A New Binning Method for Metagenomics by One-Dimensional Cellular Automata
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...
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| Main Author: | |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2015-01-01
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| Series: | International Journal of Genomics |
| Online Access: | http://dx.doi.org/10.1155/2015/197895 |
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| Summary: | 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. |
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| ISSN: | 2314-436X 2314-4378 |