Abstract
Metagenomics studies microbial DNA of environmental samples. The sequencing tools produce a set of genome fragments providing a challenge for metagenomics to associate them with the corresponding phylogenetic group. To solve this problem there are binning methods, which are classified into two sequencing categories: similarity and composition. This paper proposes an iterative clustering method, which aim at achieving a low sensitivity of clusters. The approach consists of iteratively run k-means reducing the training data in each step. Selection of data for next iteration depends on the result obtained in the previous, which is based on the compactness measure. The final performance clustering is evaluated according with the sensitivity of clusters. The results demonstrate that proposed model is better than the simple k-means for metagenome databases.
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References
Council, N.R.: The New Science of Metagenomics: Revealing the Secrets of Our Microbial Planet. The National Academies Press (2007)
Wu, Y.-W., Ye, Y.: A Novel Abundance-Based Algorithm for Binning Metagenomic Sequences Using l-Tuples. In: Berger, B. (ed.) RECOMB 2010. LNCS, vol. 6044, pp. 535–549. Springer, Heidelberg (2010)
Reddy, R.M., Mohammed, M.H., Mande, S.S.: MetaCAA: A clustering-aided methodology for efficient assembly of metagenomic datasets. Genomics 103, 161–168 (2014)
Camacho, C., Coulouris, G., Avagyan, V., Ma, N., Papadopoulos, J., Bealer, K., Madden, T.: BLAST+: architecture and applications. BMC Bioinformatics 10, 421 (2009)
McHardy, A.C., Martin, H.G., Tsirigos, A., Hugenholtz, P., Rigoutsos, I.: Accurate phylogenetic classification of variable-length DNA fragments. Nat. Meth. 4, 63–72 (2007)
Huson, D.H., Auch, A.F., Qi, J., Schuster, S.C.: MEGAN analysis of metagenomic data. Genome Research 17, 377–386 (2007)
Chan, C.-K., Hsu, A., Halgamuge, S., Tang, S.-L.: Binning sequences using very sparse labels within a metagenome. BMC Bioinformatics 9, 215 (2008)
Teeling, H., Waldmann, J., Lombardot, T., Bauer, M., Glockner, F.: TETRA: a web-service and a stand-alone program for the analysis and comparison of tetranucleotide usage patterns in DNA sequences. BMC Bioinformatics 5, 163 (2004)
Abe, T., Kanaya, S., Kinouchi, M., Ichiba, Y., Kozuki, T., Ikemura, T.: Informatics for Unveiling Hidden Genome Signatures. Genome Research 13, 693–702 (2003)
Li, W., Fu, L., Niu, B., Wu, S., Wooley, J.: Ultrafast clustering algorithms for metagenomic sequence analysis. Briefings in Bioinformatics 13, 656–668 (2012)
Kunin, V., Copeland, A., Lapidus, A., Mavromatis, K., Hugenholtz, P.: A Bioinformatician’s Guide to Metagenomics. Microbiology and Molecular Biology Reviews 72, 557–578 (2008)
MacQueen, J.: Some methods for classification and analysis of multivariate observations. In: Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability. Statistics, vol. 1, pp. 281–297. University of California Press, Berkeley (1967)
Witten, I., Frank, E.: Data Mining: Practical Machine Learning Tools and Techniques. Morgan Kaufmann, San Francisco (2005)
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Bonet, I., Montoya, W., Mesa-Múnera, A., Alzate, J.F. (2014). Iterative Clustering Method for Metagenomic Sequences. In: Prasath, R., O’Reilly, P., Kathirvalavakumar, T. (eds) Mining Intelligence and Knowledge Exploration. Lecture Notes in Computer Science(), vol 8891. Springer, Cham. https://doi.org/10.1007/978-3-319-13817-6_15
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DOI: https://doi.org/10.1007/978-3-319-13817-6_15
Publisher Name: Springer, Cham
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