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ImprintCapASM

Overview

ImprintCapASM is an R package for SNP-phased allele-specific methylation (ASM) analysis across the 41 known human imprinted differentially methylated regions (DMRs). It is designed for clinical diagnostic workflows that profile imprint disorder cases — including Beckwith-Wiedemann syndrome (BWS), Silver-Russell syndrome (SRS), Prader-Willi syndrome (PWS), Angelman syndrome (AS), and related conditions — from bisulfite sequencing data produced by targeted capture panels.

The package provides three core functions that form a sequential pipeline:

1. prepare_cpg_snp_input() — Links CpG methylation values to nearby heterozygous SNPs; produces a per-sample Excel table and a BED file
2. extract_bam_regions() — Extracts and sorts a BAM subset covering the SNP windows for each sample
3. ASM() — Reads the extracted BAM, assigns each read to a parental allele, and computes allele-specific methylation statistics; returns three output tables and a line-plot PDF

For processing multiple samples together — the standard diagnostic use case — use run_pipeline(), which runs the full three-step pipeline for all control samples as a batch, and separately for all patient samples as a batch. Controls and patients are always run independently using their respective filter_cpgs reference files.

Background

Genomic imprinting is an epigenetic phenomenon whereby a subset of genes are expressed in a parent-of-origin dependent manner, regulated by differentially methylated regions (DMRs). Loss or gain of methylation at these DMRs underlies a class of rare congenital disorders collectively known as imprinting disorders. Accurate diagnosis requires quantifying the methylation of each parental allele separately — a task that standard bisulfite sequencing alone cannot achieve without phasing methylation data to nearby heterozygous SNPs.

ImprintCapASM implements a SNP-phasing strategy: heterozygous SNPs detected in bisulfite sequencing reads are used to assign each read to a parental allele (REF or ALT), and CpG methylation values on each allele are computed and compared. Deviation from the expected allele-specific methylation pattern at a given DMR indicates a potential imprinting disorder.

Installation

From CRAN (stable release)

install.packages("ImprintCapASM")

From GitHub (development version)

# install.packages("remotes")
remotes::install_github("19-saha/ImprintCapASM")

Bioconductor dependencies

ImprintCapASM depends on several Bioconductor packages. Install them first if not already present:

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

BiocManager::install(c(
    "BiocParallel",
    "Rsamtools",
    "GenomicRanges",
    "IRanges",
    "S4Vectors",
    "SummarizedExperiment",
    "VariantAnnotation"
))

The Two filter_cpgs Reference Files

A key concept in ImprintCapASM is that controls and patients each have their own filter_cpgs reference file. These are not interchangeable:

Both files share the same structure (chr, 5_location, 3_location, DMR, then sample columns). The ASM() function auto-detects sample columns by matching the pattern ^Control_ or ^Patient_ in the column names. Passing the wrong file to the wrong sample_type will produce incorrect variance categories and misleading plots.

Both files are used identically for CpG window definition in prepare_cpg_snp_input() — what differs is the cohort-specific methylation statistics computed during ASM().

Pipeline Overview

Bisulfite sequencing run (targeted imprint capture panel)
        │
        ├── bssnper SNP calling    →  sample.SNPs.out      (VCFv4.3, plain text)
        ├── bssnper CG methylation →  sample.CGmeth.txt    (9-column TSV)
        └── Picard MarkDuplicates  →  sample_markdup.bam + .bai
                │
                ▼  [per sample, run separately for controls and patients]
        ┌──────────────────────────────────────────┐
        │   prepare_cpg_snp_input()                │
        │   Input:  sample.SNPs.out                │  Filters heterozygous SNPs (GT=0/1),
        │           sample.CGmeth.txt              │  overlaps with CpG panel windows,
        │           inst/extdata/filter_cpgs_ctrl.xlsx          │  joins CpG methylation fractions
        │             OR inst/extdata/filter_cpgs_pat.xlsx      │
        │   Output: cpg_snps_CG_{type}_{id}.xlsx   │
        │           cpg_snps_CG_{type}_{id}.bed    │
        └──────────────┬───────────────────────────┘
                       │
        ┌──────────────▼───────────────────────────┐
        │   extract_bam_regions()                  │
        │   Input:  sample_markdup.bam             │  Subsets BAM to SNP windows,
        │           cpg_snps_CG_{type}_{id}.bed    │  sorts and indexes the output
        │   Output: {type}_{id}_wide.bam + .bai    │
        └──────────────┬───────────────────────────┘
                       │
             [all samples of same type combined]
                       │
        ┌──────────────▼───────────────────────────┐
        │   ASM()                                  │
        │   Input:  cpg_snps_CG_{type}_{id}.xlsx   │  Bisulfite-aware allele assignment,
        │           {type}_{id}_wide.bam           │  per-read methylation scoring,
        │           inst/extdata/filter_cpgs_ctrl.xlsx          │  CpG variance classification using
        │             OR inst/extdata/filter_cpgs_pat.xlsx      │  cohort-matched reference
        │   Output: asm_{type}_{id}.xlsx           │
        │           snp_cpg_{type}_{id}.xlsx       │
        │           meth_summary_{type}_{id}.xlsx  │
        │           lineplot_{type}_{id}.pdf       │
        └──────────────────────────────────────────┘

