IMAGE: High-powered detection of genetic effects on DNA methylation using integrated methylation QTL mapping and allele-specific analysis

AbstractIdentifying genetic variants that are associated with methylation variation – an analysis commonly referred to as methylation quantitative trait locus (mQTL) mapping -- is important for understanding the epigenetic mechanisms underlying genotype-trait associations. Here, we develop a statistical method, IMAGE, for mQTL mapping in sequencing-based methylation studies. IMAGE properly accounts for the count nature of bisulfite sequencing data and incorporates allele-specific methylation patterns from heterozygous individuals to enable more powerful mQTL discovery. We compare IMAGE with existing approaches through extensive simulation. We also apply IMAGE to analyze two bisulfite sequencing studies, in which IMAGE identifies more mQTL than existing approaches.

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IMAGE: high-powered detection of genetic effects on DNA methylation using integrated methylation QTL mapping and allele-specific analysis

Genome Biology ◽

10.1186/s13059-019-1813-1 ◽

2019 ◽

Vol 20 (1) ◽

Cited By ~ 3

Author(s):

Yue Fan ◽

Tauras P. Vilgalys ◽

Shiquan Sun ◽

Qinke Peng ◽

Jenny Tung ◽

...

Keyword(s):

Bisulfite Sequencing ◽

Sequencing Data ◽

Specific Analysis ◽

Sequencing Studies ◽

Bisulfite Sequencing Data ◽

Allele Specific ◽

Trait Locus ◽

Allele Specific Methylation ◽

Methylation Patterns ◽

Trait Associations

Abstract Identifying genetic variants that are associated with methylation variation—an analysis commonly referred to as methylation quantitative trait locus (mQTL) mapping—is important for understanding the epigenetic mechanisms underlying genotype-trait associations. Here, we develop a statistical method, IMAGE, for mQTL mapping in sequencing-based methylation studies. IMAGE properly accounts for the count nature of bisulfite sequencing data and incorporates allele-specific methylation patterns from heterozygous individuals to enable more powerful mQTL discovery. We compare IMAGE with existing approaches through extensive simulation. We also apply IMAGE to analyze two bisulfite sequencing studies, in which IMAGE identifies more mQTL than existing approaches.

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CGmapTools improves the precision of heterozygous SNV calls and supports allele-specific methylation detection and visualization in bisulfite-sequencing data

Bioinformatics ◽

10.1093/bioinformatics/btx595 ◽

2017 ◽

Vol 34 (3) ◽

pp. 381-387 ◽

Cited By ~ 39

Author(s):

Weilong Guo ◽

Ping Zhu ◽

Matteo Pellegrini ◽

Michael Q Zhang ◽

Xiangfeng Wang ◽

...

Keyword(s):

Bisulfite Sequencing ◽

Sequencing Data ◽

Bisulfite Sequencing Data ◽

Allele Specific ◽

Allele Specific Methylation

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CpG_MPs: identification of CpG methylation patterns of genomic regions from high-throughput bisulfite sequencing data

Nucleic Acids Research ◽

10.1093/nar/gks829 ◽

2012 ◽

Vol 41 (1) ◽

pp. e4-e4 ◽

Cited By ~ 36

Author(s):

Jianzhong Su ◽

Haidan Yan ◽

Yanjun Wei ◽

Hongbo Liu ◽

Hui Liu ◽

...

Keyword(s):

High Throughput ◽

Bisulfite Sequencing ◽

Cpg Methylation ◽

Sequencing Data ◽

Bisulfite Sequencing Data ◽

Genomic Regions ◽

Methylation Patterns

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Development of a method for identifying and functionally analyzing allele-specific DNA methylation based on BS-seq data

Epigenomics ◽

10.2217/epi-2019-0023 ◽

2019 ◽

Vol 11 (15) ◽

pp. 1679-1692

Author(s):

Jiang Zhu ◽

Mu Su ◽

Yue Gu ◽

Xingda Zhang ◽

Wenhua Lv ◽

...

