請用此 Handle URI 來引用此文件:
http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/51253
完整後設資料紀錄
DC 欄位 | 值 | 語言 |
---|---|---|
dc.contributor.advisor | 趙坤茂(Kun-mao Chao) | |
dc.contributor.author | Sara Roland | en |
dc.contributor.author | 羅玳琳 | zh_TW |
dc.date.accessioned | 2021-06-15T13:28:38Z | - |
dc.date.available | 2016-03-08 | |
dc.date.copyright | 2016-03-08 | |
dc.date.issued | 2016 | |
dc.date.submitted | 2016-02-05 | |
dc.identifier.citation | [1] J. C. Wooley and Y. Ye, “Metagenomics: Facts and Artifacts, and Computational Challenges,” J. Comput. Sci. Technol. Journal of Computer Science and Technology, vol. 25, no. 1, pp. 71–81, 2010.
[2] R. I. Amann, W. Ludwig, and K. H. Schleifer, “Phylogenetic identification and in situ detection of individual microbial cells without cultivation,” Microbiological Reviews, vol. 59, no. 1, pp. 143–69, 1995. [3] M. A. Peabody, T. V. Rossum, R. Lo, and F. S. L. Brinkman, “Evaluation of shotgun metagenomics sequence classification methods using in silico and in vitro simulated communities,” BMC Bioinformatics, vol. 16, no. 1, Apr. 2015. [4] M. Kim, K.-H. Lee, S.-W. Yoon, B.-S. Kim, J. Chun, and H. Yi, “Analytical Tools and Databases for Metagenomics in the Next-Generation Sequencing Era,” Genomics Inform Genomics & Informatics, vol. 11, no. 3, p. 102, 2013. [5] P., A. Oulas, C. Pavloudi, P. Polymenakou, N. Papanikolaou, G. Kotoulas, C. Arvanitidis, and I. Iliopoulos, “Metagenomics: Tools and Insights for Analyzing Next-Generation Sequencing Data Derived from Biodiversity Studies,” BBI Bioinformatics and Biology Insights, p. 75, 2015. [6] H. Teeling and F. O. Glockner, “Current opportunities and challenges in microbial metagenome analysis--a bioinformatic perspective,” Briefings in Bioinformatics, vol. 13, no. 6, pp. 728–742, Sep. 2012. [7] D. J. Munroe and T. J. R. Harris, “Third-generation sequencing fireworks at Marco Island,” Nat Biotechnol Nature Biotechnology, vol. 28, no. 5, pp. 426–428, 2010. [8] W. B. Whitman, D. C. Coleman, and W. J. Wiebe, “Prokaryotes: The unseen majority,” Proceedings of the National Academy of Sciences, vol. 95, no. 12, pp. 6578–6583, Sep. 1998. [9] N. C. Kyrpides, P. Hugenholtz, J. A. Eisen, T. Woyke, M. Göker, C. T. Parker, R. Amann, B. J. Beck, P. S. G. Chain, J. Chun, R. R. Colwell, A. Danchin, P. Dawyndt, T. Dedeurwaerdere, E. F. Delong, J. C. Detter, P. D. Vos, T. J. Donohue, X.-Z. Dong, D. S. Ehrlich, C. Fraser, R. Gibbs, J. Gilbert, P. Gilna, F. O. Glöckner, J. K. Jansson, J. D. Keasling, R. Knight, D. Labeda, A. Lapidus, J.-S. Lee, W.-J. Li, J. Ma, V. Markowitz, E. R. B. Moore, M. Morrison, F. Meyer, K. E. Nelson, M. Ohkuma, C. A. Ouzounis, N. Pace, J. Parkhill, N. Qin, R. Rossello-Mora, J. Sikorski, D. Smith, M. Sogin, R. Stevens, U. Stingl, K.-I. Suzuki, D. Taylor, J. M. Tiedje, B. Tindall, M. Wagner, G. Weinstock, J. Weissenbach, O. White, J. Wang, L. Zhang, Y.-G. Zhou, D. Field, W. B. Whitman, G. M. Garrity, and H.