AKSmooth: Enhancing low-coverage bisulfite sequencing data via kernel-based smoothing

Whole-genome bisulfite sequencing (WGBS) is an approach of growing importance. It is the only approach that provides a comprehensive picture of the genome-wide DNA methylation profile. However, obtaining a sufficient amount of genome and read coverage typically requires high sequencing costs. Bioinformatics tools can reduce this cost burden by improving the quality of sequencing data. We have developed a statistical method Ajusted Local Kernel Smoother (AKSmooth) that can accurately and efficiently reconstruct the single CpG methylation estimate across the entire methylome using low-coverage bisulfite sequencing (Bi-Seq) data. We demonstrate the AKSmooth performance on the low-coverage (~ 4×) DNA methylation profiles of three human colon cancer samples and matched controls. Under the best set of parameters, AKSmooth-curated data showed high concordance with the gold standard high-coverage sample (Pearson 0.90), outperforming the popular analogous method. In addition, AKSmooth showed computational efficiency with runtime benchmark over 4.5 times better than the reference tool. To summarize, AKSmooth is a simple and efficient tool that can provide an accurate human colon methylome estimation profile from low-coverage WGBS data. The proposed method is implemented in R and is available at https://github.com/Junfang/AKSmooth .

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A Bayesian Approach for Analysis of Whole-Genome Bisulfite Sequencing Data Identifies Disease-Associated Changes in DNA Methylation

Genetics ◽

10.1534/genetics.116.195008 ◽

2017 ◽

Vol 205 (4) ◽

pp. 1443-1458 ◽

Cited By ~ 10

Author(s):

Owen J. L. Rackham ◽

Sarah R. Langley ◽

Thomas Oates ◽

Eleni Vradi ◽

Nathan Harmston ◽

...

Keyword(s):

Dna Methylation ◽

Bayesian Approach ◽

Bisulfite Sequencing ◽

Whole Genome ◽

Sequencing Data ◽

Whole Genome Bisulfite Sequencing ◽

Genome Bisulfite Sequencing ◽

Bisulfite Sequencing Data

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Focal disruption of DNA methylation dynamics at enhancers in IDH-mutant AML cells

Leukemia ◽

10.1038/s41375-021-01476-y ◽

2021 ◽

Author(s):

Elisabeth R. Wilson ◽

Nichole M. Helton ◽

Sharon E. Heath ◽

Robert S. Fulton ◽

Jacqueline E. Payton ◽

...

Keyword(s):

Dna Methylation ◽

Myeloid Leukemia ◽

Bisulfite Sequencing ◽

Sequencing Data ◽

Genome Wide ◽

Cd34 Cells ◽

Recurrent Mutations ◽

Genome Bisulfite Sequencing ◽

Bisulfite Sequencing Data ◽

Acute Myeloid

AbstractRecurrent mutations in IDH1 or IDH2 in acute myeloid leukemia (AML) are associated with increased DNA methylation, but the genome-wide patterns of this hypermethylation phenotype have not been comprehensively studied in AML samples. We analyzed whole-genome bisulfite sequencing data from 15 primary AML samples with IDH1 or IDH2 mutations, which identified ~4000 focal regions that were uniquely hypermethylated in IDHmut samples vs. normal CD34+ cells and other AMLs. These regions had modest hypermethylation in AMLs with biallelic TET2 mutations, and levels of 5-hydroxymethylation that were diminished in IDH and TET-mutant samples, indicating that this hypermethylation results from inhibition of TET-mediated demethylation. Focal hypermethylation in IDHmut AMLs occurred at regions with low methylation in CD34+ cells, implying that DNA methylation and demethylation are active at these loci. AML samples containing IDH and DNMT3AR882 mutations were significantly less hypermethylated, suggesting that IDHmut-associated hypermethylation is mediated by DNMT3A. IDHmut-specific hypermethylation was highly enriched for enhancers that form direct interactions with genes involved in normal hematopoiesis and AML, including MYC and ETV6. These results suggest that focal hypermethylation in IDH-mutant AML occurs by altering the balance between DNA methylation and demethylation, and that disruption of these pathways at enhancers may contribute to AML pathogenesis.

