Background#R##N#Numerous methods are available to profile several epigenetic marks, providing data with different genome coverage and resolution. Large epigenomic datasets are then generated, and often combined with other high-throughput data, including RNA-seq, ChIP-seq for transcription factors (TFs) binding and DNase-seq experiments. Despite the numerous computational tools covering specific steps in the analysis of large-scale epigenomics data, comprehensive software solutions for their integrative analysis are still missing. Multiple tools must be identified and combined to jointly analyze histone marks, TFs binding and other -omics data together with DNA methylation data, complicating the analysis of these data and their integration with publicly available datasets.
['Kamal Kishore', 'Stefano de Pretis', 'Ryan Lister', 'Marco J. Morelli', 'Valerio Bianchi', 'Bruno Amati', 'Joseph R. Ecker', 'Mattia Pelizzola']
methylPipe and compEpiTools: a suite of R packages for the integrative analysis of epigenomics data