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Composition Vector Method Based on Maximum Entropy Principle for Sequence Comparison
January/February 2012 (vol. 9 no. 1)
pp. 79-87
ASCII Text
x 
 
R. H. Chan, T. H. Chan, Hau Man Yeung, R. W. Wang, "Composition Vector Method Based on Maximum Entropy Principle for Sequence Comparison," IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol. 9, no. 1, pp. 79-87, January/February, 2012.
 
 
BibTex
x
 
@article{ 10.1109/TCBB.2011.45,
author = {R. H. Chan and T. H. Chan and Hau Man Yeung and R. W. Wang},
title = {Composition Vector Method Based on Maximum Entropy Principle for Sequence Comparison},
journal ={IEEE/ACM Transactions on Computational Biology and Bioinformatics},
volume = {9},
number = {1},
issn = {1545-5963},
year = {2012},
pages = {79-87},
doi = {http://doi.ieeecomputersociety.org/10.1109/TCBB.2011.45},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
x 
 
TY - JOUR
JO - IEEE/ACM Transactions on Computational Biology and Bioinformatics
TI - Composition Vector Method Based on Maximum Entropy Principle for Sequence Comparison
IS - 1
SN - 1545-5963
SP79
EP87
EPD - 79-87
A1 - R. H. Chan,
A1 - T. H. Chan,
A1 - Hau Man Yeung,
A1 - R. W. Wang,
PY - 2012
KW - probability
KW - macromolecules
KW - maximum entropy methods
KW - molecular biophysics
KW - organic compounds
KW - physiological models
KW - tetrapod 18S rRNA sequences
KW - composition vector method
KW - maximum entropy principle
KW - sequence comparison
KW - multiple sequence alignment methods
KW - probabilistic models
KW - Haos formula
KW - Yus formula
KW - closed-form solution
KW - optimization problem
KW - entropy-maximizing formula
KW - Entropy
KW - Estimation
KW - Optimization
KW - Computational modeling
KW - Bioinformatics
KW - Phylogeny
KW - Strain
KW - phylogenetics.
KW - Composition vector method
KW - maximum entropy principle
KW - optimization model
KW - alignment-free sequence comparison
VL - 9
JA - IEEE/ACM Transactions on Computational Biology and Bioinformatics
ER -
 
R. H. Chan, Dept. of Math., Chinese Univ. of Hong Kong, Hong Kong, China
T. H. Chan, Dept. of Math., Chinese Univ. of Hong Kong, Hong Kong, China
Hau Man Yeung, Dept. of Math., Chinese Univ. of Hong Kong, Hong Kong, China
R. W. Wang, CAS-MPG Partner Inst. for Comput. Biol., Shanghai, China
The composition vector (CV) method is an alignment-free method for sequence comparison. Because of its simplicity when compared with multiple sequence alignment methods, the method has been widely discussed lately; and some formulas based on probabilistic models, like Hao's and Yu's formulas, have been proposed. In this paper, we improve these formulas by using the entropy principle which can quantify the nonrandomness occurrence of patterns in the sequences. More precisely, existing formulas are used to generate a set of possible formulas from which we choose the one that maximizes the entropy. We give the closed-form solution to the resulting optimization problem. Hence, from any given CV formula, we can find the corresponding one that maximizes the entropy. In particular, we show that Hao's formula is itself maximizing the entropy and we derive a new entropy-maximizing formula from Yu's formula. We illustrate the accuracy of our new formula by using both simulated and experimental data sets. For the simulated data sets, our new formula gives the best consensus and significant values for three different kinds of evolution models. For the data set of tetrapod 18S rRNA sequences, our new formula groups the clades of bird and reptile together correctly, where Hao's and Yu's formulas failed. Using real data sets with different sizes, we show that our formula is more accurate than Hao's and Yu's formulas even for small data sets.

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Index Terms:
probability,macromolecules,maximum entropy methods,molecular biophysics,organic compounds,physiological models,tetrapod 18S rRNA sequences,composition vector method,maximum entropy principle,sequence comparison,multiple sequence alignment methods,probabilistic models,Haos formula,Yus formula,closed-form solution,optimization problem,entropy-maximizing formula,Entropy,Estimation,Optimization,Computational modeling,Bioinformatics,Phylogeny,Strain,phylogenetics.,Composition vector method,maximum entropy principle,optimization model,alignment-free sequence comparison
Citation:
R. H. Chan, T. H. Chan, Hau Man Yeung, R. W. Wang, "Composition Vector Method Based on Maximum Entropy Principle for Sequence Comparison," IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol. 9, no. 1, pp. 79-87, Jan.-Feb. 2012, doi:10.1109/TCBB.2011.45
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