CMStatistics 2018: Start Registration
View Submission - CMStatistics
Title: Using interval-wise testing to investigate high-resolution ``omics'' data at multiple locations and scales Authors:  Marzia Cremona - Université Laval (Canada) [presenting]
Francesca Chiaromonte - The Pennsylvania State University (United States)
Kateryna Makova - The Pennsylvania State University (United States)
Abstract: Interval-Wise Testing (IWT) is a non-parametric inferential procedure for functional data that exploits the ordered nature of measurements to adjust $P$-values and control the interval-wise error rate. We present an extended version of IWT implemented in the R/Bioconductor package IWTomics designed for complex, high-resolution ``Omics'' data. IWTomics allows one to compare several``Omics'' features over groups of genomic regions at multiple locations and scales, and outputs those at which each feature shows significant effects. We describe three collaborative projects in which IWTomics has been successfully employed to ``Omits'' studies. In the first, we analyze the profiles of a large collection of genomic landscape features in human and mouse, to identify features that influence integration and fixation of endogenous retroviruses (ERVs). In a similar framework, the second project investigates the dynamic landscape of long interspersed elements-1 ($L_1$) transposition in the human genome. Finally, the third project investigates how non-canonical 3D-conformations in the genome affect the local speed of DNA polymerization, and how this is associated to sequencing errors and to mutations in living cells.