Iterative clustering method for metagenomic sequences

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.