F., F. Yu, J. Pang, C. W. Kilpatrick, P. M. McGuire, Y. Wang, S. Lu, & C. A. Woods. (2006). Phylogeny and biogeography of the Petaurista philippensis complex (Rodentia: Sciuridae), inter- and intraspecific relationships inferred from molecular and morphometric analysis. Mol Phylogenet Evol, 38(3), 755-766. 66. Zhang, J., K. Kobert, T. Flouri, & A. Stamatakis. (2014). PEAR: a fast and accurate Illumina Paired-End reAd mergeR. Bioinformatics, 30(5), 614-620. 67. Zhou, X., B. Wang, Q. Pan, J. Zhang, S. Kumar, X. Sun, Z. Liu, H. Pan, Y. Lin, G. Liu, W. Zhan, M. Li, B. Ren, X. Ma, H. Ruan, C. Cheng, D. Wang, F. Shi, Y. Hui, Y. Tao, C. Zhang, P. Zhu, Z. Xiang, W. Jiang, J. Chang, H. Wang, Z. Cao, Z. Jiang, B. Li, G. Yang, C. Roos, P. A. Garber, M. W. Bruford, R. Li, & M. Li. (2014). Whole-genome sequencing of the snub-nosed monkey provides insights into folivory and evolutionary history. Nat Genet, 46(12), 1303-1310. 68. 大久保慶信, 七條宏樹, & 渡部大介. (2015). 滑空性эЗ類ソ繊維質消化: уДДжシЯо⑦хх⑦ヮソ比較. 日本環境動物昆虫学会誌, 26(1), 29-35. 69. 林佩蓉. (2000). 福山試驗林食果動物對五種樟科樹木果實與種子的利用. 國立東華大學自然資源管理研究所碩士論文. 70. 郭奇芊. (1999). 福山試驗林大赤鼯鼠(Petaurista petaurista)之食性,活動範圍及活動模式. 國立臺灣大學動物學硏究所碩士論文. | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/59715 | - |
| dc.description.abstract | 食葉性飛鼠是體型最小的樹棲哺乳動物，其體型為食葉性動物最小極限，全世界約有40種體型各異之飛鼠，彼此的基礎代謝率也因而有所差異，使得其各自占有食性利基。為了維持較高之單位質量代謝率，我們認為飛鼠的腸道菌扮演了關鍵的角色。本研究分析6隻Petaurista (平均體重1500 公克)、4隻Trogopterus (平均體重400 公克)、19隻Pteromys (平均體重150公克) 的腸道菌組成及預測其基因功能，利用次世代定序技術分析飛鼠糞便中16S核醣體核醣核酸基因並以PICRUSt演算法預測其分解纖維素之功能基因。結果顯示Petaurista 的腸道菌有95%屬於Firmicutes，Trogopterus約有83%屬於Firmicutes，而Pteromys則只有80%屬於Firmicutes。近年來的研究顯示肥胖的宿主其腸道中Firmicutes 占有比例均較高，且嚴格食葉性的哺乳動物體型極限大約為0.9-1.7公斤，因此得以推斷Petaurista 因為腸道中含有高比例之Firmicutes 而得以維持其高比例之食葉性，而Pteromys則依賴較多樣化之食物來源以維持其較高之單位質量代謝率。本研究亦以腸道菌之醣類代謝基因與代謝途徑、物種棲地之氣候來解釋宿主種間與宿主個體間之腸道菌相差異。 | zh_TW |
| dc.description.abstract | Folivorous flying squirrels are among the smallest arboreal mammals, reaching the limit of metabolic demands subsisting on a low quality diet (e.g. leaves) that the flying squirrels recruit suites of gut microbes to digest throughout their evolution history. Yet, even among flying squirrel species (ca. 40 species), their body sizes differ by several folds posting drastic difference in mass-specific metabolic rates and dietary niche. Therefore we studied flying squirrels of three genera, Petaurista (average weight 1500 g), Trogopterus (average weight 400g) and Pteromys (average weight 150g) and expect to see different gut microbiota that may link to efficiency in digesting plant fibers. We analyzed the fecal samples from 6 Petaurista and 19 Pteromys with 16S rRNA gene amplicon and NGS. 160 and 510 OTUs were discovered in Petaurista and Pteromys, respectively. Gut microbiota are distinct in two genera. The most distinct feature of the differences lies in the bacterial phylum Firmicutes which are over 95% in Petaurista and only 80% in Pteromys. Firmicutes are considered more abundant in obese mice to gain higher mass-specific metabolic rates. It is generally considered that mammals below the mass range 0.9-1.7 kg are near the lowest mass margin a species can be strictly folivorous. Petaurista is strictly folivorous while Pteromys take more rich food such as fruits and nuts. Therefore, it appears that the strictly folivorous Petaurista might have recruited more Firmicutes to sustain primarily on leave. We also discuss the metabolic features in the bacterial phyla and weather of habitats that may contribute to the mass-specific metabolic rates and dietary niche of the two genera of flying squirrels. | en |
| dc.description.provenance | Made available in DSpace on 2021-06-16T09:34:29Z (GMT). No. of bitstreams: 1 ntu-106-R03b21028-1.pdf: 2316114 bytes, checksum: bdc2301a78dc9dc87a63368d05e7ff9a (MD5) Previous issue date: 2017 | en |
| dc.description.tableofcontents | 口試委員會審定書…………………….………………………………….………..……i
