Introduction

Analysis and comparison of RNA m6A methylation profiles become increasingly important to understand the post-transcriptional regulations of gene expression. However, we found that current m6A profiles in public database were not readily inter-comparable, where heterogeneous profiles from the same experimental report but different cell types showed unwanted high correlations.To remove such laboratory bias, we established m6Acorr, an effective pipeline to correct m6A profiles based on quantile normalization and empirical Bayes batch regression methods. The m6Acorr server could not only efficiently eliminate the potential bias in m6A methylation profiles, but also perform profile-profile comparisons and functional analysis of hyper- (hypo-) methylated genes based on the corrected methylation profiles.