Keyword(s):

Dna Methylation ◽

High Throughput ◽

Bisulfite Sequencing ◽

The Cancer Genome Atlas ◽

Nucleotide Polymorphisms ◽

Sequencing Data ◽

Genome Bisulfite Sequencing ◽

Bisulfite Sequencing Data ◽

Allele Specific ◽

New Perspective

Aim: To comprehensively identify allele-specific DNA methylation (ASM) at the genome-wide level. Methods: Here, we propose a new method, called GeneASM, to identify ASM using high-throughput bisulfite sequencing data in the absence of haplotype information. Results: A total of 2194 allele-specific DNA methylated genes were identified in the GM12878 lymphocyte lineage using GeneASM. These genes are mainly enriched in cell cytoplasm function, subcellular component movement or cellular linkages. GM12878 methylated DNA immunoprecipitation sequencing, and methylation sensitive restriction enzyme sequencing data were used to evaluate ASM. The relationship between ASM and disease was further analyzed using the The Cancer Genome Atlas (TCGA) data of lung adenocarcinoma (LUAD), and whole genome bisulfite sequencing data. Conclusion: GeneASM, which recognizes ASM by high-throughput bisulfite sequencing and heterozygous single-nucleotide polymorphisms, provides new perspective for studying genomic imprinting.

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Quantitative comparison of within-sample heterogeneity scores for DNA methylation data

Nucleic Acids Research ◽

10.1093/nar/gkaa120 ◽

2020 ◽

Vol 48 (8) ◽

pp. e46-e46 ◽

Cited By ~ 1

Author(s):

Michael Scherer ◽

Almut Nebel ◽

Andre Franke ◽

Jörn Walter ◽

Thomas Lengauer ◽

...

Keyword(s):

Dna Methylation ◽

Bisulfite Sequencing ◽

R Package ◽

Epigenetic Mark ◽

Allele Specific ◽

Technical Bias ◽

Cellular Identity ◽

Allele Specific Methylation ◽

Methylation Patterns ◽

Sample Heterogeneity

Abstract DNA methylation is an epigenetic mark with important regulatory roles in cellular identity and can be quantified at base resolution using bisulfite sequencing. Most studies are limited to the average DNA methylation levels of individual CpGs and thus neglect heterogeneity within the profiled cell populations. To assess this within-sample heterogeneity (WSH) several window-based scores that quantify variability in DNA methylation in sequencing reads have been proposed. We performed the first systematic comparison of four published WSH scores based on simulated and publicly available datasets. Moreover, we propose two new scores and provide guidelines for selecting appropriate scores to address cell-type heterogeneity, cellular contamination and allele-specific methylation. Most of the measures were sensitive in detecting DNA methylation heterogeneity in these scenarios, while we detected differences in susceptibility to technical bias. Using recently published DNA methylation profiles of Ewing sarcoma samples, we show that DNA methylation heterogeneity provides information complementary to the DNA methylation level. WSH scores are powerful tools for estimating variance in DNA methylation patterns and have the potential for detecting novel disease-associated genomic loci not captured by established statistics. We provide an R-package implementing the WSH scores for integration into analysis workflows.

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Bioinformatic Analysis of Methylation Patterns Using Bisulfite Sequencing Data

Methods in Molecular Biology - Insect Genomics ◽

10.1007/978-1-4939-8775-7_12 ◽

2018 ◽

pp. 157-175 ◽

Cited By ~ 1

Author(s):

Jana Asselman

Keyword(s):

Bisulfite Sequencing ◽

Bioinformatic Analysis ◽

Sequencing Data ◽

Bisulfite Sequencing Data ◽

Methylation Patterns

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pWGBSSimla: a profile-based whole-genome bisulfite sequencing data simulator incorporating methylation QTLs, allele-specific methylations and differentially methylated regions

