Premium
Linear regression model of DNA sequences and its application
Author(s) -
Dai Qi,
Liu XiaoQing,
Wang TianMing,
Vukicevic Damir
Publication year - 2007
Publication title -
journal of computational chemistry
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.907
H-Index - 188
eISSN - 1096-987X
pISSN - 0192-8651
DOI - 10.1002/jcc.20556
Subject(s) - dna , dna sequencing , sequence (biology) , random sequence , mathematics , linear regression , nucleotide , computational biology , computer science , algorithm , distribution (mathematics) , biology , statistics , genetics , gene , mathematical analysis
We constructed six new models to analyze the DNA sequences. First, we regarded a DNA primary sequence as a random process in t and gave three ways to define nucleotides' random distribution functions. We extracted some parameters from the linear model and analyzed the changes of the nucleotides' distributions. In order to facilitate the comparison of DNA sequences, we proposed two ways to measure their similarities. Finally, we compared the six models by analyzing the similarities of the DNA primary sequences presented in Table 1 and selected the optimal one. © 2007 Wiley Periodicals, Inc. J Comput Chem, 2007