Input File Formats

1. SNP file — sample.SNPs.out (bssnper VCFv4.3)

Produced by BS-Snper. Plain-text VCF — no bgzip or tabix index required. The function reads the GT FORMAT field and retains only heterozygous SNPs (GT == "0/1") with sufficient depth:

#CHROM  POS     ID  REF ALT QUAL  FILTER  INFO             FORMAT                                 SAMPLE
chr11   2016400 .   G   A   85    PASS    DP=28;AD=15,13;  GT:DP:AD:ADF:ADR:BSD:BSQ:ALFR          0/1:28:15,13:...

2. Methylation file — sample.CGmeth.txt (bssnper CG output)

Tab-delimited, 9 columns with a #CHROM header. Watson and Crick strand methylation and coverage are merged internally by the function:

#CHROM  POS       CONTEXT  Watson-METH  Watson-COVERAGE  Watson-QUAL  Crick-METH  Crick-COVERAGE  Crick-QUAL
chr11   2016405   CG       155          169              33           365         494             33

3. CpG panel reference files — inst/extdata/filter_cpgs_ctrl.xlsx and inst/extdata/filter_cpgs_pat.xlsx

Two separate reference Excel files — one for controls, one for patients. Both share the same column structure: genomic coordinates and DMR name, followed by per-sample methylation percentages. The ASM() function detects sample columns automatically by matching ^Control_ or ^Patient_ column name prefixes:

chr    5_location  3_location  DMR        Control_1  Control_2  Control_3  ...
chr11  2016404     2016406     H19/IGF2   82         84         81         ...

chr    5_location  3_location  DMR        Patient_1  Patient_2  Patient_3  ...
chr11  2016404     2016406     H19/IGF2   45         83         80         ...

4. BAM file — sample_markdup.bam + .bam.bai

Duplicate-marked, coordinate-sorted BAM produced by Picard MarkDuplicates. The .bai index must be present alongside the BAM. If the index is missing, extract_bam_regions() creates it automatically via Rsamtools::indexBam().

Recommended Folder Structure

Organise your project with controls and patients in separate folders so that run_pipeline() can glob files cleanly:

project/
├── controls/
│   ├── snps/
│   │   ├── CTRL_01.SNPs.out
│   │   ├── CTRL_02.SNPs.out
│   │   └── ...
│   ├── meth/
│   │   ├── CTRL_01.CGmeth.txt
│   │   ├── CTRL_02.CGmeth.txt
│   │   └── ...
│   ├── bams/
│   │   ├── CTRL_01_markdup.bam
│   │   ├── CTRL_01_markdup.bam.bai
│   │   └── ...
│   └── output/
│
├── patients/
│   ├── snps/
│   ├── meth/
│   ├── bams/
│   └── output/
│
├── inst/extdata/filter_cpgs_ctrl.xlsx   ← control reference panel
└── inst/extdata/filter_cpgs_pat.xlsx    ← patient reference panel

Usage

Running a single control sample

library(ImprintCapASM)

# Step 1
prepare_cpg_snp_input(
    snp_file     = "controls/snps/CTRL_01.SNPs.out",
    meth_file    = "controls/meth/CTRL_01.CGmeth.txt",
    cpg_ref_file = "inst/extdata/filter_cpgs_ctrl.xlsx",
    sample_type  = "control"
)
# Writes: cpg_snps_CG_control_CTRL_01.xlsx
#         cpg_snps_CG_control_CTRL_01.bed

# Step 2
extract_bam_regions(
    bam_file    = "controls/bams/CTRL_01_markdup.bam",
    bed_file    = "cpg_snps_CG_control_CTRL_01.bed",
    output_dir  = "controls/output/",
    sample_type = "control"
)
# Writes: controls/output/control_CTRL_01_wide.bam + .bai

# Step 3
ASM(
    cpg_snp_file     = "cpg_snps_CG_control_CTRL_01.xlsx",
    sam_file         = "controls/output/control_CTRL_01_wide.bam",
    filter_cpgs_file = "inst/extdata/filter_cpgs_ctrl.xlsx",
    sample_type      = "control"
)
# Writes: asm_control_CTRL_01.xlsx
#         snp_cpg_control_CTRL_01.xlsx
#         meth_summary_control_CTRL_01.xlsx
#         lineplot_control_CTRL_01.pdf

Running a single patient sample

# Step 1
prepare_cpg_snp_input(
    snp_file     = "patients/snps/PAT_01.SNPs.out",
    meth_file    = "patients/meth/PAT_01.CGmeth.txt",
    cpg_ref_file = "inst/extdata/filter_cpgs_pat.xlsx",       
    sample_type  = "patient"
)