Bioinformatics ◽

10.1093/bioinformatics/btz635 ◽

2019 ◽

Cited By ~ 1

Author(s):

Ren-Hua Chung ◽

Chen-Yu Kang

Keyword(s):

Bisulfite Sequencing ◽

Real Data ◽

Cell Types ◽

Whole Genome ◽

Sequencing Data ◽

Whole Genome Bisulfite Sequencing ◽

Genome Bisulfite Sequencing ◽

Bisulfite Sequencing Data ◽

Allele Specific ◽

Different Cell Types

Abstract Motivation DNA methylation plays an important role in regulating gene expression. DNA methylation is commonly analyzed using bisulfite sequencing (BS-seq)-based designs, such as whole-genome bisulfite sequencing (WGBS), reduced representation bisulfite sequencing (RRBS) and oxidative bisulfite sequencing (oxBS-seq). Furthermore, there has been growing interest in investigating the roles that genetic variants play in changing the methylation levels (i.e. methylation quantitative trait loci or meQTLs), how methylation regulates the imprinting of gene expression (i.e. allele-specific methylation or ASM) and the differentially methylated regions (DMRs) among different cell types. However, none of the current simulation tools can generate different BS-seq data types (e.g. WGBS, RRBS and oxBS-seq) while modeling meQTLs, ASM and DMRs. Results We developed profile-based whole-genome bisulfite sequencing data simulator (pWGBSSimla), a profile-based bisulfite sequencing data simulator, which simulates WGBS, RRBS and oxBS-seq data for different cell types based on real data. meQTLs and ASM are modeled based on the block structures of the methylation status at CpGs, whereas the simulation of DMRs is based on observations of methylation rates in real data. We demonstrated that pWGBSSimla adequately simulates data and allows performance comparisons among different methylation analysis methods. Availability and implementation pWGBSSimla is available at https://omicssimla.sourceforge.io. Supplementary information Supplementary data are available at Bioinformatics online.

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BiSulfite Bolt: A bisulfite sequencing analysis platform

GigaScience ◽

10.1093/gigascience/giab033 ◽

2021 ◽

Vol 10 (5) ◽

Author(s):

Colin Farrell ◽

Michael Thompson ◽

Anela Tosevska ◽

Adewale Oyetunde ◽

Matteo Pellegrini

Keyword(s):

Data Aggregation ◽

Bisulfite Sequencing ◽

Low Complexity ◽

Sequencing Analysis ◽

Command Line ◽

Sequencing Data ◽

Bisulfite Sequencing Data ◽

Analysis Platform ◽

Python Package ◽

Bisulfite Sequencing Analysis

Abstract Background Bisulfite sequencing is commonly used to measure DNA methylation. Processing bisulfite sequencing data is often challenging owing to the computational demands of mapping a low-complexity, asymmetrical library and the lack of a unified processing toolset to produce an analysis-ready methylation matrix from read alignments. To address these shortcomings, we have developed BiSulfite Bolt (BSBolt), a fast and scalable bisulfite sequencing analysis platform. BSBolt performs a pre-alignment sequencing read assessment step to improve efficiency when handling asymmetrical bisulfite sequencing libraries. Findings We evaluated BSBolt against simulated and real bisulfite sequencing libraries. We found that BSBolt provides accurate and fast bisulfite sequencing alignments and methylation calls. We also compared BSBolt to several existing bisulfite alignment tools and found BSBolt outperforms Bismark, BSSeeker2, BISCUIT, and BWA-Meth based on alignment accuracy and methylation calling accuracy. Conclusion BSBolt offers streamlined processing of bisulfite sequencing data through an integrated toolset that offers support for simulation, alignment, methylation calling, and data aggregation. BSBolt is implemented as a Python package and command line utility for flexibility when building informatics pipelines. BSBolt is available at https://github.com/NuttyLogic/BSBolt under an MIT license.

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