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Integrating Overlapping Structures and Background Information of Words Significantly Improves Biological Sequence Comparison

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  • Qi Dai
  • Lihua Li
  • Xiaoqing Liu
  • Yuhua Yao
  • Fukun Zhao
  • Michael Zhang

Abstract

Word-based models have achieved promising results in sequence comparison. However, as the important statistical properties of words in biological sequence, how to use the overlapping structures and background information of the words to improve sequence comparison is still a problem. This paper proposed a new statistical method that integrates the overlapping structures and the background information of the words in biological sequences. To assess the effectiveness of this integration for sequence comparison, two sets of evaluation experiments were taken to test the proposed model. The first one, performed via receiver operating curve analysis, is the application of proposed method in discrimination between functionally related regulatory sequences and unrelated sequences, intron and exon. The second experiment is to evaluate the performance of the proposed method with f-measure for clustering Hepatitis E virus genotypes. It was demonstrated that the proposed method integrating the overlapping structures and the background information of words significantly improves biological sequence comparison and outperforms the existing models.

Suggested Citation

  • Qi Dai & Lihua Li & Xiaoqing Liu & Yuhua Yao & Fukun Zhao & Michael Zhang, 2011. "Integrating Overlapping Structures and Background Information of Words Significantly Improves Biological Sequence Comparison," PLOS ONE, Public Library of Science, vol. 6(11), pages 1-10, November.
    • Handle: RePEc:plo:pone00:0026779
    DOI: 10.1371/journal.pone.0026779
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    References listed on IDEAS

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    1. Saurabh Sinha & Xin He, 2007. "MORPH: Probabilistic Alignment Combined with Hidden Markov Models of cis-Regulatory Modules," PLOS Computational Biology, Public Library of Science, vol. 3(11), pages 1-15, November.
    2. Adam A Smith & Aaron Vollrath & Christopher A Bradfield & Mark Craven, 2008. "Similarity Queries for Temporal Toxicogenomic Expression Profiles," PLOS Computational Biology, Public Library of Science, vol. 4(7), pages 1-13, July.
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