Development and application of an integrated allele-specific pipeline for methylomic and epigenomic analysis (MEA)

Julien Richard Albert,Mohammad M Karimi,Richard Thompson,Matthew C Lorincz,Aaron B Bogutz,Tasuku Koike,Hamid Younesy

doi:10.1186/s12864-018-4835-2

Julien Richard Albert, Mohammad M Karimi + Show 5 more

Open Access

https://doi.org/10.1186/s12864-018-4835-2

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Abstract

BackgroundAllele-specific transcriptional regulation, including of imprinted genes, is essential for normal mammalian development. While the regulatory regions controlling imprinted genes are associated with DNA methylation (DNAme) and specific histone modifications, the interplay between transcription and these epigenetic marks at allelic resolution is typically not investigated genome-wide due to a lack of bioinformatic packages that can process and integrate multiple epigenomic datasets with allelic resolution. In addition, existing ad-hoc software only consider SNVs for allele-specific read discovery. This limitation omits potentially informative INDELs, which constitute about one fifth of the number of SNVs in mice, and introduces a systematic reference bias in allele-specific analyses.ResultsHere, we describe MEA, an INDEL-aware Methylomic and Epigenomic Allele-specific analysis pipeline which enables user-friendly data exploration, visualization and interpretation of allelic imbalance. Applying MEA to mouse embryonic datasets yields robust allele-specific DNAme maps and low reference bias. We validate allele-specific DNAme at known differentially methylated regions and show that automated integration of such methylation data with RNA- and ChIP-seq datasets yields an intuitive, multidimensional view of allelic gene regulation. MEA uncovers numerous novel dynamically methylated loci, highlighting the sensitivity of our pipeline. Furthermore, processing and visualization of epigenomic datasets from human brain reveals the expected allele-specific enrichment of H3K27ac and DNAme at imprinted as well as novel monoallelically expressed genes, highlighting MEA’s utility for integrating human datasets of distinct provenance for genome-wide analysis of allelic phenomena.ConclusionsOur novel pipeline for standardized allele-specific processing and visualization of disparate epigenomic and methylomic datasets enables rapid analysis and navigation with allelic resolution. MEA is freely available as a Docker container at https://github.com/julienrichardalbert/MEA.

Highlights

Allele-specific transcriptional regulation, including of imprinted genes, is essential for normal mammalian development
Implementation To generate a harmonized workflow for processing of DNA methylation (DNAme), Ribonucleic acid (RNA)-seq and Chromatin immunoprecipitation (ChIP)-seq datasets, we developed a universal strategy for detecting allele-specific reads
MEA is informative for significantly more CpGs than an Insertion or deletion (INDEL)-agnostic script To test whether the inclusion of INDELs increases the number of informative CpGs for which allelic methylation state can be calculated in practice, we processed raw reads from a previously published whole genome bisulphite sequencing (WGBS) dataset from C57BL/6 J x DBA/2 J mouse F1 inner cell mass (ICM) cells [11]

Summary

Introduction

Allele-specific transcriptional regulation, including of imprinted genes, is essential for normal mammalian development. Richard Albert et al BMC Genomics (2018) 19:463 essentially considering the sequencing reads generated from autosomes (and the X-chromosome in the case of females) as originating from isogenic rather than outbred individuals In merging both parental alleles into a single measurement, these aligners neglect allele-specific phenomena, such as genomic imprinting [1], X-chromosome inactivation [2] and sequence-dependent cis-regulatory effects [3]. Several allele-specific analysis packages rely on reference genome alignment followed by variant calling [8, 10, 14], while others leverage publicly available single nucleotide variant (SNV) data to derive a diploid genome for read alignment [5, 15, 16] This “pseudogenome” strategy is a significant improvement over the former as it enables alignment over loci with high levels of genetic variation. An INDEL-aware allele-specific pipeline that considers both SNVs and INDELs for pseudogenome reconstruction would offer a significant improvement over existing software

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