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International Journal of Genomics
Volume 2015, Article ID 197895, 6 pages
Research Article

A New Binning Method for Metagenomics by One-Dimensional Cellular Automata

1Masters Program in Biomedical Informatics and Biomedical Engineering, Feng Chia University, No. 100, Wenhwa Road, Seatwen, Taichung 40724, Taiwan
2Department of Applied Mathematics, Feng Chia University, No. 100, Wenhwa Road, Seatwen, Taichung 40724, Taiwan

Received 7 January 2015; Accepted 9 February 2015

Academic Editor: Hai Jiang

Copyright © 2015 Ying-Chih Lin. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


More and more developed and inexpensive next-generation sequencing (NGS) technologies allow us to extract vast sequence data from a sample containing multiple species. Characterizing the taxonomic diversity for the planet-size data plays an important role in the metagenomic studies, while a crucial step for doing the study is the binning process to group sequence reads from similar species or taxonomic classes. The metagenomic binning remains a challenge work because of not only the various read noises but also the tremendous data volume. In this work, we propose an unsupervised binning method for NGS reads based on the one-dimensional cellular automaton (1D-CA). Our binning method facilities to reduce the memory usage because 1D-CA costs only linear space. Experiments on synthetic dataset exhibit that our method is helpful to identify species of lower abundance compared to the proposed tool.