誌謝…………………………………….….…………………………………………….ii 中文摘要…………………………………………………………..……………………iii Abstract…………………………………...…………………………….……………….iv 目錄…………………………………….….…………………………………………….v List of figures and tables………………………….……………………………...……viii 第一章 前言……………………………….……………………………………………1 1.1 研究對象介紹…………………………………………….………...…………1 1.2 植食性哺乳動物之食性與代謝速率…………………………………………2 1.3 腸道共生細菌……………………………………………………….…...……3 1.4 總體基因體學…………………………………………………….………...…4 1.4.1 次世代定序…………………….…………..…….……..…………..........5 1.4.2 定序資料演算法……………...………………………………………….6 1.4.3 基因功能性預測……………………………………………….………..7 1.5 研究目標……………………………………………………..…………...…...7 第二章 材料與方法…………..…………………….…………………….…..………...9 2.1 糞便採集…………………………….……………………….…..…...……….9 2.2 萃取DNA……………………………….……………………………...……...9 2.3 擴增細菌16S rRNA 基因片段…………………………………..……...…..10 2.4 萃取期望片段之16S rRNA 基因…………………….…………….….……11 2.5 次世代定序.……………………….………………….………….……..……11 2.6 序列資料處理………………………….…………………………………….12 2.6.1 引子序列裁切與正反序列黏合..……….…………….……………...12 2.6.2 QIIME序列比對………………………......…………………………..12 2.7 統計分析與圖形化輸出...………………………………..…………….……13 2.7.1 序列複製數校正………………………………. …………………….13 2.7.2 菌相比例統計……………………………………………….………..14 2.7.3 主坐標分析……………………..……………………….……………14 2.7.4 線性區別分析...............................................................................…....14 2.8 腸道菌功能基因預測….………………………………………………….…15 2.8.1 代謝途徑表現量預測…………………………….……..……………15 2.8.2 菌種貢獻度分析…………………………………….……..…………15 第三章 結果………………………………………….…..……………..……………..16 3.1 飛鼠腸道菌相分布與屬間差異……………………………………….…….16 3.2 預測飛鼠腸道菌醣類代謝基因與代謝途徑………………………….…….18 第四章 討論…………………………………………………………….……….…….19 4.1 飛鼠腸道菌相分布與屬間差異……………………………………….…….19 4.2 預測飛鼠腸道菌醣類代謝基因與代謝途徑………………………….…….21 參考文獻……………………………………………………………………….…………………….22 Figures …………………………………...…………………………………………………………..30 Tables…………………………………...…………………………………………………………….40 Appendix………………………………………………………………………………….………….54 Fig. A.1. Quality control statistics (FastQC) of NGS data. …………………………………….54 Fig. A.2. Rarefaction of (A) chao 1 diversity and (B) observated OTUs……...…………...…...56 Fig. A.3. Core OTUs shared by 4 host genera. ……………………………………………...….57 Table A.1. OTU numbers and reads of orders with the same copy number.…………………....58 Table A.2. OTU numbers and reads of families with the same copy number…………..……....59 Table A.3. OTU numbers and reads of genera with the same copy number.…………………...60 Table A.4. 26 Core OTUs shared by 5 host species.……………..………….……………….....61 | |
| dc.language.iso | zh-TW | |
| dc.subject | 次世代定序 | zh_TW |
| dc.subject | 食葉性囓齒目動物 | zh_TW |
| dc.subject | 單位質量代謝率 | zh_TW |
| dc.subject | 腸道菌相 | zh_TW |
| dc.subject | 總體基因體學 | zh_TW |
| dc.subject | 食葉性囓齒目動物 | zh_TW |
| dc.subject | 單位質量代謝率 | zh_TW |
| dc.subject | 腸道菌相 | zh_TW |
| dc.subject | 總體基因體學 | zh_TW |
| dc.subject | 次世代定序 | zh_TW |
| dc.subject | next generation sequencing | en |
| dc.subject | folivorous rodent | en |
| dc.subject | mass-specific metabolic rate | en |
| dc.subject | gut microbiota | en |
| dc.subject | metagenomics | en |
| dc.subject | next generation sequencing | en |
| dc.subject | mass-specific metabolic rate | en |
| dc.subject | gut microbiota | en |
| dc.subject | metagenomics | en |
| dc.subject | folivorous rodent | en |
| dc.title | 食葉性囓齒目動物之腸道菌相研究 | zh_TW |
| dc.title | Gut Microbiota Study of Folivorous Rodents | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 105-1 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 黃曉薇,廖本揚,湯森林,陳俊堯 | |
| dc.subject.keyword | 食葉性囓齒目動物,單位質量代謝率,腸道菌相,總體基因體學,次世代定序, | zh_TW |
| dc.subject.keyword | folivorous rodent,mass-specific metabolic rate,gut microbiota,metagenomics,next generation sequencing, | en |
| dc.relation.page | 62 | |
| dc.identifier.doi | 10.6342/NTU201700527 | |
| dc.rights.note | 有償授權 | |
| dc.date.accepted | 2017-02-14 | |
| dc.contributor.author-college | 生命科學院 | zh_TW |
| dc.contributor.author-dept | 生命科學系 | zh_TW |
| 顯示於系所單位： | 生命科學系 | |
文件中的檔案：
| 檔案 | 大小 | 格式 | |
|---|---|---|---|
| ntu-106-1.pdf 未授權公開取用 | 2.26 MB | Adobe PDF |
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。