TY - JOUR
T1 - An improved string composition method for sequence comparison
AU - Lu, Guoqing
AU - Zhang, Shunpu
AU - Fang, Xiang
N1 - Funding Information:
This publication was made possible by NSF Grant Number NSF DEB-0732969 and NIH Grant Number NIH 1R01LM009219-01A1. XF is grateful to the Dean's Office in the College of Arts and Science at the University of Nebraska at Omaha for providing a Graduate Assistantship. GL acknowledges the UCR Award from the University of Nebraska at Omaha. We are grateful to the WHO/OIE/FAO H5N1 Evolution Working Group for sharing the sequence data and the clade designation information. We also want to thank Mary Christman for proofreading the final manuscript.
PY - 2008/5/28
Y1 - 2008/5/28
N2 - Background: Historically, two categories of computational algorithms (alignment-based and alignment-free) have been applied to sequence comparison-one of the most fundamental issues in bioinformatics. Multiple sequence alignment, although dominantly used by biologists, possesses both fundamental as well as computational limitations. Consequently, alignment-free methods have been explored as important alternatives in estimating sequence similarity. Of the alignment-free methods, the string composition vector (CV) methods, which use the frequencies of nucleotide or amino acid strings to represent sequence information, show promising results in genome sequence comparison of prokaryotes. The existing CV-based methods, however, suffer certain statistical problems, thereby underestimating the amount of evolutionary information in genetic sequences. Results: We show that the existing string composition based methods have two problems, one related to the Markov model assumption and the other associated with the denominator of the frequency normalization equation. We propose an improved complete composition vector method under the assumption of a uniform and independent model to estimate sequence information contributing to selection for sequence comparison. Phylogenetic analyses using both simulated and experimental data sets demonstrate that our new method is more robust compared with existing counterparts and comparable in robustness with alignment-based methods. Conclusion: We observed two problems existing in the currently used string composition methods and proposed a new robust method for the estimation of evolutionary information of genetic sequences. In addition, we discussed that it might not be necessary to use relatively long strings to build a complete composition vector (CCV), due to the overlapping nature of vector strings with a variable length. We suggested a practical approach for the choice of an optimal string length to construct the CCV.
AB - Background: Historically, two categories of computational algorithms (alignment-based and alignment-free) have been applied to sequence comparison-one of the most fundamental issues in bioinformatics. Multiple sequence alignment, although dominantly used by biologists, possesses both fundamental as well as computational limitations. Consequently, alignment-free methods have been explored as important alternatives in estimating sequence similarity. Of the alignment-free methods, the string composition vector (CV) methods, which use the frequencies of nucleotide or amino acid strings to represent sequence information, show promising results in genome sequence comparison of prokaryotes. The existing CV-based methods, however, suffer certain statistical problems, thereby underestimating the amount of evolutionary information in genetic sequences. Results: We show that the existing string composition based methods have two problems, one related to the Markov model assumption and the other associated with the denominator of the frequency normalization equation. We propose an improved complete composition vector method under the assumption of a uniform and independent model to estimate sequence information contributing to selection for sequence comparison. Phylogenetic analyses using both simulated and experimental data sets demonstrate that our new method is more robust compared with existing counterparts and comparable in robustness with alignment-based methods. Conclusion: We observed two problems existing in the currently used string composition methods and proposed a new robust method for the estimation of evolutionary information of genetic sequences. In addition, we discussed that it might not be necessary to use relatively long strings to build a complete composition vector (CCV), due to the overlapping nature of vector strings with a variable length. We suggested a practical approach for the choice of an optimal string length to construct the CCV.
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U2 - 10.1186/1471-2105-9-S6-S15
DO - 10.1186/1471-2105-9-S6-S15
M3 - Article
C2 - 18541050
AN - SCOPUS:47249127227
SN - 1471-2105
VL - 9
JO - BMC bioinformatics
JF - BMC bioinformatics
IS - SUPPL. 6
M1 - S15
ER -