Unsupervised Binning of Metagenomic Assembled Contigs Using Improved Fuzzy C-Means Method.
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.
Yun
Y
Liu
Tao
T
Hou
Bing
B
Kang
Fu
F
Liu
D000465:Algorithms; D016000:Cluster Analysis; D017143:Fuzzy Logic; D000069196:Gastrointestinal Microbiome / Q000235:genetics; D006801:Humans; D054892:Metagenome / Q000235:genetics*; D056186:Metagenomics / Q000379:methods*; D017422:Sequence Analysis, DNA / Q000379:methods*; D000069558:Unsupervised Machine Learning