-P. Klenk, “Genomic Encyclopedia of Bacteria and Archaea: Sequencing a Myriad of Type Strains,” PLoS Biology PLoS Biol, vol. 12, no. 8, May 2014. [10] E. M. Glass and F. Meyer, “The Metagenomics RAST Server: A Public Resource for the Automatic Phylogenetic and Functional Analysis of Metagenomes,” Handbook of Molecular Microbial Ecology I Metagenomics and Complementary Approaches, pp. 325–331, Mar. 2011. [11] S. Wu, Z. Zhu, L. Fu, B. Niu, and W. Li, “WebMGA: a customizable web server for fast metagenomic sequence analysis,” BMC Genomics, vol. 12, no. 1, p. 444, 2011. [12] J. Droge and A. C. Mchardy, “Taxonomic binning of metagenome samples generated by next-generation sequencing technologies,” Briefings in Bioinformatics, vol. 13, no. 6, pp. 646–655, 2012. [13] A. C. Mchardy, H. G. Martín, A. Tsirigos, P. Hugenholtz, and I. Rigoutsos, “Accurate phylogenetic classification of variable-length DNA fragments,” Nature Methods Nat Meth, vol. 4, no. 1, pp. 63–72, Oct. 2006. [14] S. T. Cole, R. Brosch, J. Parkhill, T. Garnier, C. Churcher, D. Harris, S. V. Gordon, K. Eiglmeier, S. Gas, C. E. Barry, F. Tekaia, K. Badcock, D. Basham, D. Brown, T. Chillingworth, R. Connor, R. Davies, K. Devlin, T. Feltwell, S. Gentles, N. Hamlin, S. Holroyd, T. Hornsby, K. Jagels, A. Krogh, J. Mclean, S. Moule, L. Murphy, K. Oliver, J. Osborne, M. A. Quail, M.-A. Rajandream, J. Rogers, S. Rutter, K. Seeger, J. Skelton, R. Squares, S. Squares, J. E. Sulston, K. Taylor, S. Whitehead, and B. G. Barrell, “Deciphering the biology of Mycobacterium tuberculosis from the complete genome sequence,” Nature, vol. 393, no. 6685, pp. 537–544, Nov. 1998. [15] G. L. Rosen, E. R. Reichenberger, and A. M. Rosenfeld, “NBC: the Naive Bayes Classification tool webserver for taxonomic classification of metagenomic reads,” Bioinformatics, vol. 27, no. 1, pp. 127–129, Aug. 2010. [16] M. Mohammed, T. Ghosh, R. Reddy, C. V. S. K. Reddy, N. Singh, and S. S. Mande, “INDUS - a composition-based approach for rapid and accurate taxonomic classification of metagenomic sequences,” BMC Genomics, vol. 12, no. Suppl 3, 2011. [17] D. T. Pride, “Evolutionary Implications of Microbial Genome Tetranucleotide Frequency Biases,” Genome Research, vol. 13, no. 2, pp. 145–158, 2003. [18] M. H. Mohammed, T. S. Ghosh, N. K. Singh, and S. S. Mande, “SPHINX--an algorithm for taxonomic binning of metagenomic sequences,” Bioinformatics, vol. 27, no. 1, pp. 22–30, 2010. [19] R. M. Reddy, M. H. Mohammed, and S. S. Mande, “TWARIT: An extremely rapid and efficient approach for phylogenetic classification of metagenomic sequences,” Gene, vol. 505, no. 2, pp. 259–265, 2012. [20] M. Wu and A. J. Scott, “Phylogenomic analysis of bacterial and archaeal sequences with AMPHORA2,” Bioinformatics, vol. 28, no. 7, pp. 1033–1034, Dec. 2012. [21] C. V. Mering, P. Hugenholtz, J. Raes, S. G. Tringe, T. Doerks, L. J. Jensen, N. Ward, and P. Bork, “Quantitative Phylogenetic Assessment of Microbial Communities in Diverse Environments,” Science, vol. 315, no. 5815, pp. 1126–1130, 2007. | |
dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/51253 | - |
dc.description.abstract | This thesis presents a survey of classification, or binning, algorithms for the purpose of the evaluation of the accuracy of datasets generated with next-generation sequencing technologies in metagenomic studies. In the past few years, great advances have taken place in the field of next-generation sequencing technologies, and many cutting edge algorithms have been developed to process the data generated by studies utilizing these technologies. However, the development of technologies able to generate vast amounts of data has sometimes outpaced the ability of scientists and researchers to develop ways to properly evaluate the data. The purpose of this survey is to access the applicability of algorithms developed over the last decade to the most popular sequencing technologies today, which often have much shorter read lengths than and different error profiles from earlier sequencing technologies. | en |
dc.description.provenance | Made available in DSpace on 2021-06-15T13:28:38Z (GMT). No. of bitstreams: 1 ntu-105-R02922147-1.pdf: 487206 bytes, checksum: 0eeb952e09df3ed7a78d4054e984b497 (MD5) Previous issue date: 2016 | en |
dc.description.tableofcontents | 1. Introduction 1
2. The Development of the Field of Metagenomics 2 2.1 Definition of Metagenomics 2 2.2 Methods of Metagenomics 2 2.3 Challenges Inherent in Metagenomic Studies 3 3. Next Generation Sequencing Technologies 5 3.1 Next-Generation Technologies 5 3.1.1 Illumina 6 3.1.2 SOLiD 6 3.2 Third-Generation Technologies 7 3.2.1 Pacific Biosciences 7 4. Metagenomic Binning Algorithms 9 4.1 Sequence Similarity-Based Algorithms 9 4.1.1 MG-RAST 10 4.1.2 WebMGA 11 4.2 Sequence Composition-Based Algorithms 11 4.2.1 Naïve Bayes Classifier 13 4.2.2 INDUS 13 4.3 Hybrid Methods 14 4.3.1 SPHINX 14 4.3.2 TWARIT 15 4.4 Marker Methods 15 4.4.1 MLTreeMap 16 4.4.2 Amphora2/AmphoraNet 16 5. Experiments with Simulated Datasets 18 5.1 ART Sequence Simulation 18 5.2 Sequencing Technologies Profiled 20 5.2.1 454 Pyrosequencing 20 5.2.2 Illumina Sequencing 20 5.3 Results 20 5.4 Discussion of Results 22 5.5 Conclusion 23 | |
dc.language.iso | en | |
dc.title | 次世代定序資料分類之總體基因組學裝箱演算法研究 | zh_TW |
dc.title | A Survey of Metagenomic Binning Algorithms as Applied to the Analysis of Next-Generation Datasets | en |
dc.type | Thesis | |
dc.date.schoolyear | 104-1 | |
dc.description.degree | 碩士 | |
dc.contributor.oralexamcommittee | 朱安強(An-qiang Zhu),王弘倫(Si-lun Wang) | |
dc.subject.keyword | 總體基因組學,演算法,次世代定序, | zh_TW |
dc.subject.keyword | Metagenomics,Algorithms,Next-Generation Sequencing, | en |
dc.relation.page | 28 | |
dc.rights.note | 有償授權 | |
dc.date.accepted | 2016-02-06 | |
dc.contributor.author-college | 電機資訊學院 | zh_TW |
dc.contributor.author-dept | 資訊工程學研究所 | zh_TW |
顯示於系所單位: | 資訊工程學系 |
文件中的檔案:
檔案 | 大小 | 格式 | |
---|---|---|---|
ntu-105-1.pdf 目前未授權公開取用 | 475.79 kB | 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。