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Mathematical relations between codon bias indexes and their applications
Authors:Jun Wang  Yi Zhang
Institution:(1) Department of Applied Mathematics, Dalian University of Technology, Dalian, 116024, P.R. China;(2) College of Advanced Science and Technology, Dalian University of Technology, Dalian, 116024, P.R. China;(3) School of Sciences, Hebei University of Science and Technology, Shijiazhuang, HeBei, 050018, P.R. China
Abstract:Codon Adaptation Index (CAI), Effective Number of Codons $$(\hat{N_{c}})$$ as well as its modifications $$\hat{N_{c}}^*$$ , $$\hat{N_{c}}^{**}$$ can be used to measure gene codon bias. In this article, we prove $$\hat{N_{c}}^{**}$$ is more efficient and unbiased than $$\hat{N_{c}}^*$$ and $$\hat{N_{c}}$$ by revisiting correlations of them with CAI in the level of individual amino acid’s codon bias. Correlations are studied by mathematical expressions rather than statistic methods, because the latter unavoidably depend on the data set used. Additionally, the immediate cause of correlations of $$\hat{N_{i}}$$ with CAI (as well as those of RSCU with CAI) are also described in mathematical language. Perhaps, mathematics provides us a new way to study correlations between biological indexes.
Keywords:Codon Adaptation Index  Effective Number of Codons  Correlation  Expression
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