Automatic discovery of metagenomic structure

Proceedings of the International Joint Conference on Neural Networks

Volumn 2015-September, Issue , 2015, Pages

  Lux, Markus ^a   Sczyrba, Alexander ^a   Hammer, Barbara ^a  

^a BIELEFELD UNIVERSITY   (Germany)

Author keywords

Robustness

Indexed keywords

ARTIFICIAL INTELLIGENCE; AUTOMATION; CLUSTERING ALGORITHMS; LEARNING SYSTEMS; ROBUSTNESS (CONTROL SYSTEMS);

AUTOMATED SELECTION; AUTOMATIC DISCOVERY; CLUSTERING TECHNIQUES; DIMENSIONALITY REDUCTION; HIGH DIMENSIONAL DATA; NUMBER OF CLUSTERS; OPTIMUM PARAMETERS; QUANTITATIVE EVALUATION;

PARAMETER ESTIMATION;

EID: 84951025402     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/IJCNN.2015.7280500     Document Type: Conference Paper

References (37)

1. H. M. P. Consortium et aI., "A framework for human microbiome research," Nature, vol. 486, no. 7402, pp. 215-221, 2012.
2. D. N. Frank and N. R. Pace, "Gastrointestinal microbiology enters the metagenomics era," Current opinion in gastroenterology, vol. 24, no. 1, pp. 4-10, 2008.
3. E. A. Gies, K. M. Konwar. J. T. Beatty, and S. J. Hallam, "Illuminating microbial dark matter in meromictic sakinaw lake," Applied and environmental microbiology, vol. 80. no. 21. pp. 6807-6818, 2014.
4. C. Rinke, P. Schwientek, A. Sczyrba, N. N. Ivanova, J. J. Anderson, J.-F. Cheng, A. Darling, S. Malfatti, B. K. Swan, E. A. Gies et al., "Insights into the phylogeny and coding potential of microbial dark matter," Nature, 2013.
5. T. C. Charles and D. Marco, "The potential for investigation of plantmicrobe interactions using metagenomics methods," Metagenomics: Theory, methods and applications, pp. 102-107, 2010.
6. M. Hess, A. Sczyrba, R. Egan, T. Kim, H. Chokhawala, G. Schroth, S. Luo, D. Clark, F. Chen, and T. Zhang, "Metagenomic discovery of biomass-degrading genes and genomes from cow rumen," Science, vol. 331, no. 6016, pp. 463-467, 2011.
7. J. Qin, R. Li, J. Raes, M. Arumugam, K. Burgdorf, C. Manichanh, T. Nielsen, N. Pons, F. Levenez, and T. Yamada, "A human gut microbial gene catalogue established by metage nomic sequencing," Nature, vol. 464, no. 7285, pp. 59-65, 2010.
8. J. Raes, I. Letunic, T. Yamada, L. J. Jensen, and P. Bork, "Toward molecular trait-based ecology through integration of biogeochemical, geographical and metagenomic data," Molecular systems biology, vol. 7, no. 1,2011.
9. S. S. Mande, M. H. Mohammed, and T. S. Ghosh, "Classification of metagenomic sequences: methods and challenges," Briefings in Bioinformatics, vol. 13, no. 6, pp. 669-681, 2012.
10. J. Alneberg, B. S. Bjarnason, J. de Bruijn, M. Schirmer, J. Quick, U. Z. Ijaz,N J. Loman,A F. Andersson, and C. Quince," Concoct: Clustering contigs on coverage and composition," arXiv preprint arXiv:1312. 4038, 2013.
11. Y.-w. Wu, Y.-H. Tang, S. G. Tringe, B. A. Simmons, and S. W. Singer, "Maxbin: An automated binning method to recover individual genomes from metagenomes using an expectation-maximization algorithm," Microbiome, vol. 2, no. I, pp. 1-18, 2014.
12. M. Imelfort, D. Parks, B. J. Woodcroft, P. Dennis, P. Hugenholtz, and G. W. Tyson, "Groopm: An automated tool for the recovery of population genomes from related metagenomes," PeerJ, vol. 2, p. e603, 2014.
13. Y.-w. Wu and Y. Ye, "A novel abundance-based algorithm for binning metagenomic sequences using l-tuples," in Research in Computational Molecular Biology. Springer, 2010, pp. 535-549.
14. Y. Wang, H. C. Leung, S.-M. Yiu, and F. Y. Chin, "Metacluster 5. 0: A two-round binning approach for metagenomic data for low-abundance species in a noisy sample," Bioiriformatics, vol. 28, no. 18, pp. i356-i362, 2012.
15. Y. Peng, H. C. M. Leung, S. M. Yiu, and F. Y. L. Chin, "Metaidba: A de novo assembler for metagenomic data," Bioiriformatics, vol. 27, no. 13, pp. i94-il0l, 2011. [Online]. Available: http: //bi oin formati cs. 0 x ford journals. org/content/2 7 /13/i 94. Abstract