# Step 2
extract_bam_regions(
    bam_file    = "patients/bams/PAT_01_markdup.bam",
    bed_file    = "cpg_snps_CG_patient_PAT_01.bed",
    output_dir  = "patients/output/",
    sample_type = "patient"
)

# Step 3
ASM(
    cpg_snp_file     = "cpg_snps_CG_patient_PAT_01.xlsx",
    sam_file         = "patients/output/patient_PAT_01_wide.bam",
    filter_cpgs_file = "inst/extdata/filter_cpgs_pat.xlsx",   # <-- patient reference
    sample_type      = "patient"
)

Running a full cohort with run_pipeline()

run_pipeline() processes all samples in a given folder in batch. Controls and patients are always run as separate calls with their respective reference files:

library(ImprintCapASM)

# --- Run all controls ---
run_pipeline(
    snp_dir          = "controls/snps/",
    meth_dir         = "controls/meth/",
    bam_dir          = "controls/bams/",
    filter_cpgs_file = "inst/extdata/filter_cpgs_ctrl.xlsx",
    output_dir       = "controls/output/",
    sample_type      = "control"
)

# --- Run all patients (separate call, separate reference file) ---
run_pipeline(
    snp_dir          = "patients/snps/",
    meth_dir         = "patients/meth/",
    bam_dir          = "patients/bams/",
    filter_cpgs_file = "inst/extdata/filter_cpgs_pat.xlsx",
    output_dir       = "patients/output/",
    sample_type      = "patient"
)

run_pipeline() automatically matches files across snp_dir, meth_dir, and bam_dir by sample ID, iterates Steps 1 and 2 per sample, then calls ASM() on the combined output for that cohort.

Toy Example with Built-in Data

The package ships with minimal example files covering two chr11 DMRs (H19/IGF2 and KCNQ1OT1):

library(ImprintCapASM)

snp_file     <- system.file("extdata", "example_snp.vcf",         package = "ImprintCapASM")
meth_file    <- system.file("extdata", "example_cgmeth.txt",       package = "ImprintCapASM")
cpg_ref_file <- system.file("extdata", "example_filter_cpgs.xlsx", package = "ImprintCapASM")
bam_file     <- system.file("extdata", "example.bam",              package = "ImprintCapASM")

# Step 1
prepare_cpg_snp_input(
    snp_file     = snp_file,
    meth_file    = meth_file,
    cpg_ref_file = cpg_ref_file,
    sample_type  = "control"
)

# Step 2
extract_bam_regions(
    bam_file    = bam_file,
    bed_file    = list.files(tempdir(), pattern = "\\.bed$", full.names = TRUE)[1],
    output_dir  = tempdir(),
    sample_type = "control"
)

# Step 3
ASM(
    cpg_snp_file     = list.files(tempdir(), pattern = "cpg_snps.*\\.xlsx$", full.names = TRUE)[1],
    sam_file         = list.files(tempdir(), pattern = "_wide\\.bam$",        full.names = TRUE)[1],
    filter_cpgs_file = cpg_ref_file,
    sample_type      = "control"
)

Output Files and Column Descriptions

ASM() writes three Excel files and one PDF per run.

1. asm_{type}_{sample_id}.xlsx — Read-level allele-methylation table

One row per read–CpG combination. The most granular output.

2. snp_cpg_{type}_{sample_id}.xlsx — Per SNP–CpG pair summary

One row per unique (SNP position, CpG position) combination, with allele-stratified read counts and methylation fractions.

3. meth_summary_{type}_{sample_id}.xlsx — Per allele methylation summary

One row per (sample, SNP position, CpG position, allele type) combination.

4. lineplot_{type}_{sample_id}.pdf — DMR methylation line plots

One page per DMR. Each plot shows REF_f and ALT_f (REF and ALT allele methylation fractions) across all CpG positions within the DMR, faceted by SNP. Points are shaped by CpG Category. Expected pattern for a normally imprinted DMR: one allele near 100% methylation, the other near 0%.

prepare_cpg_snp_input() output — cpg_snps_CG_{type}_{id}.xlsx

Supported Imprinted DMRs

The package covers the 41 canonical human imprinted DMRs on GRCh38, including:

System Requirements

• R ≥ 4.1.0
• Bioconductor ≥ 3.14
• samtools accessible on PATH — required at runtime by extract_bam_regions() (calls samtools view, samtools sort, samtools index)
• bgzip / tabix — not required (SNP files are read as plain-text VCF)

Citation

If you use ImprintCapASM in your research, please cite:

Saha S. et al. (2026). ImprintCapASM: SNP-phased allele-specific methylation analysis for imprint disorder diagnostics. R package version 0.1.0. https://CRAN.R-project.org/package=ImprintCapASM

License

MIT © Subham Saha

Contributing

Bug reports and feature requests are welcome via GitHub Issues. Pull requests should be submitted against the dev branch.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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