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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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epiG: statistical inference and profiling of DNA methylation from whole-genome bisulfite sequencing data

Genome Biology ◽

10.1186/s13059-017-1168-4 ◽

2017 ◽

Vol 18 (1) ◽

Cited By ~ 4

Author(s):

Martin Vincent ◽

Kamilla Mundbjerg ◽

Jakob Skou Pedersen ◽

Gangning Liang ◽

Peter A. Jones ◽

...

Keyword(s):

Dna Methylation ◽

Statistical Inference ◽

Bisulfite Sequencing ◽

Whole Genome ◽

Sequencing Data ◽

Whole Genome Bisulfite Sequencing ◽

Genome Bisulfite Sequencing ◽

Bisulfite Sequencing Data

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Analyzing whole genome bisulfite sequencing data from highly divergent genotypes

Nucleic Acids Research ◽

10.1093/nar/gkz674 ◽

2019 ◽

Vol 47 (19) ◽

pp. e117-e117 ◽

Cited By ~ 1

Author(s):

Phillip Wulfridge ◽

Ben Langmead ◽

Andrew P Feinberg ◽

Kasper D Hansen

Keyword(s):

Dna Methylation ◽

Genetic Variation ◽

Bisulfite Sequencing ◽

Joint Analysis ◽

Whole Genome ◽

Sequencing Data ◽

Whole Genome Bisulfite Sequencing ◽

Genome Bisulfite Sequencing ◽

Bisulfite Sequencing Data ◽

Personal Genomes

Abstract In the study of DNA methylation, genetic variation between species, strains or individuals can result in CpG sites that are exclusive to a subset of samples, and insertions and deletions can rearrange the spatial distribution of CpGs. How to account for this variation in an analysis of the interplay between sequence variation and DNA methylation is not well understood, especially when the number of CpG differences between samples is large. Here, we use whole-genome bisulfite sequencing data on two highly divergent mouse strains to study this problem. We show that alignment to personal genomes is necessary for valid methylation quantification. We introduce a method for including strain-specific CpGs in differential analysis, and show that this increases power. We apply our method to a human normal-cancer dataset, and show this improves accuracy and power, illustrating the broad applicability of our approach. Our method uses smoothing to impute methylation levels at strain-specific sites, thereby allowing strain-specific CpGs to contribute to the analysis, while accounting for differences in the spatial occurrences of CpGs. Our results have implications for joint analysis of genetic variation and DNA methylation using bisulfite-converted DNA, and unlocks the use of personal genomes for addressing this question.

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Analyzing whole genome bisulfite sequencing data from highly divergent genotypes

10.1101/076844 ◽

2016 ◽

Cited By ~ 2

Author(s):

Phillip Wulfridge ◽

Ben Langmead ◽

Andrew P. Feinberg ◽

Kasper D. Hansen

Keyword(s):

Dna Methylation ◽

Genetic Variation ◽

Bisulfite Sequencing ◽

Inbred Mouse ◽

Mouse Strains ◽

Whole Genome ◽

Sequencing Data ◽

Whole Genome Bisulfite Sequencing ◽

Genome Bisulfite Sequencing ◽

Bisulfite Sequencing Data

AbstractIn the study of DNA methylation, genetic variation between species, strains, or individuals can result in CpG sites that are exclusive to a subset of samples, and insertions and deletions can rearrange the spatial distribution of CpGs. How to account for this variation in an analysis of the interplay between sequence variation and DNA methylation is not well understood, especially when the number of CpG differences between samples is large. Here we use whole-genome bisulfite sequencing data on two highly divergent inbred mouse strains to study this problem. We find that while the large number of strain-specific CpGs necessitates considerations regarding the reference genomes used during alignment, properties such as CpG density are surprisingly conserved across the genome. We introduce a method for including strain-specific CpGs in differential analysis, and show that accounting for strain-specific CpGs increases the power to find differentially methylated regions between the strains. Our method uses smoothing to impute methylation levels at strain-specific sites, thereby allowing strain-specific CpGs to contribute to the analysis, and also allowing us to account for differences in the spatial occurrences of CpGs. Our results have implications for analysis of genetic variation and DNA methylation using bisulfite-converted DNA.

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