Clustering Metagenome Fragments Using Growing Self Organizing Map

Abstract

Abstract=- The microorganism sarnples taken directly from environment are not easy to assemble because they contains mixtures of microorganism. If sampie cornplexity is very high and comes from highly diverse environment, the difficulty of assembling DNA sequences is increasing since the interspecies chimeras can happen, To avoid this problem, in this research, we proposed binning based on cornposition using unsupcrvised learning. We ernployed trinucleotide and tetranucleotide frequency as Ieatures and GSOM algorithm as clustering method. GSOM was implemented to map featurcs into high dimension feature space. We tested our method using small microbial community dataset. The quality of cluster was evaluated based on the folIowing parameters topographic error. quantization error, and error percentage. The evaluation results show that the best cluster can be obtained using GSOM and tetranucleotide.