Fast, epiallele-aware methylation
caller and reporter

Introduction

epialleleR is an R package for calling and reporting cytosine methylation and hypermethylated variant epiallele frequencies (VEF) at the level of genomic regions or individual cytosines in next-generation sequencing data using binary alignment map (BAM) files as an input. See below for additional functionality.

Current Features

• calling cytosine methylation and saving calls in BAM file (callMethylation)
• creating sample BAM files given mandatory and optional BAM fields (simulateBam)
• conventional reporting of cytosine methylation (generateCytosineReport)
• reporting the hypermethylated variant epiallele frequency (VEF) at the level of genomic regions (generate[Bed|Amplicon|Capture]Report) or individual cytosines (generateCytosineReport)
• reporting linearised Methylated Haplotype Load (lMHL, generateMhlReport)
• extracting methylation patterns for genomic region of interest (extractPatterns)
• visualising methylation patterns (plotPatterns)
• testing for the association between epiallele methylation status and sequence variations (generateVcfReport)
• assessing the distribution of per-read beta values for genomic regions of interest (generateBedEcdf)

Recent improvements

v1.14 [BioC 3.20]

• creates pretty plots of methylation patterns

v1.12 [BioC 3.19]

• inputs long-read sequencing alignments
• full support for short-read sequencing alignments by Illumina DRAGEN, Bismark, bwa-meth, BSMAP
• RRBS-specific options
• lower memory usage

v1.10 [BioC 3.18]

• inputs both single-end and paired-end sequencing alignments
• makes and stores methylation calls
• creates sample BAM files
• reports linearised MHL

v1.4 [BioC 3.15]

• significant speed-up
• method to extract and visualize methylation patterns

v1.2 [BioC 3.14]

• even faster and more memory-efficient BAM loading (by means of HTSlib)
• min.baseq parameter to reduce the effect of low quality bases on methylation or SNV calling (in v1.0 the output of generateVcfReport was equivalent to the one of samtools mpileup -Q 0 ...)

check out NEWS for more!

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Installation

install via Bioconductor

```r if (!requireNamespace("BiocManager", quietly = TRUE)) install.packages("BiocManager")

BiocManager::install("epialleleR") ```

Install the latest version via install_github

r library(devtools) install_github("BBCG/epialleleR", build_vignettes=FALSE, repos=BiocManager::repositories(), dependencies=TRUE, type="source")

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Using the package

Please read epialleleR vignette at GitHub pages or within the R environment: vignette("epialleleR", package="epialleleR"), or consult the function's help pages for the extensive information on usage, parameters and output values.

Comparison of beta, VEF and lMHL values for various use cases is given by the values vignette (vignette("values", package="epialleleR"))

Very brief synopsis:

```r library(epialleleR)

make methylation calls if necessary

callMethylation( input.bam.file=system.file("extdata", "test", "dragen-se-unsort-xg.bam", package="epialleleR"), output.bam.file=tempfile(pattern="output-", fileext=".bam"), genome=system.file("extdata", "test", "reference.fasta.gz", package="epialleleR") )

make a sample BAM file from scratch

simulateBam(output.bam.file=tempfile(pattern="simulated-", fileext=".bam"), pos=c(1, 2), XM=c("ZZZzzZZZ", "ZZzzzzZZ"), XG=c("CT", "AG"))

or use external files

amplicon.bam <- system.file("extdata", "amplicon010meth.bam", package="epialleleR") amplicon.bed <- system.file("extdata", "amplicon.bed", package="epialleleR") amplicon.vcf <- system.file("extdata", "amplicon.vcf.gz", package="epialleleR")

preload the data

bam.data <- preprocessBam(amplicon.bam)

methylation patterns and their plot

patterns <- extractPatterns(bam=amplicon.bam, bed=amplicon.bed, bed.row=3) plotPatterns(patterns)

CpG VEF report for individual bases

cg.vef.report <- generateCytosineReport(bam.data)

BED-guided VEF report for genomic ranges

bed.report <- generateBedReport(bam=amplicon.bam, bed=amplicon.bed, bed.type="capture")

VCF report

vcf.report <- generateVcfReport(bam=amplicon.bam, bed=amplicon.bed, vcf=amplicon.vcf, vcf.style="NCBI")

lMHL report

mhl.report <- generateMhlReport(bam=amplicon.bam) ```

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Citing the epialleleR package

Oleksii Nikolaienko, Per Eystein Lønning, Stian Knappskog, epialleleR: an R/Bioconductor package for sensitive allele-specific methylation analysis in NGS data. GigaScience, Volume 12, 2023, giad087, https://doi.org/10.1093/gigascience/giad087. Data: GSE201690

Our experimental studies that use the package

Per Eystein Lonning, Oleksii Nikolaienko, Kathy Pan, Allison W. Kurian, Hans Petter Petter Eikesdal, Mary Pettinger, Garnet L Anderson, Ross L Prentice, Rowan T. Chlebowski, and Stian Knappskog. Constitutional BRCA1 methylation and risk of incident triple-negative breast cancer and high-grade serous ovarian cancer. JAMA Oncology 2022. https://doi.org/10.1001/jamaoncol.2022.3846

Oleksii Nikolaienko, Hans P. Eikesdal, Elisabet Ognedal, Bjørnar Gilje, Steinar Lundgren, Egil S. Blix, Helge Espelid, Jürgen Geisler, Stephanie Geisler, Emiel A.M. Janssen, Synnøve Yndestad, Laura Minsaas, Beryl Leirvaag, Reidun Lillestøl, Stian Knappskog, Per E. Lønning. Prenatal BRCA1 epimutations contribute significantly to triple-negative breast cancer development. Genome Medicine 2023. https://doi.org/10.1186/s13073-023-01262-8. Data: GSE243966

Oleksii Nikolaienko, Garnet L Anderson, Rowan T Chlebowski, Su Yon Jung, Holly R Harris, Stian Knappskog, and Per E Lønning. MGMT epimutations and risk of incident cancer of the colon, glioblastoma multiforme, and diffuse large B-cell lymphomas. Clinical Epigenetics 2025. https://doi.org/10.1186/s13148-025-01835-x

epialleleR at Bioconductor

release, development version

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License

Artistic License 2.0