A novel semi-supervised learning approach to analyzing metagenomic reads
Pages 629 - 630
Abstract
A novel semi-supervised learning approach is proposed to partition metagenomic reads by combining supervised learning method and unsupervised learning method. In this paper, corresponding analytic methods and dedicated simulating studies were conducted within the scope of a controlled framework emphasizing performance improvement, especially when reads from similar species are applied. According to the experimentation and the learning deliverables, the proposed method outperforms the existing algorithms in the case that the reads partitioning has a considerable number of similar species.
References
[1]
S. F. Altschul, W. Gish, W. Miller, E. W. Myers, and D. J. Lipman. Basic local alignment search tool. Journal of molecular biology, 215(3):403--410, 1990.
[2]
A. Brady and S. L. Salzberg. Phymm and phymmbl: metagenomic phylogenetic classification with interpolated markov models. Nature methods, 6(9):673--676, 2009.
[3]
K. Chen and L. Pachter. Bioinformatics for whole-genome shotgun sequencing of microbial communities. PLoS computational biology, 1(2):e24, 2005.
[4]
W. Gerlach, S. Jünemann, F. Tille, A. Goesmann, and J. Stoye. Webcarma: a web application for the functional and taxonomic classification of unassembled metagenomic reads. Bmc Bioinformatics, 10(1):430, 2009.
[5]
J. Handelsman, M. R. Rondon, S. F. Brady, J. Clardy, and R. M. Goodman. Molecular biological access to the chemistry of unknown soil microbes: a new frontier for natural products. Chemistry & biology, 5(10):R245--R249, 1998.
[6]
D. H. Huson, A. F. Auch, J. Qi, and S. C. Schuster. Megan analysis of metagenomic data. Genome Research, 17(3):377--386, 2007.
[7]
A. Kislyuk, S. Bhatnagar, J. Dushoff, and J. S. Weitz. Unsupervised statistical clustering of environmental shotgun sequences. BMC bioinformatics, 10(1):316, 2009.
[8]
T. C. Nguyen and D. Zhu. Markovbin: An algorithm to cluster metagenomic reads using a mixture modeling of hierarchical distributions. In Proceedings of the International Conference on Bioinformatics, Computational Biology and Biomedical Informatics, page 115. ACM, 2013.
[9]
D. C. Richter, F. Ott, A. F. Auch, R. Schmid, and D. H. Huson. Metasim---a sequencing simulator for genomics and metagenomics. PloS one, 3(10):e3373, 2008.
[10]
H. Teeling, J. Waldmann, T. Lombardot, M. Bauer, and F. O. Glöckner. Tetra: a web-service and a stand-alone program for the analysis and comparison of tetranucleotide usage patterns in dna sequences. BMC bioinformatics, 5(1):163, 2004.
[11]
Y. Wang, H. C. Leung, S.-M. Yiu, and F. Y. Chin. Metacluster 4.0: a novel binning algorithm for ngs reads and huge number of species. Journal of Computational Biology, 19(2):241--249, 2012.
[12]
Y.-W. Wu and Y. Ye. A novel abundance-based algorithm for binning metagenomic sequences using l-tuples. In Research in Computational Molecular Biology, pages 535--549. Springer, 2010.
[13]
B. Yang, Y. Peng, H. Leung, S.-M. Yiu, J. Qin, R. Li, and F. Y. Chin. Metacluster: unsupervised binning of environmental genomic fragments and taxonomic annotation. In Proceedings of the First ACM International Conference on Bioinformatics and Computational Biology, pages 170--179. ACM, 2010.
Index Terms
- A novel semi-supervised learning approach to analyzing metagenomic reads
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Published: 20 September 2014
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