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  • Title: Unsupervised Binning of Metagenomic Assembled Contigs Using Improved Fuzzy C-Means Method.
    Author: Liu Y, Hou T, Kang B, Liu F.
    Journal: IEEE/ACM Trans Comput Biol Bioinform; 2017; 14(6):1459-1467. PubMed ID: 27295684.
    Abstract:
    Metagenomic contigs binning is a necessary step of metagenome analysis. After assembly, the number of contigs belonging to different genomes is usually unequal. So a metagenomic contigs dataset is a kind of imbalanced dataset and traditional fuzzy c-means method (FCM) fails to handle it very well. In this paper, we will introduce an improved version of fuzzy c-means method (IFCM) into metagenomic contigs binning. First, tetranucleotide frequencies are calculated for every contig. Second, the number of bins is roughly estimated by the distribution of genome lengths of a complete set of non-draft sequenced microbial genomes from NCBI. Then, IFCM is used to cluster DNA contigs with the estimated result. Finally, a clustering validity function is utilized to determine the binning result. We tested this method on a synthetic and two real datasets and experimental results have showed the effectiveness of this method compared with other tools.
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