AIMER: A SNP-independent software for identifying imprinting-like allelic methylated regions from DNA methylome

Abstract

Genomic imprinting is essential for mammalian growth and embryogenesis. High-throughput bisulfite sequencing accompanied with parental haplotype-specific information allows analysis of imprinted genes and imprinting control regions (ICRs) on a large scale. Currently, although several allelic methylated regions (AMRs) detection software were developed, methods for detecting imprinted AMRs is still limited. Here, we developed a SNP-independent statistical approach, AIMER, to detect imprinting-like AMRs. By using the mouse frontal cortex methylome as input, we demonstrated that AIMER performs very well in detecting known germline ICRs compared with other methods. Furthermore, we found the putative parental AMRs AIMER detected could be distinguished from sequence-dependent AMRs. Finally, we found a novel germline imprinting-like AMR using WGBS data from 17 distinct mouse tissue samples. The results indicate that AIMER is a good choice for detecting imprinting-like (parent-of-origin-dependent) AMRs. We hope this method will be helpful for future genomic imprinting studies. The Python source code for our project is now publicly available on both GitHub (https://github.com/ZhaoLab-TMU/AIMER) and Gitee (https://gitee.com/zhaolab_tmu/AIMER).

Keywords

• Differentially methylated region
• Allele-specific methylation
• SNP-independent
• Imprinting-like
• AIMER