epialleler

name: bioconductor-epiallelerdescription: Epialleles are specific DNA methylation patterns that are mitotically and/or meiotically inherited. This package calls and reports cytosine methylation as well as frequencies of hypermethylated epialleles at the level of genomic regions or individual cytosines in next-generation sequencing data using binary alignment map (BAM) files as an input. Among other things, this package can also extract and visualise methylation patterns and assess allele specificity of methylation.when_to_use: Use when: Epialleles are specific DNA methylation patterns that are mitotically and/or meiotically inherited. Not for: Requires R ≥ 4.1 and Bioconductor ≥ 3.16user-invocable: false

epialleler

Workflows

Standard Workflow

library(epialleler)
capture.bam <- system.file("extdata", "capture.bam", package="epialleler")
capture.bed <- system.file("extdata", "capture.bed", package="epialleler")
bam.data <- preprocessBam(capture.bam, targets=capture.bed)
cg.vef.report <- generateCytosineReport(bam.data)

Note: Inputs are a short-read BAM file and a target BED file; output is a data.table containing the cytosine VEF report.

Long Read Methylation Analysis

library(epialleler)
longread.bam <- system.file("extdata", "longread.bam", package="epialleler")
longread.data <- preprocessBam(longread.bam, min.mapq=30, min.baseq=20, min.prob=178)
cg.report <- generateCytosineReport(longread.data, threshold.reads=FALSE)

Note: Input is a long-read BAM file; output is a conventional cytosine report.

Methylation Bimodality Ecdf

library(epialleler)
# Assess potential bimodality of methylation for genomic regions of interest
# generateBedEcdf(bam, bed)

Note: Inputs are a BAM file and a BED file of genomic regions; output is an assessment of methylation bimodality.

Methylation Calling Unannotated Bams

library(epialleler)
input.bam <- system.file("extdata", "test", "bwameth-se-unsort-yd.bam", package="epialleler")
output.bam <- tempfile(pattern="output-", fileext=".bam")
genome <- preprocessGenome(system.file("extdata", "test", "reference.fasta.gz", package="epialleler"))
callMethylation(input.bam, output.bam, genome)

Note: Inputs are an unannotated BAM file and a preprocessed reference genome; output is a new BAM file containing XG/XM tags.

Sequence Variant Association

library(epialleler)
# Test the association of methylation with single-nucleotide variations within epialleles
# generateVcfReport(bam, vcf)

Note: Inputs are a BAM file and a VCF file; output is a VCF report testing SNV-epiallele association.

Long Read Methylation Reporting

library(epialleler)
longread.bam <- system.file("extdata", "longread.bam", package="epialleler")
longread.data <- preprocessBam(longread.bam, min.mapq=30, min.baseq=20, min.prob=178)
cg.report <- generateCytosineReport(longread.data, threshold.reads=FALSE)

Note: Input is a long-read BAM file; output is a conventional cytosine report.

Methylation Pattern And Ecdf Visualization

library(epialleler)
# Explore DNA methylation patterns of genomic regions of interest
# patterns <- extractPatterns(bam, bed)
# plotPatterns(patterns)

Note: Inputs are a BAM file and a BED file; output is a visualization of methylation patterns.

Short Read Methylation Calling

library(epialleler)
input.bam <- system.file("extdata", "test", "bwameth-se-unsort-yd.bam", package="epialleler")
output.bam <- tempfile(pattern="output-", fileext=".bam")
genome <- preprocessGenome(system.file("extdata", "test", "reference.fasta.gz", package="epialleler"))
callMethylation(input.bam, output.bam, genome)

Note: Inputs are an unannotated BAM file and a preprocessed reference genome; output is a new BAM file containing XG/XM tags.

Snv Epiallele Association

library(epialleler)
# Test the association of methylation with single-nucleotide variations within epialleles
# generateVcfReport(bam, vcf)

Note: Inputs are a BAM file and a VCF file; output is a VCF report testing SNV-epiallele association.

When to Use

• To call and report cytosine methylation levels (beta values) and variant epiallele frequencies (VEF) of hypermethylated alleles from BAM files.
• To analyze DNA methylation patterns at the level of individual cytosines (generateCytosineReport) or genomic regions (generateBedReport, generateAmpliconReport, generateCaptureReport).
• To call methylation on unannotated short-read BAM files lacking XG/XM tags using callMethylation and a preprocessed reference genome (preprocessGenome).

When NOT to Use

• For long-read sequencing where thresholding is not recommended; use generateMhlReport to calculate Linearised Methylated Haplotype Load (lMHL) instead of VEF.
• For multi-threaded BAM reading/writing if HTSlib is not configured; though nthreads can speed up decompression.

Data Requirements

• BAM files: Short-read BAMs must contain XG and XM tags (or be processed with callMethylation). Long-read BAMs must contain MM and ML tags.
• BED files: Genomic coordinates for targeted regions (amplicon or capture).
• FASTA files: Reference genome sequence (only required for short-read methylation calling via preprocessGenome).

Key Parameters

• bam: Path to the input BAM file or preprocessed BAM data.
• threshold.reads (TRUE): Whether to produce a thresholded (VEF) report. Set to FALSE for conventional cytosine reports.
• filter.reads (TRUE): Whether to drop reads with too few in-context cytosines or high out-of-context methylation.
• min.mapq (0): Minimum mapping quality threshold for reads.
• min.baseq (0): Minimum base quality threshold.
• min.prob (0): Minimum probability threshold for long-read modifications.
• nthreads (1): Number of decompression threads to speed up BAM/FASTA reading.

Best Practices

• Preprocess large BAM files once using preprocessBam and pass the resulting object to downstream reporting functions to save time.
• For paired-end short-read data, ensure the BAM file is sorted by QNAME (unsorted) so that preprocessBam can merge overlapping read pairs correctly.
• Keep filter.reads=TRUE for short-read bisulfite or enzymatic sequencing to remove reads from incompletely converted DNA molecules.

Common Pitfalls

• Running preprocessBam on a location-sorted paired-end BAM: Sort by QNAME first using samtools sort -n.
• Missing XG/XM tags in short-read BAMs: Run callMethylation with a preprocessed reference genome first.

Alternatives

• Bismark or Modkit for general methylation extraction.

References

• Homepage: bioconductor.org/packages/epialleler
• Vignette: https://bioconductor.org/packages/release/bioc/vignettes/epialleler/inst/doc/epialleler.html