Statistical inference for homologous gene pairs between two circular genomes: a new circular–circular regression model

Ashis SenGupta () and Sungsu Kim ()
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Ashis SenGupta: Indian Statistical Institute
Sungsu Kim: Worcester Polytechnic Institute

Statistical Methods & Applications, 2016, vol. 25, issue 3, No 5, 432 pages

Abstract: Abstract In this paper, we investigate the problem of determining the relationship, represented by similarity of the homologous gene configuration, between paired circular genomes using a regression analysis. We propose a new regression model for studying two circular genomes, where the Möbius transformation naturally arises and is taken as the link function, and propose the least circular distance estimation method, as an appropriate method for analyzing circular variables. The main utility of the new regression model is in identification of a new angular location of one of a homologous gene pair between two circular genomes, for various types of possible gene mutations, given that of the other gene. Furthermore, we demonstrate the utility of our new regression model for grouping of various genomes based on closeness of their relationship. Using angular locations of homologous genes from the five pairs of circular genomes (Horimoto et al. in Bioinformatics 14:789–802, 1998), the new model is compared with the existing models.

Keywords: Circular regression; Circular genome; Circular distance (search for similar items in EconPapers)
Date: 2016
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DOI: 10.1007/s10260-015-0341-8

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