Blind clustering of DNA fragments based on Kullback-Leibler divergence

Abstract

In whole genome shotgun sequencing when DNA fragments are derived from thousands of microorganisms in the environment sample, traditional alignment methods are impractical to use because of their high computation complexity. In this paper, we take the divergence vector which is consist of Kullback-Leibler divergences of different word lengths as the feature vector. Based on this, we use BP neural network to identify whether two fragments are from the same microorganism and obtain the similarity between fragments. Finally, we develop a new novel method to cluster DNA fragments from different microorganisms into different groups. Experiments show that it performs well. © Springer-Verlag Berlin Heidelberg 2005.