We consider thousands of endogenous retrovirus detected in the human and mouse genomes, and quantify a large number of genomic landscape features at high resolution around their integration sites and in control regions.We propose to analyze this data employing a recently developed functional inferential procedure and functional logistic regression, with the aim of gaining insights on the effects of genomic landscape features on the integration and fixation of endogenous retroviruses.
Cremona, M. A., Campos-Sánchez, R., Pini, A., Vantini, S., Makova, K. D., Chiaromonte, F., Functional data analysis of “Omics” data: how does the genomic landscape influence integration and fixation of endogenous retroviruses?, in Aneiros, G., Bongiorno, E., Cao, R., Vieu, P. (ed.), Functional Statistics and Related Fields, Springer, Cham 2017: 87- 93. 10.1007/978-3-319-55846-2_12 [http://hdl.handle.net/10807/119607]
Functional data analysis of “Omics” data: how does the genomic landscape influence integration and fixation of endogenous retroviruses?
Pini, Alessia;
2017
Abstract
We consider thousands of endogenous retrovirus detected in the human and mouse genomes, and quantify a large number of genomic landscape features at high resolution around their integration sites and in control regions.We propose to analyze this data employing a recently developed functional inferential procedure and functional logistic regression, with the aim of gaining insights on the effects of genomic landscape features on the integration and fixation of endogenous retroviruses.
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