16. Y. W. Anf Henry Chi Ming Leung, S. M. Yiu, and F. Y. L. Chin, "Metacluster-ta: Taxonomic annotation for metagenomic data based on assembly-assisted binning," BMC Genomics, vol. 15, no. Suppll, p. S12, 2014.
17. M. Albertsen, P. Hugenholtz, A. Skarshewski, K. L. Nielsen, G. W. Tyson, and P. H. Nielsen, "Genome sequences of rare, uncultured bacteria obtained by differential coverage binning of multiple metagenomes," Nature Biotechnology, vol. 31, no. 6, pp. 533-538, 2013.
18. C. C. Laczny, N. Pinel, N. V lassis, and P. Wilmes, "Alignment-free visualization of metagenomic data by non linear dimension reduction," Scientific reports, vol. 4, 2014.
19. L. Van der Maaten and G. Hinton, "Visualizing data using t-sne," Journal of Machine Learning Research, vol. 9, no. 2579-2605, p. 85, 2008.
20. L. Vendramin, R. J. G. B. Campello, and E. R. Hruschka, "Relative clustering validity criteria: A comparative overview," Statistical Analysis and Data Mining, vol. 3, no. 4, pp. 209-235, 2010. [Online]. Available: http://dx. doi. org/IO. 1002/sam. 10080
21. A. K. Jain, "Data clustering: 50 years beyond k-means," Pattern Recognition Letters, vol. 31, no. 8, pp. 651-666,2010.
22. L. van der Maaten, "Barnes-hut-SNE," arXiv preprint arXiv:1301. 3342, 2013. [Online]. Available: http://arxiv. org/abs/1301. 3342
23. A. Gisbrecht, B. Hammer, B. Mokbel, and A. Sczyrba, "Nonlinear dimensionality reduction for cluster identification in metagenomic samples," in Information Visualisation (IV), 2013 17th International Coriference. IEEE, 2013, pp. 174-179.
24. H. Teeling, A. Meyerdierks, M. Bauer, R. Amann, and F. O. Gliickner, "Application of tetranucleotide frequencies for the assignment of genomic fragments," Environmental microbiology, vol. 6, no. 9, pp. 938-947,2004.
25. F. Gori, D. Mavroedis, M. S. Jetten, and E. Marchiori, "Genomic signatures for metagenomic data analysis: Exploiting the reverse complementarity of tetranucleotides," in Systems Biology (IS B), 2011 IEEE International Coriference on. IEEE, 2011, pp. 149-154.
26. V. Estivill-Castro, "Why so many clustering algorithms: A position paper," ACM SIGKDD Explorations Newsletter, vol. 4, no. I, pp. 65-75, 2002.
27. J. A. Hartigan, "Clustering algorithms," 1975.
28. D. Arthur and S. Vassilvitskii, "k-means++: The advantages of careful seeding," in P roceedings of the eighteenth annual ACM-SIAM symposium on Discrete algorithms. Society for Industrial and Applied Mathematics, 2007, pp. 1027-1035.
29. T. Martinetz, K. Schulten et al., A" neural-gas" network learns topologies. University of Illinois at Urbana-Champaign, 1991.
30. M. Ester,H.-P. Kriegel,J Sander, and X. Xu," A density-based algorithm for discovering clusters in large spatial databases with noise. " in Kdd, vol. 96, no. 34, 1996, pp. 226-231.
31. U. Von Luxburg, "A tutorial on spectral clustering," Statistics and computing, vol. 17, no. 4, pp. 395-416, 2007.
32. Y. Liu, Z. Li, H. Xiong, X. Gao, and J. Wu, "Understanding of internal clustering validation measures," in Data Mining (ICDM), 2010 IEEE 10th International Coriference on. IEEE, 2010, pp. 911-916.
33. R. Tibshirani, G. Walther, and T. Hastie, "Estimating the number of clusters in a data set via the gap statistic," Journal of the Royal Statistical Society: Series B (Statistical Methodology), vol. 63, no. 2, pp. 411-423, 2001.
34. A. Ben-Hur, A. Elisseeff, and I. Guyon, "A stability based method for discovering structure in clustered data," in Pacific symposium on biocomputing, vol. 7, 2001, pp. 6-17.
35. L. Y. Geer,A Marchler-Bauer,R C. Geer,L Han,J He,S He,C Liu, W. Shi, and S. H. Bryant, "The ncbi biosystems database," Nucleic acids research, p. gkp858, 2009.
36. H. B. Mann and D. R. Whitney, "On a test of whether one of two random variables is stochastically larger than the other," The annals of mathematical statistics, pp. 50-60, 1947.
37. A. McHardy, T. Rattei, and A. Sczyrba. (2015) Critical assessment of metagenomic interpretation. [Online]. Available: http://cami-challenge. orgl