RESUMEN
Genome-scale studies of the bacterial regulatory network have been leveraged by declining sequencing cost and advances in ChIP (chromatin immunoprecipitation) methods. Of which, ChIP-exo has proven competent with its near-single base-pair resolution. While several algorithms and programs have been developed for different analytical steps in ChIP-exo data processing, there is a lack of effort in incorporating them into a convenient bioinformatics pipeline that is intuitive and publicly available. In this paper, we developed ChIP-exo Analysis Pipeline (ChEAP) that executes the one-step process, starting from trimming and aligning raw sequencing reads to visualization of ChIP-exo results. The pipeline was implemented on the interactive web-based Python development environment - Jupyter Notebook, which is compatible with the Google Colab cloud platform to facilitate the sharing of codes and collaboration among researchers. Additionally, users could exploit the free GPU and CPU resources allocated by Colab to carry out computing tasks regardless of the performance of their local machines. The utility of ChEAP was demonstrated with the ChIP-exo datasets of RpoN sigma factor in E. coli K-12 MG1655. To analyze two raw data files, ChEAP runtime was 2 min and 25 s. Subsequent analyses identified 113 RpoN binding sites showing a conserved RpoN binding pattern in the motif search. ChEAP application in ChIP-exo data analysis is extensive and flexible for the parallel processing of data from various organisms.