Abstract
The advances in next-generation sequencing technologies allow researchers to sequence in parallel millions of microbial organisms directly from environmental samples. The result of this “shotgun” sequencing are many short DNA fragments of different organisms, which constitute the basis for the field of metagenomics. Although there are big databases with known microbial DNA that allow us classify some fragments, these databases only represent around 1% of all the species existing in the entire world. For this reason, it is important to use unsupervised methods to group the fragments with the same taxonomic levels. In this paper we focus on the binning step in metagenomics in an unsupervised way. We propose a consensus clustering method based on an iterative clustering process using different lengths of sequences in the databases and a mixture of distance as approach to finding the consensus clustering. The final performance clustering is evaluated according with the purity of clusters. The results achieved by the proposed method outperforms results obtained by simple methods and iterative methods.
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Bonet, I., Escobar, A., Mesa-Múnera, A., Alzate, J.F. (2017). Consensus Clustering for Binning Metagenome Sequences. In: Pichardo-Lagunas, O., Miranda-Jiménez, S. (eds) Advances in Soft Computing. MICAI 2016. Lecture Notes in Computer Science(), vol 10062. Springer, Cham. https://doi.org/10.1007/978-3-319-62428-0_23
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DOI: https://doi.org/10.1007/978-3-319-62428-0_23
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